What this service solves
We design the database and knowledge base a company needs so its agents, GPTs and AI models have reliable, current and traceable information. We turn scattered documents, manuals, contracts, proposals and operational history into an indexed, vectorized corpus queryable via RAG, with version control and permissions.
What we solve
- Documentation scattered across Drive, Notion, email and local folders.
- AI agents that hallucinate because they don't have access to the company's source of truth.
- Unstructured, untagged data for analytical use cases.
- No governance: nobody knows which version is the official one.
What's included
- Audit and inventory of current corporate knowledge.
- Knowledge base schema design.
- Automated ingestion from Drive, Notion, SharePoint, etc.
- Cleaning, deduplication and enrichment of documents.
- Vectorization and loading into a database (Pinecone, Weaviate, Supabase).
- Permissions, versioning and refresh policy.
How we work
- Corporate knowledge discovery.
- Schema design and stack selection.
- Ingestion pipeline implementation.
- Vectorization and retrieval testing.
- Documentation and technical handover.
How we work: service methodology
- Phase 1 · Data Source Audit and Entity Mapping (Week 1) — A thorough inventory of databases, APIs, files and systems where corporate information lives.
- Phase 2 · Unified Schema and Relational/Vector Model Design (Week 1-2) — Defining the master entity schema (Customers, Transactions, Products, Documents) in PostgreSQL / BigQuery / Pinecone.
- Phase 3 · Building ETL/ELT Pipelines and Ingestion Connectors (Week 2-3) — Developing automated sync flows in Python / n8n with error handling and schema validation.
- Phase 4 · Cleaning, Deduplication and Semantic Indexing (Week 3-4) — Cleaning corrupted records, normalizing formats and generating embeddings for vector search.
- Phase 5 · Data Dictionary Documentation, Security and Handover (Week 4) — Configuring access roles, automated backups, and delivering the data dictionary and technical architecture manual.
What we need from your team
- An inventory of databases and software used across the company.
- Anonymized samples of the data to be unified.
- Read credentials for source databases or APIs and the destination cloud environment.
Deliverables
- A production knowledge base.
- Automated ingestion pipeline.
- Technical documentation.
- Governance and permissions policy.
- Usage metrics dashboard.
Who it's for
- Companies with a lot of unorganized internal documentation.
- Teams that want to build AI agents or corporate GPTs.
- Legal, HR and sales departments with scattered critical knowledge.
Signs you need it
- Company information lives spread across Drive, CRM, ERP and loose spreadsheets, with no one sure which version is official.
- AI projects stall because there's no clean, unified database to feed them.
- AI assistants or agents you already tried make up answers because they don't have structured access to the business's real knowledge.
- Every team interprets the data differently because there's no shared dictionary or data model.
Measurable KPIs
- Documents ingested and current in the KB.
- Retrieval response time (latency).
- Answer accuracy of the RAG-powered agent.
- Reduction in time spent on internal queries.
Expected outcome
The company moves from scattered knowledge to a live, governed database connected to its AI agents. Every agent answer cites official sources, leaders know which documents are the source of truth, and teams stop manually searching for what already exists.
Technology stack
- Pinecone
- Weaviate
- Supabase pgvector
- Chroma
- LlamaIndex
- LangChain
- n8n
- Notion
- Google Drive API