Solutions & Services · RAG Development Services
Answers your team can trust, from knowledge you already own.
Your organization’s real knowledge lives in contracts, wikis, tickets, policies and drive folders — and your people burn hours hunting for it or asking colleagues. Kimbodo builds production retrieval-augmented generation (RAG) systems: governed chat and search over your own documents, with citations, permissions and measurable answer quality. Built in weeks, run in your cloud.
A production RAG system: governed ingestion, retrieval tuned to your content, and grounded answers with citations — delivered where your team already works.
What Kimbodo Builds
Knowledge Assistants
One governed answer source for a whole team.
- Internal knowledge-base chatbots with citations
- Slack and Microsoft Teams assistants
- Policy, legal and compliance Q&A with approved answers
- Customer-support answer engines with human escalation
Retrieval Pipelines
The unglamorous 80% that makes RAG actually work.
- Document parsing for PDFs, office files, email and scans
- Chunking, metadata and permission-aware indexing
- Vector stores: pgvector, Pinecone, OpenSearch, Vertex AI Search
- Hybrid search and re-ranking tuned on your content
Quality & Governance
Provable accuracy, not vibes.
- Retrieval and answer evaluation harnesses
- Hallucination controls, citation enforcement and refusals
- Role-based access mirrored from your source systems
- Usage, cost and answer-quality monitoring
Timeline & Engagement Model
- Weeks 1–2: Discovery & corpus audit — sources, permissions, answer-quality targets and security review
- Weeks 3–6: Build — ingestion pipeline, retrieval tuning, grounded generation, first user interface
- Weeks 7–8: Validate — evaluation against a golden question set with your subject-matter experts
- Weeks 9–12: Harden & launch — permissions, monitoring, documentation and rollout; optional ongoing optimization subscription
Proof, Not Promises
- AI-powered legal knowledgebase bot — governed retrieval over legal content, with approved answers delivered through Slack
- AI-powered talent & employer chatbot with human escalation — knowledge-grounded chat with human approval flows
- AI-powered service review system with live chat & email integration — customer-facing answers wired into real business systems
Buyer Questions
How much does a RAG system cost?
Focused single-team assistants typically start around $35,000. Multi-source, permission-aware systems with evaluation harnesses and production hardening usually land between $55,000 and $170,000 depending on integrations and data readiness. The estimator gives you a range for your specific scope in about a minute.
Can it run entirely inside our cloud?
Yes. We deploy into your AWS, GCP or Azure account — including fully private setups with Claude on Bedrock or Gemini on Vertex AI — so documents and embeddings never leave your perimeter.
How do you prevent wrong answers?
Every build ships with citation enforcement, refusal behavior for out-of-scope questions, and an evaluation harness scored against a golden question set your experts approve. You see measured answer quality before launch, and monitoring keeps measuring it after.
What if our documents are a mess?
That’s normal. The discovery phase includes a corpus audit, and the ingestion pipeline handles parsing, deduplication and metadata extraction. If deeper data work is needed we scope it explicitly rather than letting it silently eat the budget.
Know what your RAG system would cost — right now
Four questions. Instant preliminary range, timeline and architecture. Reviewed by an engineer, not a salesperson.