Forecast
Scenario -> analyze_delta -> risk_drivers -> escalated_curve -> classify -> render_pr_comment.
Code islands
The marketing pages are grounded in source, not imagination.
This page maps the public claims back to the internal system boundaries.
cost_enginePure math layer: Monte Carlo, classification, calibration, capability, structured pricing, cloud, capex, footprint, evidence.
takeoffPR product layer: Scanners, scenario translation, estimate_pr, policy discovery, JSON output, GitHub Action glue.
blue_bookLedger layer: Provider ingest, OTel, FOCUS, price medallions, actuals, specs, capability, allocation, footprint actuals.
autobuildSelf-maintenance layer: Health, planner, scout, governor, driver, metering, locks, drift, resilience, jobs.
snapshotsFrozen basis: Pricing, price index, capability, spec sheet, actuals, cloud index, footprint basis.
End-to-end trace
A public claim should point to a source boundary.
When the site says Class1 reads a pull request, that maps to takeoff: scanners, diff parsing, scenario translation, estimate_pr, and policy discovery. When the site says Class1 models P90 monthly delta, that maps to cost_engine: Scenario, analyze_delta, risk_drivers, escalation, classification, recommendation, and report rendering.
When the site says the estimate is auditable, that maps to Blue Book and snapshots: pricing, structured rates, price histories, model specs, capability grades, actuals, cloud indexes, and footprint bases. When the site says the system can learn, that maps to calibration: stored estimates, FOCUS actuals, ActuarialTable, and recommended recalibration.
This page exists to keep marketing honest. A buyer can ask for any claim and the answer should be a module, a data file, a test, or a named open item. That posture is stronger than pretending every future feature is already finished.
Enforcement
.class1 config -> evaluate_policy -> gate_payload -> CI exit code.
Calibration
Persist estimate at merge -> ingest actuals -> ActuarialTable -> class improves only with pairs.
Data moat
Bronze source artifacts -> silver normalizers -> gold snapshots -> reproducible estimates.
Claim-to-code map
The sales story is defensible because each promise has a local implementation path.
Before-merge approvaltakeoff/estimate_pr.py compares base and head, builds the report, evaluates policy, writes comments and JSON, and exits non-zero when the budget gate fails.
Risk-adjusted forecastcost_engine/monte_carlo.py runs paired scenarios on shared draws so deltas are sharp and self-deltas are exactly zero.
Model-fit economicscost_engine/capability.py and recommend.py connect benchmark fitness to retries, rework, and cost per completed task.
Data moatblue_book/prices, blue_book/spec, blue_book/capability, actuals, and snapshots implement the reproducible basis behind the numbers.
Honest gapsGOAL.md and NEEDS_HUMAN.md keep variance waterfall actuals, license gate, paste-a-diff web demo, and general intelligence grades visible.