Calibration

The Calibration API teaches an environment your real costs: submit observations and auto-calibrate its prediction interval, learning curve, and machine policy from your actuals.

Auto-generated from the public OpenAPI spec — this page never drifts from the running API. Base URL https://api.arcnm.io. Authenticate with the X-API-Key header (see Authentication).

Recalibrate an environment's prediction interval from recent samples.

POST /api/v1/parts/calibration/conformal-recalibrate

Recalibrate the prediction interval applied to priced quotes from a fresh set of (predicted, actual) samples. Once updated, subsequent quotes use the new interval. A monthly cadence is the recommended default; submit fresh samples whenever your recent outcomes have shifted.

Request body (application/json)

Field	Type	Required	Description
env_id	string	no	Target environment; required when scope is 'environment'.
samples	object[]	no	Up to 20,000 (predicted, actual) samples; both values must be greater than 0.
scope	platform | environment	yes	Scope to recalibrate; tenant tokens may use 'environment' only.
target_alpha	number	no	Target miss rate for the prediction interval (0.10 → a 90% interval).
target_metric	cycle_time_s | setup_time_s | unit_cost	no	Metric whose prediction interval is recalibrated.

Request

curl -X POST https://api.arcnm.io/api/v1/parts/calibration/conformal-recalibrate \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "scope": "platform"
  }'

import requests

resp = requests.post(
    "https://api.arcnm.io/api/v1/parts/calibration/conformal-recalibrate",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "scope": "platform"
    },
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/conformal-recalibrate", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "scope": "platform"
  }),
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
409	conflict	A conflicting change, or an Idempotency-Key reused with a different body.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
a	number	Fitted first-unit cost coefficient of the curve.
alpha	number	Target miss rate of the prediction interval (e.g. 0.10 → 90%).
b	number	Fitted log-log slope exponent of the learning curve.
env_id	string	Identifier of the calibrated environment.
fit	object	Structured object with the fitted parameters and fit diagnostics.
fitted_accuracy	number	Accuracy (0–1) of the fitted policy on the training rows.
fitted_policy	object	Structured object describing the fitted selection policy.
grid_size	integer	Number of policy candidates evaluated during the fit.
holdout_accuracy	number	Accuracy (0–1) of the fitted policy on held-out rows.
holdout_coverage	number	Fraction (0–1) of held-out actuals falling inside the interval.
holdout_mape	number	Mean absolute percentage error on the held-out rows.
id	string	Identifier of the resulting record, when applicable.
ingested	integer	Number of observations persisted by an ingest call.
learning_rate	number	Learning rate (0–1): cost retained each time output doubles.
matched	integer	Number of supplied rows matched to an existing quote.
message	string	Human-readable note about the run, when present.
n_calibration	integer	Number of samples used to fit the conformal interval.
n_holdout	integer	Number of rows held out to size the prediction interval.
n_iterations	integer	Iterations the midpoint solver ran before converging.
n_lots	integer	Number of production lots used in the curve fit.
n_train	integer	Number of rows used to fit the calibration.
observed_coverage	number	Empirical coverage (0–1) of the fitted interval on its samples.
part_revision_id	string	Identifier of the part revision that was fit.
policy_history_id	string	Identifier of the newly written selection-policy row.
q_alpha	number	Fitted conformal quantile that sets the interval half-width.
r_squared	number	Coefficient of determination (0–1) of the curve fit.
residual_sigma_log	number	Standard deviation of the fit residuals in log space.
run_id	string	Identifier of the calibration run, when one was created.
samples	integer	Number of samples considered by the run.
samples_applied	integer	Number of samples actually applied to the fit.
scope	string	Scope the run applied to: 'platform' or 'environment'.
seed_accuracy	number	Accuracy (0–1) of the seed policy before calibration.
status	string	Outcome status of the calibration run.
target_metric	string	Metric the calibration targeted (e.g. 'unit_cost').
unknown_part_numbers	string[]	Part numbers that could not be resolved to a known part.
unmatched_part_ids	string[]	Part revision IDs with no prior quote, so they were skipped.
work_orders_received	integer	Number of ERP work-order rows received.
work_orders_resolved	integer	Number of work orders resolved to a known part revision.

Example response

{
  "a": 0,
  "alpha": 0,
  "b": 0,
  "env_id": "string",
  "fit": {},
  "fitted_accuracy": 0,
  "fitted_policy": {},
  "grid_size": 0,
  "holdout_accuracy": 0,
  "holdout_coverage": 0.9,
  "holdout_mape": 0.05,
  "id": "string",
  "ingested": 0,
  "learning_rate": 0,
  "matched": 0,
  "message": "string",
  "n_calibration": 0,
  "n_holdout": 0,
  "n_iterations": 0,
  "n_lots": 0,
  "n_train": 0,
  "observed_coverage": 0.9,
  "part_revision_id": "string",
  "policy_history_id": "string",
  "q_alpha": 0,
  "r_squared": 0,
  "residual_sigma_log": 0,
  "run_id": "string",
  "samples": 0,
  "samples_applied": 0,
  "scope": "string",
  "seed_accuracy": 0,
  "status": "string",
  "target_metric": "string",
  "unknown_part_numbers": [
    "string"
  ],
  "unmatched_part_ids": [
    "string"
  ],
  "work_orders_received": 0,
  "work_orders_resolved": 0
}

One-click environment calibration from (part_revision_id, actual_unit_cost) rows.

