Decision Dataset Foundry
We structure human judgment, actions, and failures from live operations into datasets for AI improvement.
How to quickly see whether this service fits
We surface the key points you need for a first decision.
Best fit for
Teams where recommendation/judgment is core, teams blocked by public-data limits, and orgs reducing failure/churn
First thing we tackle
4-12 weeks
Check this first
Idea stage with little/no real decision execution
Service Overview
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 proceed-or-pause decisions, scoring, failure reasons, and execution outcomes into model-trainable decision datasets.
Key Benefits
Process
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
Deliverables
Service Information
Self-Diagnosis Checklist
📋 Suitable Cases
- ✓Procurement/bidding: recommendation -> proceed-or-pause decision -> 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
⚠️ Unsuitable Cases
- ✗Idea stage with little/no real decision execution
- ✗Organizations unable to establish consent and data-security practices
Design Approach
Rule-based first-pass tagging, similar-case retrieval, baseline scoring, and data-quality checks
Approve label definitions, run sampling QA, set KPI targets, and govern sensitive-data policy
No outcome capture, unstable label criteria, or requests for unsafe collection without de-identification
Real Implementation Case
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
Verification Results
Recommended Path
Data / Performance Analytics
When data is accumulating but it is unclear what to watch or how to read performance structurally
Related Services
AI-SYSTEM Operating OS
Fix decisions before execution and organize the operating flow during execution
AI Bid / Support Opportunity Recommendation
Find and screen public projects, grants, and bidding opportunities faster
AI Business Intelligence Dashboard
Analyze acquisition, channel, and revenue data in one view
We answer the highest-risk questions before procurement does.
For B2B customers, trust is not a supporting detail. These five rules are the baseline across our service surfaces.
Data scope
We use the minimum information needed for the workflow and explain what enters the system and what is stored.
AI usage boundary
We separate AI-supported steps such as summarization, recommendation, and draft generation from final human judgment.
Human approval points
External delivery, customer response, final submission, and spending-related steps default to human review.
Logs and auditability
Operators should be able to trace what entered, what was suggested, and where the process stopped when something fails.
Access control
We separate operator, reviewer, and admin responsibilities and avoid broad access to internal-only data.
What we lock before launch
- •What data can enter the workflow
- •Which outputs must never go out without review
- •Where the flow stops and who confirms issues
- •What logs operators need to resolve incidents quickly
What you can confirm before talking to us
Data scope
Human approval points
Logs and auditability
Service Information
Project Duration
Initial build 4-6 weeks, stabilization 8-12 weeks
Price
Scope-based pricing by domain count and labeling complexity (PoC -> scale contract recommended)
Implementation Period
4-12 weeks
Human Resources
3-6 hours/week
Suitable Organization
Teams where recommendation/judgment is core, teams blocked by public-data limits, and orgs reducing failure/churn
Verification Results
Main Services
- Decision-event schema design: standardize proceed-or-pause status, 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