RAG & Knowledge Systems Development

RAG & knowledge systems

Chat with your own data — properly grounded.

Retrieval-augmented assistants and search experiences over your docs, tickets and product knowledge. Hybrid retrieval, citations, access control — built to hold up under audit, not just under a demo.

Citation-first responses

Per-tenant isolation

Eval-gated rollouts

Production at scale

Scope a knowledge system See live deployments →

Knowledge · ask

query · 8421

Question

What's our refund policy for annual plans paid via invoice?

Retrieved · top 3

policies/refunds.md

§2.4 · score 0.91

legal/MSA-v3.pdf

p. 11 · score 0.84

support/macros.notion

annual · score 0.79

Answer

Annual plans paid by invoice are refundable pro-rata within 30 days of the latest renewal^[1], subject to the MSA cancellation clause^[2]. Support macros mirror this in customer-facing replies^[3].

3 sources · 0.38s · $0.0019✓ grounded

Citation acc.

98% ↑

p95 latency

0.4s

25+

Knowledge bases shipped

98%

Citation accuracy

12M+

Documents indexed

0.4s

Median p95 latency

The retrieval pipeline

Four stages. All four, engineered.

Every "chat with your docs" demo skips at least one of these. Production deployments fail there. We build all four, deliberately.

Ingest

Every source, normalised.

Loaders for Notion, Drive, Confluence, Linear, S3, PDFs, tickets and your warehouse — with deduping, OCR and structured-metadata extraction.

20+ source connectors
OCR + table extraction
Permission inheritance

Embed

Chunked and embedded with intent.

Semantic chunking sized to your domain, dense + sparse embeddings, and a re-index pipeline that re-runs when models or content change.

Domain-aware chunking
Dense + BM25 hybrid
Scheduled re-indexing

Retrieve

Hybrid retrieval, then rerank.

Vector + keyword + filters, then a reranker for relevance — so the top-k passed into the LLM is actually the right top-k.

Hybrid + metadata filters
Cross-encoder rerank
Recall@k benchmarks

Synthesize

Grounded answers, with sources.

Answers that cite their chunks, refuse politely when the knowledge isn't there, and never quietly hallucinate over missing context.

Inline citations
Refusal templates
Faithfulness evals

The knowledge stack

Frontier models, boring storage — exactly where each belongs.

Every tool below is on a live knowledge base this quarter — not an aspirational architecture diagram.

Anthropic Claude
Synthesis
OpenAI
Embeddings
LangChain
Orchestration
Supabase
Vector store
PostgreSQL
pgvector
Vercel AI SDK
Runtime
Notion
Source
Slack
Surface

Why RAG, why now

Your team alreadyknows the answer.

Most of what your customers, support agents and new hires ask is already written down somewhere. The problem isn't that the knowledge doesn't exist — it's that nobody can find the right paragraph fast enough. RAG turns scattered documents, tickets and decks into a single source you can ask in plain English.

Done well, it cuts handle times, on-ramps new joiners faster and quietly improves every customer touchpoint. Done badly, it hallucinates confidently and breaks trust on day one. The difference is engineering, not magic.

2.5h

avg. day spent searching for info

41%

support handle-time cut on launch

98%

citation accuracy at production

Use cases

Six workloads where RAG already pays back.

We'll only recommend a knowledge system where the content exists, the access model is clear, and there's a real metric to move.

01Internal docs

Internal knowledge assistants

One assistant that answers across Notion, Drive, Confluence and your wiki — citing the page, not paraphrasing it.

Notion / Drive / Confluence
Per-team scoping
Citation-first answers

02Support

Customer support copilots

Suggest grounded replies in Zendesk / Intercom, drafted from your KB, prior tickets and product changelog.

Inline ticket drafts
Macro-aware tone
Confidence scoring

03Sales

Sales enablement search

Reps ask in Slack and get the right case study, pricing slide, or objection answer — pulled from decks and CRM.

Deck + CRM sources
Account-aware answers
Slack-native surface

04Compliance

Policy & compliance Q&A

Answer regulatory and policy questions with strict source-only synthesis — audit log on every retrieval and generation.

