Implementation Studio

LLM Knowledge Base Builder

This is now an interactive implementation workspace: set your inputs, generate a concrete markdown blueprint, and export it directly into your repo/Obsidian workflow.

Project configuration

Project name

Domain scope

Primary users

Enabled source channels

Preferred output formats

Generated markdown blueprint

# TekSure Research OS

## 1) Product Goal
Build an LLM-powered knowledge base system for Consumer tech support, digital safety, and learning.
Primary users: Content team, support specialists, and AI agents.

## 2) Source Ingest
- Enabled sources: Web articles, Research papers, GitHub repositories.
- Ingest into /raw with metadata (source URL, author, date, tags, trust score).
- Store local copies of key images for grounded visual references.

## 3) Wiki Compilation
- Compile /raw into /wiki markdown pages.
- Auto-generate: summaries, backlinks, topic indexes, and concept pages.
- Keep a compact registry file so agents can quickly locate relevant pages.

## 4) Agentic Q&A + Outputs
- Use the wiki as the operating context for deep research queries.
- Preferred outputs: Markdown answer docs, Marp slide decks.
- Save generated outputs back into /wiki/outputs so each query improves future context.

## 5) Health Checks
- Detect missing citations and stale facts.
- Suggest new candidate pages from unlinked concepts.
- Run scheduled integrity checks and produce a weekly report.

## 6) Delivery Plan
### Week 1-2
- Build source ingestion adapters and raw metadata schema.
- Implement initial wiki compiler with backlinks + taxonomy.

### Week 3-4
- Add CLI tools for Q&A, report generation, and slide export.
- Add knowledge linting for consistency, freshness, and coverage.

### Week 5+
- Add governance workflow (approve/reject AI edits).
- Track trust + drift metrics by topic.

Health-check preview

Coverage: 3/6 ingest channels selected
Output strategy: diversified
Personas: defined
Domain scope: defined

Next step: wire this into automation

Use this page as the product-facing config layer, then attach backend jobs for ingest, compile, and scheduled linting.

Start implementation sprint