🎧
Support deflection copilot
Answers customer tickets from your help center, past resolutions and product docs — with citations — and hands off gracefully when it's out of its depth.
RAG development services
AI assistants that answer from your documents — with citations.
We build retrieval-augmented generation assistants and production LLM agents that read everything your company knows — docs, tickets, wikis, contracts, SOPs — and answer with the source quoted, the permissions enforced and the accuracy measured.
What we build
Six assistants that know your business cold.
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Internal knowledge assistant
Lives in Slack or Teams. New hires stop pinging seniors: policies, processes and tribal knowledge become one question away, permission-filtered by team.
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Document Q&A
Contracts, research, compliance binders, thousand-page PDFs. Ask in plain English, get the answer with the exact passage highlighted and linked.
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Sales enablement bot
Battle cards, pricing rules, case studies and objection handlers — your reps ask mid-call and get the approved answer in seconds, not a shrug.
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Compliance & SOP assistant
Turns regulatory text and internal SOPs into instant, auditable answers — every response cited to the clause, every query logged for the audit trail.
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Evals & monitoring
A golden-set evaluation harness, accuracy scorecards and drift alerts. You always know the assistant's real accuracy — before your customers do.
Every build includes
Engineered answers, not chatbot guesses.
- Chunking & embedding strategy — tuned to your document types, not a default splitter.
- Permission-aware retrieval — role filters applied before the model sees anything.
- Citations on every answer — the source passage, linked. Trust is checkable.
- Evaluation harness — a golden set of real questions scored weekly, with the numbers shared with you.
- Human handoff — confidence thresholds, one-click ticket creation, full conversation attached.
- Your keys, your cloud — model accounts, vector store and logs all live in infrastructure you own.
Built on proven rails
The stack we ship in production.
- Claude
- GPT
- LangChain
- LlamaIndex
- pgvector
- Pinecone
- Qdrant
- Voyage embeddings
- Cohere rerank
- Langfuse
- Supabase
- Next.js
- Slack
- Teams
- SharePoint
- Notion
Model-agnostic by design: the retrieval layer, evals and guardrails outlive any single model. Swapping the LLM later is a config change, not a rebuild.
How it ships
From document chaos to cited answers in 6–10 weeks.
Document audit
We inventory your sources, design the chunking strategy and flag the gaps that would produce wrong answers.
Retrieval + evals
The pipeline is built alongside a golden set of real questions — accuracy is a number from day one, not a vibe.
Pilot on real traffic
A limited rollout on live tickets or an internal team. We tune retrieval and tone against real questions.
Production + monitoring
Permissions on, scorecards running, handoff live. Weekly accuracy reports for the first quarter.
From $15,000 — live in 6–10 weeks
Fixed-price scope signed before work starts. Model and hosting usage typically runs $200–$1,000/month, billed by the providers to your own accounts — never marked up by us.
Proof, not promises
Flowbase — B2B SaaS, 40-person team
Support was answering the same 200 questions every week.
We built Flowbase a RAG copilot over their help center, API docs and 18 months of resolved tickets. It drafts or sends cited answers, deflects the repetitive majority and creates rich handoff tickets for the rest.
61%tickets deflected
4.8/5CSAT on AI answers
3 wksto production
61%of tickets resolved without a human
Questions
What buyers ask before their first RAG build.
How do you measure accuracy?
Every build ships with an evaluation harness: a golden set of real questions with approved answers, scored on retrieval recall, answer faithfulness and citation coverage — before launch and every week after. You see the number, not a promise.
Will it leak data between teams or customers?
No. Retrieval is permission-aware: every chunk carries access metadata and queries are filtered by the asker's role before the model sees anything. Support can't read HR docs; one customer can never see another's data.
Which LLM do you use?
Fit-for-purpose. Most systems run Claude or GPT for reasoning with smaller models for routing. The architecture is model-agnostic, keys live in your accounts, and swapping models later is a configuration change.
What does a RAG assistant cost?
From $15,000 fixed-price for a scoped assistant over one document set, live in 6–10 weeks. Production platforms with permissions and evals run to $80,000. Usage is typically $200–$1,000/month, billed to your accounts.
Can it hand off to a human?
Yes — confidence thresholds are built in. When it's unsure, it says so, creates a ticket with the full conversation and routes it to the right team. Low-confidence answers are never bluffed.
Pairs well with
AI Voice AgentsLet customers call your knowledge base — the voice agent reads from the same sources.Explore →
Vertical AI AutomationTurn answers into actions: tickets created, CRMs updated, follow-ups sent.Explore →
Data & AI AnalyticsPair document knowledge with live numbers — ask questions across both.Explore →
Your company's knowledge, one question away.
Book a free 30-minute strategy call. Bring your messiest document set — we'll tell you exactly what a RAG assistant over it would cost, and how accurate it can be.