AINA Data Engine Room Founder Closeout Report

This is the pause report for external review. The short version: the data engine room has become a serious local production spine for the Personalization Engine, but it is still intentionally local and gated. It is ready to be reviewed, backed up, and used as the source of truth for the next integration lane.

What We Built

We turned the repo into a self-contained authority layer for AINA personalization:

role/title/context -> source authority -> AI Fluency capability map -> workflow/practice/evaluator/proof refs -> platform-safe export

This means the engine is no longer just a giant job-title list. It now knows how to preserve source evidence, avoid repeating old LLM work, prove which data is trusted, and produce safe exports for the future Academy/platform layer.

Why This Matters

The core product vision remains:

From AI anxiety to AI fluency: assessment, simulation, personalized curriculum, and proof.

The engine room now supports that idea headlessly. It can prepare role-aware capability maps, recommend workflow/practice/evaluator paths, and keep proof/source references attached. It also knows what it should refuse to do until production release gates exist.

What Is In The Repo

The repo contains:

The data-engine code and CLI commands in src/aina_data_engine/.
Tests in tests/.
Hundreds of validation receipts in artifacts/validation/.
Human-readable reports in artifacts/reports/.
Planning boards and milestone maps in docs/planning/.
Date-stamped handoffs in docs/handoff/.
Anti-loop learnings in docs/learnings/.
Local vector/chunk snapshots under artifacts/embeddings/production/.

Tracked inventory:

Area	Count
Total tracked files	1,554
Docs	473
Reports	376
Validation receipts	361
Tests	111
Source files	137

What Has Been Done

Major completed surfaces:

Area	What changed
Title/role coverage	The repo has a broad local title universe and top 500/top 1,000 cohort proof.
JD-aware context	The plan moved away from fixing titles in isolation and toward using JD/company/industry/responsibility/workflow context.
AI Fluency map	Five-layer capability map exists locally: task exposure, tool proficiency, judgment quality, data discipline, outcome evidence.
Runtime contracts	Local payloads, evaluator refs, proof refs, curriculum/workflow refs, and export contracts exist.
Embeddings	151,983 valid Gemini vectors are present, with no stale vectors and complete top 500/top 1,000 vector coverage.
Retrieval proof	AIN-510 exact-cosine retrieval gate is promotion-ready locally, with runtime embedding authority still off.
Donor repo harvesting	Prior work from `personalization-engine-aina`, E5, source intelligence, workflow grounding, review packets, alpha feedback, and industry taxonomy has been inventoried and reduced into receipts.
Anti-loop policy	The repo now records that already-reviewed donor data should be verified and reused before burning tokens on repeat title-by-title review.
Export boundary	Top 500/top 1,000 platform-safe exports exist without raw JDs, raw learner data, vector blobs, or unsafe labels.
Retirement proof	Donor retirement pack exists, but no deletion is allowed.

Important Numbers

Metric	Value
Combined semantic chunks	467,436
Valid vector rows	151,983
Stale vector rows	0
Unvectorized chunks	315,453
Top 500 vector coverage	500
Top 1,000 vector coverage	1,000
Top 500 export rows	500
Top 1,000 export rows	1,000
Source-authority registry rows	48
Donor retirement entries	59
Prompt/workflow/ontology files inventoried	7,916
Prompt/workflow/ontology parseable rows	48,534
Industry taxonomy rows verified	17,118
Review packet rows counted	2,000

What Is Not Live Yet

This is very important: this repo is not yet the public product runtime.

Still blocked:

Public runtime.
Real-user data.
Production telemetry.
External writes.
Runtime embedding authority.
Donor repo deletion.
Raw JD/company/learner export.
Broad ungated embedding of every remaining source family.

That is a good thing. The work is useful because it is controlled and reviewable.

Branch And Backup Situation

The donor-promotion branch is safe to fast-forward into local main because main is an ancestor of the branch.

Two audit branches are preserved but not merged:

codex/curriculum-release-audit-2026-06-15
codex/source-intelligence-scaleout-audit-2026-06-15

They may contain useful findings, but they should be manually reviewed and ported if needed. They are not product authority by themselves.

This checkout currently has no GitHub remote configured, so the immediate preservation route is a verified local git bundle under /srv/aina/checkpoints/aina-data-engine-room/2026-06-16-closeout/. A private GitHub backup can be added later once the target repo/org is chosen.

Current Mission

The current mission is:

Make AINA Data Engine Room the local production data authority, and let Academy/platform consume only pinned, safe exports from it.

In practical terms:

Use existing validated data first.
Do deterministic repair and schema checks before LLM review.
Use embeddings only after source families are clean and useful to product exports.
Keep runtime authority off until release receipts exist.
Treat donor repos as read-only sources, not active product surfaces.

Milestone Status

Milestone	Status
Preserve and reconcile repo state	Closing now.
Source authority registry	Working and passing.
Platform-safe export manifest	Working and passing.
Academy consumer proof	Working locally for top 500/top 1,000.
AI Fluency capability map	Working locally/headlessly.
Retrieval proof	Working locally with exact cosine.
Embedding expansion	Partially complete; should resume only after source-family gates.
Public production launch	Not enabled and not claimed.
Donor retirement	Ledger exists; no deletion approved.

Recommended Next Moves

After external review:

Decide whether to create a private GitHub remote for backup.
Review the two preserved audit branches and manually port anything valuable.
Let aina-academy consume the pinned top 500/top 1,000 export.
Continue source-authority work from already-validated donor data instead of rerunning broad LLM reviews.
Resume embeddings only for clean, product-consumed source families.
Keep Cloudflare/runtime integration as a separate lane with explicit auth/privacy/release receipts.

Plain-English Bottom Line

AINA now has a serious local Personalization Engine data spine. It has source receipts, export contracts, runtime boundaries, AI Fluency capability-map proof, retrieval proof, vector proof, and donor-reuse discipline.

The next risk is not “we do not have enough data.” The next risk is accidentally redoing good prior work or promoting raw/noisy data too early. The correct next move is to preserve this state, get it reviewed, and then integrate the Academy/platform layer through safe exports.