POST /api/v1/parts/calibration/environments/{env_id}/auto-calibrate

The headline customer-facing endpoint — turn a list of ERP actual unit costs into a calibrated environment in one call. The server re-quotes every part with the environment's current pricing so the result reflects your true cost delta. Parts with no prior successful quote are returned in unmatched_part_ids so you can quote them and retry. Safe for AI-agent use over MCP and idempotent: the same set of actuals yields the same result.

Parameters

Name	In	Type	Required	Description
env_id	path	string	yes	Identifier of the env.

Request body (application/json)

Field	Type	Required	Description
actuals	AutoCalibrateActualRow[]	yes	Up to 20,000 (part_revision_id, actual_unit_cost) rows. Designed for ERP exports: an AI agent or a CSV uploader can stream a tenant's history in one call.
holdout_pct	number	no	Fraction of rows held out to size the prediction interval.
target_alpha	number	no	Target miss rate for the prediction interval (default 0.10 → a 90% interval).

Request

curl -X POST https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/auto-calibrate \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "actuals": [
      {
        "actual_unit_cost": 0,
        "annual_volume": 500,
        "bin": "string",
        "lot_size": 50,
        "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
        "tooling_lifetime_units": 0
      }
    ]
  }'

import requests

resp = requests.post(
    "https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/auto-calibrate",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "actuals": [
            {
                "actual_unit_cost": 0,
                "annual_volume": 500,
                "bin": "string",
                "lot_size": 50,
                "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
                "tooling_lifetime_units": 0
            }
        ]
    },
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/auto-calibrate", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "actuals": [
      {
        "actual_unit_cost": 0,
        "annual_volume": 500,
        "bin": "string",
        "lot_size": 50,
        "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
        "tooling_lifetime_units": 0
      }
    ]
  }),
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
404	not_found	A referenced resource doesn't exist or isn't visible to your organisation.
409	conflict	A conflicting change, or an Idempotency-Key reused with a different body.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
a	number	Fitted first-unit cost coefficient of the curve.
alpha	number	Target miss rate of the prediction interval (e.g. 0.10 → 90%).
b	number	Fitted log-log slope exponent of the learning curve.
env_id	string	Identifier of the calibrated environment.
fit	object	Structured object with the fitted parameters and fit diagnostics.
fitted_accuracy	number	Accuracy (0–1) of the fitted policy on the training rows.
fitted_policy	object	Structured object describing the fitted selection policy.
grid_size	integer	Number of policy candidates evaluated during the fit.
holdout_accuracy	number	Accuracy (0–1) of the fitted policy on held-out rows.
holdout_coverage	number	Fraction (0–1) of held-out actuals falling inside the interval.
holdout_mape	number	Mean absolute percentage error on the held-out rows.
id	string	Identifier of the resulting record, when applicable.
ingested	integer	Number of observations persisted by an ingest call.
learning_rate	number	Learning rate (0–1): cost retained each time output doubles.
matched	integer	Number of supplied rows matched to an existing quote.
message	string	Human-readable note about the run, when present.
n_calibration	integer	Number of samples used to fit the conformal interval.
n_holdout	integer	Number of rows held out to size the prediction interval.
n_iterations	integer	Iterations the midpoint solver ran before converging.
n_lots	integer	Number of production lots used in the curve fit.
n_train	integer	Number of rows used to fit the calibration.
observed_coverage	number	Empirical coverage (0–1) of the fitted interval on its samples.
part_revision_id	string	Identifier of the part revision that was fit.
policy_history_id	string	Identifier of the newly written selection-policy row.
q_alpha	number	Fitted conformal quantile that sets the interval half-width.
r_squared	number	Coefficient of determination (0–1) of the curve fit.
residual_sigma_log	number	Standard deviation of the fit residuals in log space.
run_id	string	Identifier of the calibration run, when one was created.
samples	integer	Number of samples considered by the run.
samples_applied	integer	Number of samples actually applied to the fit.
scope	string	Scope the run applied to: 'platform' or 'environment'.
seed_accuracy	number	Accuracy (0–1) of the seed policy before calibration.
status	string	Outcome status of the calibration run.
target_metric	string	Metric the calibration targeted (e.g. 'unit_cost').
unknown_part_numbers	string[]	Part numbers that could not be resolved to a known part.
unmatched_part_ids	string[]	Part revision IDs with no prior quote, so they were skipped.
work_orders_received	integer	Number of ERP work-order rows received.
work_orders_resolved	integer	Number of work orders resolved to a known part revision.

Example response

{
  "a": 0,
  "alpha": 0,
  "b": 0,
  "env_id": "string",
  "fit": {},
  "fitted_accuracy": 0,
  "fitted_policy": {},
  "grid_size": 0,
  "holdout_accuracy": 0,
  "holdout_coverage": 0.9,
  "holdout_mape": 0.05,
  "id": "string",
  "ingested": 0,
  "learning_rate": 0,
  "matched": 0,
  "message": "string",
  "n_calibration": 0,
  "n_holdout": 0,
  "n_iterations": 0,
  "n_lots": 0,
  "n_train": 0,
  "observed_coverage": 0.9,
  "part_revision_id": "string",
  "policy_history_id": "string",
  "q_alpha": 0,
  "r_squared": 0,
  "residual_sigma_log": 0,
  "run_id": "string",
  "samples": 0,
  "samples_applied": 0,
  "scope": "string",
  "seed_accuracy": 0,
  "status": "string",
  "target_metric": "string",
  "unknown_part_numbers": [
    "string"
  ],
  "unmatched_part_ids": [
    "string"
  ],
  "work_orders_received": 0,
  "work_orders_resolved": 0
}

Calibrate an env from raw ERP work-order rows.