Strict source-only mode
Per-query audit trail
Document version pinning

05Product

Product knowledge agents

User-facing assistants over your help center, changelog and API reference — with deep-links back to docs.

Help-center search
API doc grounding
Feedback loop on answers

06Research

Research & analyst assistants

Brief generation over reports, transcripts and warehouse data — with cited sources and an export-ready format.

Multi-doc synthesis
Markdown / docx export
Scheduled monitoring

Live deployments

Indexed, retrieved, trusted in production.

Explore the full portfolio

Enterprise · India

Internal Q&A across 280k Confluence pages and Drive folders.

0.38sp95 retrieval + answer

SaaS Support · US

Support copilot cut average handle time by 41%.

−41%avg. handle time

Regulated · FinTech

Policy Q&A passed a Big-4 source-traceability audit.

100%answers source-traceable

Our approach

Source to citationin six fixed stages.

Recall@k before vibes. Citation accuracy before launch. Latency and cost dashboards before invoices.

Avg. build: 6–10 weeks

01
Source & access mapping
What knowledge lives where, who can read it, and what we're explicitly never allowed to surface.
Deliverables: Source map · ACL model
Week 1
02
Eval set first
Before a single chunk is embedded, we write the golden Q&A set. Retrieval and answer quality ship behind it.
Deliverables: Golden eval set
Week 1
03
Ingest & embed
Loaders, deduping, OCR, semantic chunking and a re-index schedule sized to how often content changes.
Deliverables: Indexer · Re-index plan
Weeks 2–3
04
Retrieval tuning
Hybrid retrieval, filters, reranker. We tune against Recall@k and citation accuracy — not against a demo.
Deliverables: Retrieval benchmarks
Weeks 3–4
05
Synthesis & surfaces
Grounded generation, citation rendering, refusal handling — plumbed into Slack, Zendesk, in-product widgets.
Deliverables: Surfaces · Eval pass
Weeks 4–6
06
Observability & evolve
Live latency, cost and faithfulness dashboards. Quarterly re-eval as content and models shift.
Deliverables: Dashboards · Retro
Ongoing

Standard package

Eval suites, citations, dashboards

What's included

Every knowledge system shipswith the engineering, not just the demo.

Connectors that respect permissions, retrieval you can audit, and dashboards that surface drift before it surfaces complaints.

01Source connectors with permission inheritance baked in
02Domain-aware chunking strategy + scheduled re-index pipeline
03Hybrid retrieval (vector + BM25) with cross-encoder rerank
04Citation-rendering UI: file, page, link back to the source
05Golden eval set + live faithfulness regression dashboards
06Per-tenant data isolation and configurable PII redaction
07Latency, cost and recall dashboards in Looker Studio
0830-day post-launch tuning window — prompts, evals, chunking

FAQ

Honest answersbefore you ask.

Can't find what you're looking for? Send a brief — we reply within a business day.

01: How is this different from a generic AI chatbot?
A generic chatbot guesses from training data. A RAG system retrieves the relevant chunks from your knowledge first, then generates an answer that cites those chunks — and refuses politely when the knowledge isn't there. The work is in the retrieval, not the prompt.
02: Where does our data actually live?
By default on your cloud (AWS, GCP, Azure) or in a managed Postgres / Supabase instance you control. Per-tenant isolation, configurable PII redaction, and the option to keep embeddings on-prem. We sign DPAs and align to your data-residency rules.
03: What stops it from hallucinating?
Three things: retrieval quality (we benchmark Recall@k and citation accuracy before launch), grounded synthesis (the model only answers from retrieved chunks), and a refusal template when the knowledge isn't there. Live faithfulness evals catch regressions as content drifts.
04: How fresh is the knowledge?
Depends on the source. Notion, Slack, Drive and your warehouse can be near-real-time via webhooks. Larger document corpora ship with a re-index schedule you set — typically hourly, daily or on-change.

Let's scope

Got a corpus that should already be queryable?

Book a source-mapping call. We'll review where your knowledge lives, score the use-case, and tell you honestly whether RAG is the right answer — no decks, no pressure.