ARCNM

Concepts

Adaptive Calibration & environments

Teach ARCNM your shop. Costing environments group your fleet, rates, and historical observations.

Out of the box, ARCNM prices parts against industry-standard benchmarks. With one batch of your own historical observations, Adaptive Calibration learns your shop — your machines, your operators, your overhead, your supplier rates — and stays aligned as your reality shifts.

Every quote you accept, every realised cost you push back, every correction your estimators make is fed into the next calibration cycle. The model gets sharper with use.

This is the defensible difference between a generic quoting engine and a tenant-specific cortex.

Costing environments

A costing environment is a tenant-scoped bundle of:

Component Holds
Fleet machines Hourly rate, capacity, axis count, RPM ceiling, capability
Labour rates EUR / hour, per discipline / shift
Overhead rates Variable + fixed, VDI 3258-compatible schema
Material catalogue EUR / kg, density, machinability, supplier SKU
Default materials Per discipline
Calibration data Your observed prices, fed back as targets

An org can have any number of environments. Typical setup:

  • production — real machines, real rates, attached to every customer quote.
  • staging — sandbox prices used by CI / pre-flight.
  • subcontractor_a — alternate fleet for outsourced work.

Set the env per calculation:

{
  "part_revision_id":      "1f…",
  "costing_environment_id":"env_2a…"
}

Parent environments

Environments support a parent chain — override only what differs from the parent (a night-shift labour rate, for example) and inherit everything else.

production
  └── production-night-shift   (overrides labour rate)
      └── production-night-shift-2025-q4  (overrides electricity cost)

The cascade walks the chain until a tier resolves.

Parameter resolution

Every cost coefficient resolves through a strict precedence order. First match wins. No silent defaults — a parameter that can't resolve raises parameter_unresolved on the request rather than emitting a suspiciously low price.

Resolved values are wrapped with full provenance: every quote is traceable back to which source supplied which number.

{
  "feed_rate_mm_min": {
    "value":      450.0,
    "source":     "environment_calibration",
    "provenance": {
      "env_id":   "env_2a…",
      "samples":  42,
      "interval": [410.0, 490.0]
    }
  }
}

Sources, in precedence order:

  1. Manual override on the environment (operator-set).
  2. Parent override (inherited via the parent chain).
  3. Environment calibration (learned from your observations).
  4. Platform default (industry-standard benchmark).

If you've never calibrated, every parameter resolves at tier 4 — you still get a defensible quote from published-literature benchmarks.

Calibration

You give ARCNM a batch of "target prices" for parts you've already costed externally (your ERP, an external quote, an internal cost-engineer estimate), and the environment adapts so the next quote lands within tolerance. Drive it from the dashboard (Environments → Calibration) or over the API — see Teach an environment from your actuals.

Observations that fall outside a conservative safe band are logged and suppressed, not applied — a value far outside the expected range almost always signals a data-entry error or a misclassification, not a real cost shift. The dashboard surfaces what was applied vs suppressed.

When to recalibrate

  • After hardware changes (new machine, new fixture, retired equipment).
  • Quarterly, as a hygiene cycle.
  • When the drift detector fires (see below).

20-40 observations per discipline is enough to tighten the environment's intervals materially.

Drift detection

ARCNM watches the live stream of quotes against any realised target prices you push back. When the model and reality diverge:

  • The dashboard surfaces a Drift badge on the environment.
  • A re-calibration run is recommended.

Conformal intervals

Every resolved parameter carries a conformal-prediction interval. The interval widens when the evidence is thin and tightens as data accumulates:

{
  "labour_rate_eur_per_hour": {
    "value":   38.50,
    "interval": [37.10, 39.90]
  }
}

The cost itself comes with a quote-level interval (alongside the unit_cost on the calculation result):

{ "unit_cost": 12.84, "currency": "EUR", "interval": [11.95, 13.85] }

Use the interval to:

  • Show "± €0.95" on a quote PDF.
  • Set a quote-buffer for a high-confidence margin.
  • Trigger a re-quote when the interval is too wide for the customer.

Where platform + environment evidence both apply, ARCNM reports a conservative interval rather than the tightest one it could. You'd rather your quote say "± €1.20" than under-promise tightness that doesn't exist.

See also