POST /api/v1/parts/calibration/environments/{env_id}/auto-calibrate-from-erp

ERP-native variant of auto-calibrate: accepts the raw columns most ERPs export (part_number, work_order_id, quantity, total_cost) and handles part-number resolution + unit-cost derivation server-side. Idempotent on work_order_id.

Parameters

Name	In	Type	Required	Description
env_id	path	string	yes	Identifier of the env.

Request body (application/json)

Field	Type	Required	Description
holdout_pct	number	no	Fraction of rows held out to size the prediction interval.
target_alpha	number	no	Target miss rate for the prediction interval (0.10 → a 90% interval).
work_orders	ErpWorkOrderPayload[]	yes	Up to 20 000 raw ERP work-order lines. Designed for the AI-agent ERP-import use case: pull tenant's last 12 months of WIP_DISCRETE_JOBS rows, POST them once, and the service resolves part numbers, decomposes setup vs variable cost from the lot-size diversity, and returns a calibrated env.

Request

curl -X POST https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/auto-calibrate-from-erp \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "work_orders": [
      {
        "annual_forecast": 0,
        "part_number": "BRACKET-001",
        "quantity": 0,
        "total_cost": 642,
        "work_order_id": "string"
      }
    ]
  }'

import requests

resp = requests.post(
    "https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/auto-calibrate-from-erp",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "work_orders": [
            {
                "annual_forecast": 0,
                "part_number": "BRACKET-001",
                "quantity": 0,
                "total_cost": 642,
                "work_order_id": "string"
            }
        ]
    },
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/auto-calibrate-from-erp", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "work_orders": [
      {
        "annual_forecast": 0,
        "part_number": "BRACKET-001",
        "quantity": 0,
        "total_cost": 642,
        "work_order_id": "string"
      }
    ]
  }),
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
404	not_found	A referenced resource doesn't exist or isn't visible to your organisation.
409	conflict	A conflicting change, or an Idempotency-Key reused with a different body.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
a	number	Fitted first-unit cost coefficient of the curve.
alpha	number	Target miss rate of the prediction interval (e.g. 0.10 → 90%).
b	number	Fitted log-log slope exponent of the learning curve.
env_id	string	Identifier of the calibrated environment.
fit	object	Structured object with the fitted parameters and fit diagnostics.
fitted_accuracy	number	Accuracy (0–1) of the fitted policy on the training rows.
fitted_policy	object	Structured object describing the fitted selection policy.
grid_size	integer	Number of policy candidates evaluated during the fit.
holdout_accuracy	number	Accuracy (0–1) of the fitted policy on held-out rows.
holdout_coverage	number	Fraction (0–1) of held-out actuals falling inside the interval.
holdout_mape	number	Mean absolute percentage error on the held-out rows.
id	string	Identifier of the resulting record, when applicable.
ingested	integer	Number of observations persisted by an ingest call.
learning_rate	number	Learning rate (0–1): cost retained each time output doubles.
matched	integer	Number of supplied rows matched to an existing quote.
message	string	Human-readable note about the run, when present.
n_calibration	integer	Number of samples used to fit the conformal interval.
n_holdout	integer	Number of rows held out to size the prediction interval.
n_iterations	integer	Iterations the midpoint solver ran before converging.
n_lots	integer	Number of production lots used in the curve fit.
n_train	integer	Number of rows used to fit the calibration.
observed_coverage	number	Empirical coverage (0–1) of the fitted interval on its samples.
part_revision_id	string	Identifier of the part revision that was fit.
policy_history_id	string	Identifier of the newly written selection-policy row.
q_alpha	number	Fitted conformal quantile that sets the interval half-width.
r_squared	number	Coefficient of determination (0–1) of the curve fit.
residual_sigma_log	number	Standard deviation of the fit residuals in log space.
run_id	string	Identifier of the calibration run, when one was created.
samples	integer	Number of samples considered by the run.
samples_applied	integer	Number of samples actually applied to the fit.
scope	string	Scope the run applied to: 'platform' or 'environment'.
seed_accuracy	number	Accuracy (0–1) of the seed policy before calibration.
status	string	Outcome status of the calibration run.
target_metric	string	Metric the calibration targeted (e.g. 'unit_cost').
unknown_part_numbers	string[]	Part numbers that could not be resolved to a known part.
unmatched_part_ids	string[]	Part revision IDs with no prior quote, so they were skipped.
work_orders_received	integer	Number of ERP work-order rows received.
work_orders_resolved	integer	Number of work orders resolved to a known part revision.

Example response

{
  "a": 0,
  "alpha": 0,
  "b": 0,
  "env_id": "string",
  "fit": {},
  "fitted_accuracy": 0,
  "fitted_policy": {},
  "grid_size": 0,
  "holdout_accuracy": 0,
  "holdout_coverage": 0.9,
  "holdout_mape": 0.05,
  "id": "string",
  "ingested": 0,
  "learning_rate": 0,
  "matched": 0,
  "message": "string",
  "n_calibration": 0,
  "n_holdout": 0,
  "n_iterations": 0,
  "n_lots": 0,
  "n_train": 0,
  "observed_coverage": 0.9,
  "part_revision_id": "string",
  "policy_history_id": "string",
  "q_alpha": 0,
  "r_squared": 0,
  "residual_sigma_log": 0,
  "run_id": "string",
  "samples": 0,
  "samples_applied": 0,
  "scope": "string",
  "seed_accuracy": 0,
  "status": "string",
  "target_metric": "string",
  "unknown_part_numbers": [
    "string"
  ],
  "unmatched_part_ids": [
    "string"
  ],
  "work_orders_received": 0,
  "work_orders_resolved": 0
}

Recalibrate an environment from its accumulated observations.

