AI · Service 03

Generative AI.

RAG, agents, copilots and content systems — engineered for production with evaluation, guardrails and observability from day one. No demo-ware.

6–12 wks
First agent in production
40–60%
Avg. handle-time reduction
0.82+
Typical answer-quality score
10×
Cost reduction vs naive RAG
Overview

Agents and RAG, done properly.

A GenAI prototype takes a weekend. A GenAI product takes retrieval quality, evaluation, safety, observability, cost control, integration and change management. We ship the product.

We've built agents for claims triage, tenancy queries, audit evidence, tax enquiries and procurement — each live in production, with human-in-the-loop where stakes require it.

Where we help.

01

LLM selection & evaluation

Bake-off across frontier and small open models on your ground truth. Commercials, latency, safety, quality — all on the same sheet.

02

RAG & knowledge-base design

Chunking strategy, hybrid retrieval, re-ranking, citations, freshness, access controls. Not a vector-DB demo.

03

Agents & workflows

Tool-using agents with typed schemas, deterministic guard-rails, retries, fallback paths, cost budgets per call.

04

Copilots & UX

In-product copilots with the right affordances for trust: citations, edits, undo, audit trail, feedback loop.

05

Safety, evals & observability

Offline eval harness, online quality monitoring, red-team suite, prompt versioning, cost/latency dashboards.

06

Fine-tuning & distillation

Where a small specialised model beats a frontier API on cost, latency and quality — we build it.

How we work

A production-first delivery path.

1
Week 1

Scope & evals

Define tasks, collect ground truth, build offline eval harness before a single prompt is written.

2
Weeks 2–3

Retrieval & baseline

Data ingestion, retrieval bake-off, prompt v1 against evals.

3
Weeks 4–8

Agent build

Tools, guardrails, UX, integration. Fortnightly demos against evals.

4
Weeks 9–10

Hardening & launch

Red-team, load test, observability, rollout plan, training.

Deliverables

  • Production agentIn your cloud, integrated with your systems, monitored.
  • Evaluation harnessOffline + online, gated to CI/CD.
  • Retrieval layerIngestion, chunking, hybrid search, access control.
  • Safety & red-team reportKnown failure modes, mitigations, residual risks.
  • Cost & quality dashboardsLangfuse / equivalent, per-session unit economics.
  • Operator runbook & trainingYour team ready to own and extend.
Technology

Tools & frameworks we use.

Claude
OpenAI GPT-4o / o-series
Azure OpenAI
AWS Bedrock
Gemini
LangChain
LangGraph
LlamaIndex
Pinecone
Weaviate
pgvector
Langfuse
Ragas
Guardrails AI
In production

A real engagement.

Case study

City council — AI-assisted FOI response drafting.

A RAG agent drafts Freedom-of-Information responses grounded in council records, routes to the right reviewer, and tracks deadline risk. 58% of responses now drafted in < 2 minutes; backlog eliminated.

Read full case study
58%
Responses auto-drafted
−71%
Avg. handling time
0
Missed statutory deadlines
14 wks
Scope to live
FAQ

Common questions.

Which model should we use?+
Depends on task, data sensitivity, latency budget and commercials. We run a structured bake-off on your data — we're partner-certified with Microsoft (Azure OpenAI), AWS (Bedrock), Google (Vertex) and deploy open models where appropriate.
How do you prevent hallucinations?+
Retrieval with citations, structured outputs, schema validation, evaluation harnesses, human review on high-stakes actions. We engineer for it, we don't hope for it.
Can we host everything on-prem or in our tenant?+
Yes. For regulated workloads we deploy open models (Llama, Mistral, Qwen) in your cloud or on-prem with vLLM / TGI.
What about data protection / DPIA?+
We run a DPIA workshop early and deliver the artefact as part of the engagement. Our work has been reviewed by ICO-regulated clients.
Ready when you are

Put your data to work.

Book a free 30-minute consultation with a senior Databuzz consultant.