How do you prevent hallucinations?

We build evaluation harnesses that measure hallucination rate on held-out test sets, and deploy output validation and citation grounding that rejects low-confidence answers.

What document types can you ingest?

PDFs, Word docs, HTML, Markdown, Confluence, Notion, SharePoint, Salesforce knowledge base, and custom data sources via API.

How do you handle document permissions?

We implement user-level and role-level retrieval filtering so each user only retrieves content they're authorised to see.

How do you keep the index fresh?

Scheduled re-indexing, change detection webhooks, and incremental update pipelines ensure your assistant always reflects current documents.

Service/Legal

RAG Systems & Knowledge AI

AI that answers from your data — with citations, not guesses.

We design and build Retrieval-Augmented Generation (RAG) systems that ground AI responses in your documents, policies, and knowledge bases. Every system ships with citation tracking, freshness management, and hallucination evaluation.

Book a Discovery Call See case studies

Key outcomes

AI answers grounded in your actual documents with source citations
Reduced hallucination rates below production thresholds
Always-fresh index reflecting latest content updates

Delivery speed

AI-speed delivery

RAG architecture with chunking and embedding strategyVector store setup and ingestion pipelineCitation and source-attribution frameworkEvaluation harness (RAGAS, custom metrics)Freshness scheduler and re-indexing automationRAG architecture with chunking and embedding strategyVector store setup and ingestion pipelineCitation and source-attribution frameworkEvaluation harness (RAGAS, custom metrics)Freshness scheduler and re-indexing automation

Who it's for

Built for teams that need this now

This service was designed around a specific kind of problem. If any of these sound like your team, you're in the right place.

Knowledge management teams

Struggling with findability in large internal document repositories

Customer support teams

Who need AI-assisted answers grounded in product and policy documentation

Legal and compliance teams

Needing accurate, cited, source-traceable answers from contract and policy documents

Common triggers

Signs you need this

Most teams come to us after one of these moments. Recognise any of them?

Your keyword search returns irrelevant results and users give up

Support agents spend too long finding the right policy or procedure

You need answers that cite sources — not just guesses

Your documents go stale and the AI gives outdated answers

You've tried a basic RAG setup but accuracy is too low for production

Recognise two or more of these?

Let's talk - no commitment

30+

Products in production

“We treat every rag systems & knowledge ai engagement as a production commitment - not a prototype.”

- Thinkscoop Engineering

How we deliver

4 phases to production

Every engagement follows a structured delivery process with clear artifacts at each stage - so you always know exactly where you are.

Assess01

Retrieval Strategy

Chunking strategy documentEmbedding model selection reportIndex architecture designFreshness and re-indexing policy

Build02

Pipeline Implementation

Ingestion pipeline for all document typesVector store setup and optimisationCitation and source attribution frameworkPermission-aware retrieval layer

Evaluate03

Accuracy & Quality

Evaluation harness (RAGAS or custom)Hallucination rate baseline and thresholdsCoverage and recall metricsFailure mode catalogue

Deploy04

Production & Monitor

Production RAG deploymentFreshness scheduler and re-indexing automationMonitoring dashboard with quality metricsRegression test suite for ongoing changes

Questions

Straight answers

The questions we get asked most often. No marketing spin - just clear answers.

Ask us anything

What you get

Every deliverable, spelled out

RAG architecture with chunking and embedding strategy

Vector store setup and ingestion pipeline

Citation and source-attribution framework

Evaluation harness (RAGAS, custom metrics)

Freshness scheduler and re-indexing automation

Ready to get started?

RAG Systems & Knowledge AI starts with a 30-minute call.

No sales pitch. We'll scope your project, challenge assumptions, and tell you honestly if this is the right fit - before anything is signed.

Book a Discovery Call See relevant work