POST /api/v1/parts/calibration/environments/{env_id}/finalize

Recalibrate this environment for the selected target metric using every observation submitted for it so far, then activate the result. Use after submitting observations. To start from raw ERP actuals without submitting observations first, use the one-click auto-calibrate endpoint instead.

Parameters

Name	In	Type	Required	Description
env_id	path	string	yes	Identifier of the env.

Request body (application/json)

Field	Type	Required	Description
notes	string	no	Optional free-text note recorded with the run.
target_alpha	number	no	Target miss rate for the prediction interval (0.10 → a 90% interval).
target_metric	cycle_time_s | setup_time_s | unit_cost | machine_choice	no	Metric to calibrate the environment for.

Request

curl -X POST https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/finalize \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "notes": "string",
    "target_alpha": 0.1,
    "target_metric": "cycle_time_s"
  }'

import requests

resp = requests.post(
    "https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/finalize",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "notes": "string",
        "target_alpha": 0.1,
        "target_metric": "cycle_time_s"
    },
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/finalize", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "notes": "string",
    "target_alpha": 0.1,
    "target_metric": "cycle_time_s"
  }),
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
404	not_found	A referenced resource doesn't exist or isn't visible to your organisation.
409	conflict	A conflicting change, or an Idempotency-Key reused with a different body.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
a	number	Fitted first-unit cost coefficient of the curve.
alpha	number	Target miss rate of the prediction interval (e.g. 0.10 → 90%).
b	number	Fitted log-log slope exponent of the learning curve.
env_id	string	Identifier of the calibrated environment.
fit	object	Structured object with the fitted parameters and fit diagnostics.
fitted_accuracy	number	Accuracy (0–1) of the fitted policy on the training rows.
fitted_policy	object	Structured object describing the fitted selection policy.
grid_size	integer	Number of policy candidates evaluated during the fit.
holdout_accuracy	number	Accuracy (0–1) of the fitted policy on held-out rows.
holdout_coverage	number	Fraction (0–1) of held-out actuals falling inside the interval.
holdout_mape	number	Mean absolute percentage error on the held-out rows.
id	string	Identifier of the resulting record, when applicable.
ingested	integer	Number of observations persisted by an ingest call.
learning_rate	number	Learning rate (0–1): cost retained each time output doubles.
matched	integer	Number of supplied rows matched to an existing quote.
message	string	Human-readable note about the run, when present.
n_calibration	integer	Number of samples used to fit the conformal interval.
n_holdout	integer	Number of rows held out to size the prediction interval.
n_iterations	integer	Iterations the midpoint solver ran before converging.
n_lots	integer	Number of production lots used in the curve fit.
n_train	integer	Number of rows used to fit the calibration.
observed_coverage	number	Empirical coverage (0–1) of the fitted interval on its samples.
part_revision_id	string	Identifier of the part revision that was fit.
policy_history_id	string	Identifier of the newly written selection-policy row.
q_alpha	number	Fitted conformal quantile that sets the interval half-width.
r_squared	number	Coefficient of determination (0–1) of the curve fit.
residual_sigma_log	number	Standard deviation of the fit residuals in log space.
run_id	string	Identifier of the calibration run, when one was created.
samples	integer	Number of samples considered by the run.
samples_applied	integer	Number of samples actually applied to the fit.
scope	string	Scope the run applied to: 'platform' or 'environment'.
seed_accuracy	number	Accuracy (0–1) of the seed policy before calibration.
status	string	Outcome status of the calibration run.
target_metric	string	Metric the calibration targeted (e.g. 'unit_cost').
unknown_part_numbers	string[]	Part numbers that could not be resolved to a known part.
unmatched_part_ids	string[]	Part revision IDs with no prior quote, so they were skipped.
work_orders_received	integer	Number of ERP work-order rows received.
work_orders_resolved	integer	Number of work orders resolved to a known part revision.

Example response

{
  "a": 0,
  "alpha": 0,
  "b": 0,
  "env_id": "string",
  "fit": {},
  "fitted_accuracy": 0,
  "fitted_policy": {},
  "grid_size": 0,
  "holdout_accuracy": 0,
  "holdout_coverage": 0.9,
  "holdout_mape": 0.05,
  "id": "string",
  "ingested": 0,
  "learning_rate": 0,
  "matched": 0,
  "message": "string",
  "n_calibration": 0,
  "n_holdout": 0,
  "n_iterations": 0,
  "n_lots": 0,
  "n_train": 0,
  "observed_coverage": 0.9,
  "part_revision_id": "string",
  "policy_history_id": "string",
  "q_alpha": 0,
  "r_squared": 0,
  "residual_sigma_log": 0,
  "run_id": "string",
  "samples": 0,
  "samples_applied": 0,
  "scope": "string",
  "seed_accuracy": 0,
  "status": "string",
  "target_metric": "string",
  "unknown_part_numbers": [
    "string"
  ],
  "unmatched_part_ids": [
    "string"
  ],
  "work_orders_received": 0,
  "work_orders_resolved": 0
}

Calibrate cost-down-with-volume from lot-progression actuals.

POST /api/v1/parts/calibration/environments/{env_id}/learning-curve-fit

Calibrate how this environment's per-unit cost falls as cumulative production grows, using a sequence of production-lot actuals for one part. Choose the cumulative model (the safer default, observed directly) or the per-unit model. Submit at least three lots.

