Custom RAG & SQL Agents
built for your data
I design, build, and ship production-grade retrieval-augmented generation systems and natural-language SQL agents — so your team gets answers from your documents and databases in plain English, with citations, evals, and zero hallucinations.
Two products. One engineer. End-to-end delivery.
I don't hand you a notebook. I ship a deployed, monitored, evaluated system your team can actually use — then train your engineers to maintain it.
End-to-end retrieval-augmented generation pipelines that ground LLM answers in your private documents. Built with chunking strategies tuned to your content type, hybrid retrieval (keyword + semantic), reranking, and answer-verification layers so users get accurate, source-cited responses every time.
- Document ingestion: PDFs, Notion, Confluence, Slack, Google Drive
- Hybrid retrieval: BM25 + dense embeddings (OpenAI, Cohere, or local)
- Reranking with cross-encoders for precision
- Citations and source previews in every answer
- Eval suites: faithfulness, relevance, citation accuracy
- Streaming responses for sub-2s perceived latency
Conversational agents that translate business questions into validated SQL against your existing databases. Schema-aware prompt construction, query validation, read-only execution safety, and natural-language summaries of the results — so non-technical stakeholders self-serve analytics without breaking anything.
- Schema introspection and dynamic prompt construction
- Read-only DB user + query validation for safety
- Multi-turn conversations with context (filters, follow-ups)
- Auto-generated visualizations (charts, tables)
- Natural-language summaries of every result set
- Works with Postgres, MySQL, Snowflake, BigQuery, Redshift
Don't read a slide deck. Try the product.
All three tabs are real, end-to-end systems powered by Python LangChain + Groq (gpt-oss-20b) + nomic-embed-text + FAISS. The RAG agent retrieves from 7 preloaded policy docs; the SQL agent writes real SQL against a Mysql database; the Supervisor routes a single question to either or both and synthesizes a combined answer.
Upload PDF / TXT / MD
Drop a file or click to browse · indexed in FAISS, persisted to disk
Upload a document to start
Drop a PDF / TXT / MD above. The agent embeds it into FAISS, then answers your questions with citations from the document.
No documents uploaded yet — RAG queries will return empty until you upload one.
Press Enter to send · Real LLM via Groq · LangChain + FAISS vector store
⚡ Backend: FastAPI · LangChain · Groq · FAISS · Mysql
Built for knowledge-intensive industries
The same RAG + SQL primitives, tuned to each industry's accuracy, compliance, and latency requirements.
Legal
Case-law GraphRAG for due diligence — retrieve precedents, statutes, and rulings with citations partners can trust.
"Find every ruling in CA on non-compete clauses from 2020+"
Financial Services
Research assistants that synthesize filings, transcripts, and internal memos. SQL agents for portfolio analytics.
"Summarize the risk factors from this 10-K with citations"
Healthcare
Clinical knowledge bases grounded in SOPs, formularies, and guidelines — with strict source attribution and audit logs.
"What's the protocol for adult sepsis at our hospital?"
E-commerce Ops
SQL agents for inventory, fulfillment, and LTV analytics — letting ops teams self-serve without pinging data.
"Which SKUs have <10 units left and >50 weekly sales?"
Manufacturing
Maintenance manuals + IoT logs in one RAG. Technicians ask in plain language, get the right page + safety warnings.
"Why is line 3 showing error code E-447?"
Education
Course-material tutors that answer student questions strictly from the syllabus — no made-up citations, ever.
"Explain the difference between TCP and UDP from week 4"
Travel & Hospitality
Policy + booking agents that handle complex multi-step questions across reservation, loyalty, and fare-rule systems.
"Can I rebook a cancelled flight to a partner airline?"
Internal IT / DevOps
Runbooks, postmortems, and Confluence in one RAG. On-call engineers resolve incidents faster with cited answers.
"What's the rollback procedure for the payments service?"
From zero to deployed in 6 weeks
Transparent milestones. Weekly demos. Code in your repo. You see the system working before any large commitment.
Discovery (Week 1)
We map your data sources, success criteria, latency budget, and compliance constraints. You get a written architecture proposal with eval criteria — before any code is written.
- Architecture doc
- Eval rubric
- Fixed-quote proposal
Prototype (Week 1–2)
A working slice of the system on a real subset of your data — chunking strategy, retrieval, answer generation, eval scores. You see it work before committing to full build.
- Live prototype URL
- Baseline eval report
- Refined scope
Build (Week 2–4)
Full implementation: ingestion pipelines, vector store, retrieval + reranking, query layer, observability. Weekly demos. Code lives in your repo from day one.
- Production code
- CI/CD
- Eval suite (automated)
Eval & Hardening (Week 4–5)
Adversarial testing, hallucination checks, latency tuning, security review. We hit your success metrics before launch — not after.
- Eval report (passing)
- Latency benchmarks
- Security checklist
Deploy (Week 5–6)
Deployed to your cloud (AWS / GCP / Azure / on-prem) with monitoring, alerting, and rollback. Documentation and a 1-hour handoff session for your team.
- Live deployment
- Runbook
- Team handoff session
Iterate (Ongoing)
Optional retainer: weekly eval runs, drift detection, model upgrades, and feature additions. Or I train your team to own it — your call.
- Weekly eval reports
- On-call support
- Training sessions
Fixed-scope projects. No retainers required.
Pay for outcomes, not hours. Every project starts with a free architecture proposal — you commit only after you see the plan.
Starter
A working RAG MVP on your data
- RAG over up to 3 document sources
- Hybrid retrieval (BM25 + embeddings)
- Citations + source previews
- Basic eval suite (20 test cases)
- Deployed to your Vercel / Render
- 1 round of revisions
- 30 days post-launch support
Pro
Production RAG + SQL agent, fully integrated
- RAG over unlimited sources
- Natural-language SQL agent on 1 DB
- Reranking + answer verification
- Full eval suite (100+ test cases)
- Observability (Langfuse / LangSmith)
- Deployed to your AWS / GCP / Azure
- 3 rounds of revisions
- 90 days post-launch support
- 2-hour team handoff training
Enterprise
Multi-system + on-prem + dedicated support
- Everything in Pro, plus:
- Multi-DB / multi-tenant SQL agents
- On-prem or VPC deployment
- Custom compliance (HIPAA, FedRAMP)
- Model fine-tuning or local LLMs
- Dedicated Slack channel
- Quarterly eval + drift reports
- On-site workshop (optional)
Need something different? Tell me about your project and I'll propose a custom scope.
Tell me about your data.
I'll send you an architecture proposal within 24 hours.
No sales call required to start. Send me what you're trying to build, what data you have, and any constraints (compliance, latency, budget). You'll get a written proposal back — including a fixed quote if it fits the Starter or Pro tier.