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Decision Dataset Foundry

We structure human judgment, actions, and failures from live operations into datasets for AI improvement.

Decision Dataset Foundry intentionally creates, captures, and normalizes tacit knowledge, reasoning, failure cases, and field context during live operations, then turns them into proprietary, model-ready data assets.

Across procurement, marketing, CS, real estate, commerce, health, and more, we capture and normalize real decisions (Go/No-Go), scoring, failure reasons, and execution outcomes into model-trainable decision datasets.

Unlock performance gains with non-public judgment/failure data

Build hard-to-copy proprietary datasets

Improve alerts, hold decisions, and recommendations from failure patterns

Expand decision automation gradually and safely

Extract cross-domain strategic insights

Create long-term lock-in as data and models compound

Define domain and judgment points (recommend/select/execute/stop/fail)

Design schema, labels, collection UI, and logging policy

Build collection pipeline across APIs/logs/dashboard/tagging/database

Run labeling operations with rule-based first pass and human QA

Package/evaluate dataset with sampling, bias checks, and quality report

Connect to training and continuously improve operations

Decision Event Schema v1

Labeling Guideline (failure reasons, risk tags, rationale templates)

Dataset Package (train/valid/test + data dictionary)

Data Quality Report (missing/duplicates/bias/consistency)

Model Improvement Plan (data-to-model KPI loop)

Governance & Privacy Note (consent/de-identification/retention)

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4-12 weeks

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3-6 hours/week

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Teams where recommendation/judgment is core, teams blocked by public-data limits, and orgs reducing failure/churn

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At least one live workflow/MVP with real decisions, outcome logging structure, and data consent/security principles

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✓Procurement/bidding: recommendation -> Go/No-Go -> execution -> success/failure for better Fit Score
✓Real estate: listing score/risk judgment linked with field outcomes to improve prediction
✓Commerce seller ops: item-selection decisions linked to margin/risk and sales outcomes

⚠️

✗Idea stage with little/no real decision execution
✗Organizations unable to establish consent and data-security practices

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Rule-based first-pass tagging, similar-case retrieval, baseline scoring, and data-quality checks

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Approve label definitions, run sampling QA, set KPI targets, and govern sensitive-data policy

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No outcome capture, unstable label criteria, or requests for unsafe collection without de-identification

Humans made decisions, but reasons and outcomes were not captured, so AI did not improve

Judgment, failure, and outcomes accumulate as datasets, continuously improving model accuracy and automation

⚠️ Early schema design is critical because failure definitions and outcome metrics differ by domain.

: Continuous operation model (project duration varies, dataset accumulation is ongoing)

Initial build 4-6 weeks, stabilization 8-12 weeks

Scope-based pricing by domain count and labeling complexity (PoC -> scale contract recommended)

4-12 weeks

3-6 hours/week

Teams where recommendation/judgment is core, teams blocked by public-data limits, and orgs reducing failure/churn

: Continuous operation model (project duration varies, dataset accumulation is ongoing)

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Decision-event schema design: standardize Go/No-Go, score (0-100), risk tags, rationale text, and outcomes
Failure/churn/hold data generation: collect why it failed or paused via structured + narrative inputs
Cross-domain normalization: map domain-specific judgments into common features
Human-in-the-Loop labeling: auto classification + human review for high-quality labels
Training dataset packaging: train/valid/test split with quality metrics
Model improvement loop: prediction -> execution -> outcome -> retraining