Parameters

Name	In	Type	Required	Description
env_id	path	string	yes	Identifier of the env.

Request body (application/json)

Field	Type	Required	Description
lots	LearningCurveLotPayload[]	yes	Sequence of production-lot actuals to fit; at least three.
model	crawford_unit | wright_cumulative	no	Learning-curve model to fit (per-unit or cumulative).
part_revision_id	string	yes	Identifier of the part revision the lots belong to.

Request

curl -X POST https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/learning-curve-fit \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
    "lots": [
      {
        "lot_index": 0,
        "lot_size": 50,
        "lot_total_cost": 0
      }
    ]
  }'

import requests

resp = requests.post(
    "https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/learning-curve-fit",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
        "lots": [
            {
                "lot_index": 0,
                "lot_size": 50,
                "lot_total_cost": 0
            }
        ]
    },
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/learning-curve-fit", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
    "lots": [
      {
        "lot_index": 0,
        "lot_size": 50,
        "lot_total_cost": 0
      }
    ]
  }),
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
404	not_found	A referenced resource doesn't exist or isn't visible to your organisation.
409	conflict	A conflicting change, or an Idempotency-Key reused with a different body.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
a	number	Fitted first-unit cost coefficient of the curve.
alpha	number	Target miss rate of the prediction interval (e.g. 0.10 → 90%).
b	number	Fitted log-log slope exponent of the learning curve.
env_id	string	Identifier of the calibrated environment.
fit	object	Structured object with the fitted parameters and fit diagnostics.
fitted_accuracy	number	Accuracy (0–1) of the fitted policy on the training rows.
fitted_policy	object	Structured object describing the fitted selection policy.
grid_size	integer	Number of policy candidates evaluated during the fit.
holdout_accuracy	number	Accuracy (0–1) of the fitted policy on held-out rows.
holdout_coverage	number	Fraction (0–1) of held-out actuals falling inside the interval.
holdout_mape	number	Mean absolute percentage error on the held-out rows.
id	string	Identifier of the resulting record, when applicable.
ingested	integer	Number of observations persisted by an ingest call.
learning_rate	number	Learning rate (0–1): cost retained each time output doubles.
matched	integer	Number of supplied rows matched to an existing quote.
message	string	Human-readable note about the run, when present.
n_calibration	integer	Number of samples used to fit the conformal interval.
n_holdout	integer	Number of rows held out to size the prediction interval.
n_iterations	integer	Iterations the midpoint solver ran before converging.
n_lots	integer	Number of production lots used in the curve fit.
n_train	integer	Number of rows used to fit the calibration.
observed_coverage	number	Empirical coverage (0–1) of the fitted interval on its samples.
part_revision_id	string	Identifier of the part revision that was fit.
policy_history_id	string	Identifier of the newly written selection-policy row.
q_alpha	number	Fitted conformal quantile that sets the interval half-width.
r_squared	number	Coefficient of determination (0–1) of the curve fit.
residual_sigma_log	number	Standard deviation of the fit residuals in log space.
run_id	string	Identifier of the calibration run, when one was created.
samples	integer	Number of samples considered by the run.
samples_applied	integer	Number of samples actually applied to the fit.
scope	string	Scope the run applied to: 'platform' or 'environment'.
seed_accuracy	number	Accuracy (0–1) of the seed policy before calibration.
status	string	Outcome status of the calibration run.
target_metric	string	Metric the calibration targeted (e.g. 'unit_cost').
unknown_part_numbers	string[]	Part numbers that could not be resolved to a known part.
unmatched_part_ids	string[]	Part revision IDs with no prior quote, so they were skipped.
work_orders_received	integer	Number of ERP work-order rows received.
work_orders_resolved	integer	Number of work orders resolved to a known part revision.

Example response

{
  "a": 0,
  "alpha": 0,
  "b": 0,
  "env_id": "string",
  "fit": {},
  "fitted_accuracy": 0,
  "fitted_policy": {},
  "grid_size": 0,
  "holdout_accuracy": 0,
  "holdout_coverage": 0.9,
  "holdout_mape": 0.05,
  "id": "string",
  "ingested": 0,
  "learning_rate": 0,
  "matched": 0,
  "message": "string",
  "n_calibration": 0,
  "n_holdout": 0,
  "n_iterations": 0,
  "n_lots": 0,
  "n_train": 0,
  "observed_coverage": 0.9,
  "part_revision_id": "string",
  "policy_history_id": "string",
  "q_alpha": 0,
  "r_squared": 0,
  "residual_sigma_log": 0,
  "run_id": "string",
  "samples": 0,
  "samples_applied": 0,
  "scope": "string",
  "seed_accuracy": 0,
  "status": "string",
  "target_metric": "string",
  "unknown_part_numbers": [
    "string"
  ],
  "unmatched_part_ids": [
    "string"
  ],
  "work_orders_received": 0,
  "work_orders_resolved": 0
}

Calibrate which machine an environment recommends from past choices.

POST /api/v1/parts/calibration/environments/{env_id}/policy/calibrate

Calibrate how this environment chooses a machine, using a list of historical 'the shop actually picked machine X' decisions. Once calibrated, the environment's machine recommendations better match your shop's real choices.

Parameters

Name	In	Type	Required	Description
env_id	path	string	yes	Identifier of the env.

Request body (application/json)

Field	Type	Required	Description
history	object[]	yes	Past machine-choice records, each with candidates and the chosen_machine_id.
region	string	no	Region whose seed policy to start from (e.g. DE-BY); null for the default.

