Document ingestion, chunking, and embedding pipelines
AI systems grounded in your company's actual knowledge — policies, docs, product data, customer history — that retrieve, reason, and answer with citations instead of hallucinations.
AI Services
RAG
AI systems grounded in your company's actual knowledge — policies, docs, product data, customer history — that retrieve, reason, and answer with citations instead of hallucinations.
RAG
Grounded AI that answers from your internal knowledge — with citations.
PineconeSupabasepgvectorOpenAIAnthropic ClaudeLangChainLlamaIndex
Operating problem
Where this usually breaks
SaaS teams building knowledge-base support assistants
Internal tools teams enabling self-serve answers from docs
Compliance-heavy industries requiring sourced responses
Agencies productizing internal IP for client use
What gets built
A system, not a one-off workflow
Grounded AI that answers from your internal knowledge — with citations.
Vector database design (Pinecone, Supabase, pgvector)
AI systems grounded in your company's actual knowledge — policies, docs, product data, customer history — that retrieve, reason, and answer with citations instead of hallucinations.
Hybrid retrieval (semantic + keyword) for accuracy
AI systems grounded in your company's actual knowledge — policies, docs, product data, customer history — that retrieve, reason, and answer with citations instead of hallucinations.
Deliverables
- Document ingestion, chunking, and embedding pipelines
- Vector database design (Pinecone, Supabase, pgvector)
- Hybrid retrieval (semantic + keyword) for accuracy
- Citations and source linking in every answer
- Evaluation harness to measure retrieval quality
- Re-indexing schedules so the knowledge stays fresh
Best fit for
- SaaS teams building knowledge-base support assistants
- Internal tools teams enabling self-serve answers from docs
- Compliance-heavy industries requiring sourced responses
- Agencies productizing internal IP for client use
Outcomes
Why teams keep this running after launch
01
AI that grounds answers in your real knowledge — not made up
02
Citations make every answer auditable and trustworthy
03
Fresh data via scheduled re-indexing, not stale snapshots
04
Evaluation harness catches retrieval regressions early
Implementation
How the engagement runs
- Step 1
Discovery
Map your workflow, tools, success metrics, and constraints.
- Step 2
Design
Document inputs, outputs, edge cases, and the system architecture.
- Step 3
Build
Implementation wired into your real environment with monitoring.
- Step 4
Handover
Runbooks, documentation, and a clean transfer to your team.
FAQ
Why not just put all our docs into ChatGPT's context?
Context windows are limited, costly, and lose precision quickly. Proper RAG retrieves the right 5–10 chunks for each question — faster, cheaper, more accurate, and citable.
How do you handle confidential data?
Embeddings and indexes can run in your environment. I work with self-hosted options (pgvector on your Postgres) when data residency matters, or vendor solutions with the right contracts.
How do you measure if RAG is working?
Every system ships with an evaluation harness — a set of question-answer pairs we score continuously. Retrieval quality, answer accuracy, and citation rate are tracked over time.
Ready to ship a RAG system?
Book a free consultation. We'll scope your workflow and decide if this is the right first build for your team.