Business Memory & Knowledge Graph — Seed Template & Ingestion Checklist

What this tool does

This seed template helps you capture and structure the business knowledge that matters: clients, projects, deliverables, decisions, insights, and the artifacts that go with them. It’s designed so both humans and AI agents can find context quickly, reduce repeated work, and safely automate routine tasks.

Why it matters (the hunger)

One-person and small businesses grow brittle when useful knowledge is scattered in inboxes, notes, and file folders. A simple business memory makes repeated work easier, speeds onboarding, improves proposals and support, and enables safe automation.

Quick overview

• A starter schema (entities and core fields)
• Tagging and naming guidelines
• An ingestion checklist for documents and call notes
• Sample queries and usage scenarios (proposals, support, summary briefs)
• Operational notes: privacy, ownership, and automation tips

Starter Schema (entities and suggested fields)

Keep the model lightweight. Start with these entities and extend as you learn.

Client

• client_id (stable identifier)
• name
• industry
• contact_points (emails, phone, roles)
• status (lead, active, paused, former)
• notes (summary, relationship context)

Project

• project_id
• client_id (link)
• title
• scope_summary
• start_date / end_date
• status
• value_estimate

Deliverable / Asset

• asset_id
• project_id
• type (proposal, template, report, contract)
• version
• location (link or storage pointer)
• tags

Decision

• decision_id
• project_id / client_id
• decision_text
• made_by
• date
• implication (next steps, risk)

Insight / Note

• insight_id
• related_entities (links to client/project/asset)
• summary
• confidence (low/medium/high)
• source (call, document, observation)

Tagging & Naming Guidelines

Consistent tags let you filter quickly, and make AI retrieval far more reliable.

• Use short, composable tags: e.g. product-launch, pricing, compliance, case-study
• Prefer controlled values where possible: have a list for tags like region, industry, and engagement-type
• Name files for rapid scanning: client_project_asset_v01.pdf (avoid spaces, include client and a brief descriptor)
• Include source metadata: who captured the note, call date, channel
• Versioning: append v1, v2 or date (YYYYMMDD) for deliverables

Ingestion Checklist (documents, call notes, emails)

Capture the minimum useful metadata every time to make items discoverable and actionable.

1. Assign or confirm client_id and project_id.
2. Set document type (proposal, invoice, contract, note, spec).
3. Add a short one-line summary (what is it and why it matters).
4. Apply 2–5 tags from your controlled tag list.
5. Capture who created it and when (timestamp).
6. For call notes: capture key decisions, open items, and assigned owners.
7. For sensitive content: mark privacy_level (public/internal/confidential) and record any access restrictions.
8. Store a pointer to the file or transcript and optionally extract a short text excerpt for embedding/indexing.

Sample queries & usage patterns

Use queries to surface context for a proposal, to prepare for a client call, or to automate reminders.

Example: "Find the last three decisions for client ACME about pricing and any open action items".

Translated into retrieval steps:

• Fetch decisions where client_id = ACME and tags include pricing, ordered by date desc limit 3
• For each decision, list open action items and responsible owner
• Include links to the underlying notes or recordings

Example: "When preparing a proposal, show recent deliverables, past pricing, related insights, and any flagged risks for this client."

Operational notes — automation, AI, and safety

• Start small. Index client records and call notes first, then add assets and decisions.
• Use embeddings thoughtfully. Store short cleaned text excerpts (or transcripts) for semantic search and keep pointers to originals for verification.
• Record provenance. Always store who added or validated an item so an AI agent can surface a confidence level and a human can verify before acting.
• Access control. Tag sensitive items and limit automated workflows that can share confidential data.
• Keep the schema flexible. Add fields when a new repeated need appears, but avoid over-modeling early on.

Suggested implementation steps

1. Pick a storage approach you can extend: lightweight graph DB, document DB with typed metadata, or a combined approach (document store + graph layer).
2. Create a small controlled tag list and a short intake form for new items.
3. Index the most recent 6–12 months of client work first so queries immediately return useful context.
4. Run a 2-week experiment: use the ingestion checklist for every call and document, then measure whether proposals or support tasks get faster or require fewer clarifications.

Next steps & templates

Copy this seed, then customize fields and tags for your domain. Consider adding an interactive intake form to capture the ingestion checklist in structured form (see capability notes).

Privacy & compliance reminder

Do not store regulated personal data without appropriate consent and controls. Treat client contracts, health data, and financial information as restricted and add explicit retention and access rules.

Where this tool fits

This starter template is best for entrepreneurs and small teams who want immediate value from a simple, operational business memory—faster proposals, better support context, and safer automation.