Request

curl -X POST https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/policy/calibrate \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "history": [
      {}
    ]
  }'

import requests

resp = requests.post(
    "https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/policy/calibrate",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "history": [
            {}
        ]
    },
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/policy/calibrate", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "history": [
      {}
    ]
  }),
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
404	not_found	A referenced resource doesn't exist or isn't visible to your organisation.
409	conflict	A conflicting change, or an Idempotency-Key reused with a different body.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
a	number	Fitted first-unit cost coefficient of the curve.
alpha	number	Target miss rate of the prediction interval (e.g. 0.10 → 90%).
b	number	Fitted log-log slope exponent of the learning curve.
env_id	string	Identifier of the calibrated environment.
fit	object	Structured object with the fitted parameters and fit diagnostics.
fitted_accuracy	number	Accuracy (0–1) of the fitted policy on the training rows.
fitted_policy	object	Structured object describing the fitted selection policy.
grid_size	integer	Number of policy candidates evaluated during the fit.
holdout_accuracy	number	Accuracy (0–1) of the fitted policy on held-out rows.
holdout_coverage	number	Fraction (0–1) of held-out actuals falling inside the interval.
holdout_mape	number	Mean absolute percentage error on the held-out rows.
id	string	Identifier of the resulting record, when applicable.
ingested	integer	Number of observations persisted by an ingest call.
learning_rate	number	Learning rate (0–1): cost retained each time output doubles.
matched	integer	Number of supplied rows matched to an existing quote.
message	string	Human-readable note about the run, when present.
n_calibration	integer	Number of samples used to fit the conformal interval.
n_holdout	integer	Number of rows held out to size the prediction interval.
n_iterations	integer	Iterations the midpoint solver ran before converging.
n_lots	integer	Number of production lots used in the curve fit.
n_train	integer	Number of rows used to fit the calibration.
observed_coverage	number	Empirical coverage (0–1) of the fitted interval on its samples.
part_revision_id	string	Identifier of the part revision that was fit.
policy_history_id	string	Identifier of the newly written selection-policy row.
q_alpha	number	Fitted conformal quantile that sets the interval half-width.
r_squared	number	Coefficient of determination (0–1) of the curve fit.
residual_sigma_log	number	Standard deviation of the fit residuals in log space.
run_id	string	Identifier of the calibration run, when one was created.
samples	integer	Number of samples considered by the run.
samples_applied	integer	Number of samples actually applied to the fit.
scope	string	Scope the run applied to: 'platform' or 'environment'.
seed_accuracy	number	Accuracy (0–1) of the seed policy before calibration.
status	string	Outcome status of the calibration run.
target_metric	string	Metric the calibration targeted (e.g. 'unit_cost').
unknown_part_numbers	string[]	Part numbers that could not be resolved to a known part.
unmatched_part_ids	string[]	Part revision IDs with no prior quote, so they were skipped.
work_orders_received	integer	Number of ERP work-order rows received.
work_orders_resolved	integer	Number of work orders resolved to a known part revision.

Example response

{
  "a": 0,
  "alpha": 0,
  "b": 0,
  "env_id": "string",
  "fit": {},
  "fitted_accuracy": 0,
  "fitted_policy": {},
  "grid_size": 0,
  "holdout_accuracy": 0,
  "holdout_coverage": 0.9,
  "holdout_mape": 0.05,
  "id": "string",
  "ingested": 0,
  "learning_rate": 0,
  "matched": 0,
  "message": "string",
  "n_calibration": 0,
  "n_holdout": 0,
  "n_iterations": 0,
  "n_lots": 0,
  "n_train": 0,
  "observed_coverage": 0.9,
  "part_revision_id": "string",
  "policy_history_id": "string",
  "q_alpha": 0,
  "r_squared": 0,
  "residual_sigma_log": 0,
  "run_id": "string",
  "samples": 0,
  "samples_applied": 0,
  "scope": "string",
  "seed_accuracy": 0,
  "status": "string",
  "target_metric": "string",
  "unknown_part_numbers": [
    "string"
  ],
  "unmatched_part_ids": [
    "string"
  ],
  "work_orders_received": 0,
  "work_orders_resolved": 0
}

Calibration health summary for one environment.

GET /api/v1/parts/calibration/environments/{env_id}/status

Read view of an environment's calibration health: whether it is calibrated, when it was last calibrated, and how many submitted observations are waiting to be applied. Designed as the AI agent's discovery endpoint — call this first to decide whether to submit fresh observations or recalibrate.

Parameters

Name	In	Type	Required	Description
env_id	path	string	yes	Identifier of the env.

Request

curl -X GET https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/status \
  -H "X-API-Key: $ARCNM_API_KEY"

import requests

resp = requests.get(
    "https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/status",
    headers={"X-API-Key": "YOUR_API_KEY"},
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/status", {
  method: "GET",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
  },
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
404	not_found	A referenced resource doesn't exist or isn't visible to your organisation.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
active_posterior_count	integer	Number of active calibrated parameters; 0 means uncalibrated.
conformal	object[]	Recent prediction-interval calibrations, each a structured object.
drift_alerts	object[]	Recent drift alerts, each a structured object with signal and severity.
env_id	string	Identifier of the environment this status describes.
last_fit_at	string	ISO 8601 timestamp of the last calibration; null if never run.
observations	object[]	Per-source counts of submitted observations awaiting application.

Example response

{
  "active_posterior_count": 0,
  "conformal": [
    {}
  ],
  "drift_alerts": [
    {}
  ],
  "env_id": "string",
  "last_fit_at": "string",
  "observations": [
    {}
  ]
}

Calibrate an environment from (predicted, actual) pairs.

