Aug 16, 2026 · ~2 min

Uncertainty-Aware Bid ML

Client-level bid probability and uncertainty · q1000 forecasts · Monte Carlo decisions

Tabular ML · UQ · Decision Systems

Built withPython · XGBoost · CatBoost · TabICLv2 · Quantile regression · Monte Carlo simulation · SHA256 manifests

Architecture diagram showing common feature coordinates, quantile models, a q1000 distribution interface, regional Monte Carlo simulation, policy evaluation, and evidence-gated deployment — One path from 1,000-quantile forecasts to verified candidate deployment

WHY

A bid recommendation was not useful if a client could not see how likely it was to win or how much uncertainty surrounded it. Regional competition and client conditions changed the meaning of the same point estimate, while a small analyst team could not review more than 1,000 clients consistently. The project therefore needed to produce bid probability, downside risk, and a recommendation together rather than return one number.

Distribution interfaceq1000

Quantiles from 0.001 through 0.999

Internal bid KPI+30%

Compared with the prior single-quantile model

Inference136 s → 25 s

Vectorized simulation and evaluation

HOW

From heterogeneous price histories to a deployable decision

The interface keeps model families interchangeable while the deployment gate keeps an evaluated candidate from becoming a write without evidence.

01Common coordinate
Map heterogeneous price histories and a 63-feature schema into one affine target coordinate.
02Distribution models
Generate q1000 candidates with XGBoost and CatBoost quantile models plus routed TabICLv2 predictors.
03Regional simulation
Estimate candidate validity and lowest-price probability under region-specific Monte Carlo contracts.
04Policy evaluation
Combine time-split evaluation, historical rank signals, and operating constraints.
05Evidence-gated deployment
Verify manifests, calibration semantics, artifact size, SHA256, and dry-run output before an approved write.

RESULT

Operational contract

Layer	Implementation	Decision output
Forecast	XGBoost / CatBoost quantiles, TabICLv2 routing	1,000-quantile distribution
Simulation	Region-specific Monte Carlo	Validity and lowest-price probability
Evaluation	Time split, CRPS, pinball, operational KPI	Candidate policy
Deployment	Manifest + SHA256 + dry run + approval	Verified artifact only

Model map separating active quantile and TabICLv2 candidates from evaluated uncertainty-model families and their distribution, decision, and operational evaluation roles — Active candidates and research implementations are evaluated through distinct quality layers

Contribution

Unified the model zoo behind a q1000 contract and connected about 80,000 candidate bids per notice to region-specific decision simulation.
Built the training, evaluation, routed inference, reporting, artifact provenance, dry-run, and approval-gated deployment path that serves more than 1,000 clients.

Evidence

Private project