POST /api/v1/parts/calibration/environments/{env_id}/teach

Calibrate an environment from caller-supplied (predicted, actual) rows. Each predicted value must come from a current quote for the part. If you only have actuals, use the one-click auto-calibrate endpoint, which re-quotes each part server-side.

Parameters

Name	In	Type	Required	Description
env_id	path	string	yes	Identifier of the env.

Request body (application/json)

Field	Type	Required	Description
holdout_pct	number	no	Fraction (0.05–0.5) of rows held out to size the prediction interval.
rows	TeachRow[]	yes	Between 1 and 20,000 (predicted, actual) rows to calibrate from.
target_alpha	number	no	Target miss rate for the prediction interval (0.10 → a 90% interval).

Request

curl -X POST https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/teach \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "rows": [
      {
        "actual": 0,
        "bin": "string",
        "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
        "predicted": 0,
        "target_metric": "unit_cost"
      }
    ]
  }'

import requests

resp = requests.post(
    "https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/teach",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "rows": [
            {
                "actual": 0,
                "bin": "string",
                "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
                "predicted": 0,
                "target_metric": "unit_cost"
            }
        ]
    },
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/environments/{env_id}/teach", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "rows": [
      {
        "actual": 0,
        "bin": "string",
        "part_revision_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
        "predicted": 0,
        "target_metric": "unit_cost"
      }
    ]
  }),
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
404	not_found	A referenced resource doesn't exist or isn't visible to your organisation.
409	conflict	A conflicting change, or an Idempotency-Key reused with a different body.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
a	number	Fitted first-unit cost coefficient of the curve.
alpha	number	Target miss rate of the prediction interval (e.g. 0.10 → 90%).
b	number	Fitted log-log slope exponent of the learning curve.
env_id	string	Identifier of the calibrated environment.
fit	object	Structured object with the fitted parameters and fit diagnostics.
fitted_accuracy	number	Accuracy (0–1) of the fitted policy on the training rows.
fitted_policy	object	Structured object describing the fitted selection policy.
grid_size	integer	Number of policy candidates evaluated during the fit.
holdout_accuracy	number	Accuracy (0–1) of the fitted policy on held-out rows.
holdout_coverage	number	Fraction (0–1) of held-out actuals falling inside the interval.
holdout_mape	number	Mean absolute percentage error on the held-out rows.
id	string	Identifier of the resulting record, when applicable.
ingested	integer	Number of observations persisted by an ingest call.
learning_rate	number	Learning rate (0–1): cost retained each time output doubles.
matched	integer	Number of supplied rows matched to an existing quote.
message	string	Human-readable note about the run, when present.
n_calibration	integer	Number of samples used to fit the conformal interval.
n_holdout	integer	Number of rows held out to size the prediction interval.
n_iterations	integer	Iterations the midpoint solver ran before converging.
n_lots	integer	Number of production lots used in the curve fit.
n_train	integer	Number of rows used to fit the calibration.
observed_coverage	number	Empirical coverage (0–1) of the fitted interval on its samples.
part_revision_id	string	Identifier of the part revision that was fit.
policy_history_id	string	Identifier of the newly written selection-policy row.
q_alpha	number	Fitted conformal quantile that sets the interval half-width.
r_squared	number	Coefficient of determination (0–1) of the curve fit.
residual_sigma_log	number	Standard deviation of the fit residuals in log space.
run_id	string	Identifier of the calibration run, when one was created.
samples	integer	Number of samples considered by the run.
samples_applied	integer	Number of samples actually applied to the fit.
scope	string	Scope the run applied to: 'platform' or 'environment'.
seed_accuracy	number	Accuracy (0–1) of the seed policy before calibration.
status	string	Outcome status of the calibration run.
target_metric	string	Metric the calibration targeted (e.g. 'unit_cost').
unknown_part_numbers	string[]	Part numbers that could not be resolved to a known part.
unmatched_part_ids	string[]	Part revision IDs with no prior quote, so they were skipped.
work_orders_received	integer	Number of ERP work-order rows received.
work_orders_resolved	integer	Number of work orders resolved to a known part revision.

Example response

{
  "a": 0,
  "alpha": 0,
  "b": 0,
  "env_id": "string",
  "fit": {},
  "fitted_accuracy": 0,
  "fitted_policy": {},
  "grid_size": 0,
  "holdout_accuracy": 0,
  "holdout_coverage": 0.9,
  "holdout_mape": 0.05,
  "id": "string",
  "ingested": 0,
  "learning_rate": 0,
  "matched": 0,
  "message": "string",
  "n_calibration": 0,
  "n_holdout": 0,
  "n_iterations": 0,
  "n_lots": 0,
  "n_train": 0,
  "observed_coverage": 0.9,
  "part_revision_id": "string",
  "policy_history_id": "string",
  "q_alpha": 0,
  "r_squared": 0,
  "residual_sigma_log": 0,
  "run_id": "string",
  "samples": 0,
  "samples_applied": 0,
  "scope": "string",
  "seed_accuracy": 0,
  "status": "string",
  "target_metric": "string",
  "unknown_part_numbers": [
    "string"
  ],
  "unmatched_part_ids": [
    "string"
  ],
  "work_orders_received": 0,
  "work_orders_resolved": 0
}

Submit calibration observations for an environment.

POST /api/v1/parts/calibration/outcomes

Submit a batch of observations (predicted vs. actual outcomes) for an environment so it can be calibrated. Tenant-bound; scope='environment' only. Safe for AI agent use: idempotent on each observation's natural key and rate-limited by the global tenant policy. Recalibrate the environment afterwards to apply the accumulated observations.

Request body (application/json)

Field	Type	Required	Description
env_id	string	no	Target environment; required when scope is 'environment'.
observations	object[]	no	Up to 20,000 observations; each carries target_metric, observation_type, predicted, and actual.
oracle	string	yes	Source of the observations (e.g. operator report, MES, supplier quote).
scope	platform | environment	yes	Scope of the observations; tenant tokens may submit 'environment' only.

Request

curl -X POST https://api.arcnm.io/api/v1/parts/calibration/outcomes \
  -H "X-API-Key: $ARCNM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "scope": "platform",
    "oracle": "string"
  }'

import requests

resp = requests.post(
    "https://api.arcnm.io/api/v1/parts/calibration/outcomes",
    headers={"X-API-Key": "YOUR_API_KEY"},
    json={
        "scope": "platform",
        "oracle": "string"
    },
)
resp.raise_for_status()
print(resp.json())

const resp = await fetch("https://api.arcnm.io/api/v1/parts/calibration/outcomes", {
  method: "POST",
  headers: {
    "X-API-Key": process.env.ARCNM_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "scope": "platform",
    "oracle": "string"
  }),
})
const data = await resp.json()

Responses

Status	Description
200	Successful Response
422	Validation Error

Errors

Standard error responses — see the Errors catalog for the full envelope, request_id, and retry-safety table.

Status	Code	When
401	invalid_api_key	Missing, malformed, or revoked API key.
403	insufficient_scope	The key is valid but lacks a scope this endpoint requires.
409	conflict	A conflicting change, or an Idempotency-Key reused with a different body.
429	rate_limited	Per-key or per-org rate limit exceeded — back off with jitter and retry.

Response body 200

Field	Type	Description
a	number	Fitted first-unit cost coefficient of the curve.
alpha	number	Target miss rate of the prediction interval (e.g. 0.10 → 90%).
b	number	Fitted log-log slope exponent of the learning curve.
env_id	string	Identifier of the calibrated environment.
fit	object	Structured object with the fitted parameters and fit diagnostics.
fitted_accuracy	number	Accuracy (0–1) of the fitted policy on the training rows.
fitted_policy	object	Structured object describing the fitted selection policy.
grid_size	integer	Number of policy candidates evaluated during the fit.
holdout_accuracy	number	Accuracy (0–1) of the fitted policy on held-out rows.
holdout_coverage	number	Fraction (0–1) of held-out actuals falling inside the interval.
holdout_mape	number	Mean absolute percentage error on the held-out rows.
id	string	Identifier of the resulting record, when applicable.
ingested	integer	Number of observations persisted by an ingest call.
learning_rate	number	Learning rate (0–1): cost retained each time output doubles.
matched	integer	Number of supplied rows matched to an existing quote.
message	string	Human-readable note about the run, when present.
n_calibration	integer	Number of samples used to fit the conformal interval.
n_holdout	integer	Number of rows held out to size the prediction interval.
n_iterations	integer	Iterations the midpoint solver ran before converging.
n_lots	integer	Number of production lots used in the curve fit.
n_train	integer	Number of rows used to fit the calibration.
observed_coverage	number	Empirical coverage (0–1) of the fitted interval on its samples.
part_revision_id	string	Identifier of the part revision that was fit.
policy_history_id	string	Identifier of the newly written selection-policy row.
q_alpha	number	Fitted conformal quantile that sets the interval half-width.
r_squared	number	Coefficient of determination (0–1) of the curve fit.
residual_sigma_log	number	Standard deviation of the fit residuals in log space.
run_id	string	Identifier of the calibration run, when one was created.
samples	integer	Number of samples considered by the run.
samples_applied	integer	Number of samples actually applied to the fit.
scope	string	Scope the run applied to: 'platform' or 'environment'.
seed_accuracy	number	Accuracy (0–1) of the seed policy before calibration.
status	string	Outcome status of the calibration run.
target_metric	string	Metric the calibration targeted (e.g. 'unit_cost').
unknown_part_numbers	string[]	Part numbers that could not be resolved to a known part.
unmatched_part_ids	string[]	Part revision IDs with no prior quote, so they were skipped.
work_orders_received	integer	Number of ERP work-order rows received.
work_orders_resolved	integer	Number of work orders resolved to a known part revision.

Example response

{
  "a": 0,
  "alpha": 0,
  "b": 0,
  "env_id": "string",
  "fit": {},
  "fitted_accuracy": 0,
  "fitted_policy": {},
  "grid_size": 0,
  "holdout_accuracy": 0,
  "holdout_coverage": 0.9,
  "holdout_mape": 0.05,
  "id": "string",
  "ingested": 0,
  "learning_rate": 0,
  "matched": 0,
  "message": "string",
  "n_calibration": 0,
  "n_holdout": 0,
  "n_iterations": 0,
  "n_lots": 0,
  "n_train": 0,
  "observed_coverage": 0.9,
  "part_revision_id": "string",
  "policy_history_id": "string",
  "q_alpha": 0,
  "r_squared": 0,
  "residual_sigma_log": 0,
  "run_id": "string",
  "samples": 0,
  "samples_applied": 0,
  "scope": "string",
  "seed_accuracy": 0,
  "status": "string",
  "target_metric": "string",
  "unknown_part_numbers": [
    "string"
  ],
  "unmatched_part_ids": [
    "string"
  ],
  "work_orders_received": 0,
  "work_orders_resolved": 0
}