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SOLUTION

AI Agent & RAG Development

Production AI agents and retrieval-augmented generation (RAG) applications that answer from your own data, act through controlled tools and keep people in charge of consequential decisions.

All solutions
THE OPPORTUNITY

Built around your operation.

We design and build AI agents and RAG applications that work inside real business systems: assistants that answer from approved knowledge, agents that draft, classify or route work, and copilots embedded in internal tools. Retrieval, permissions, evaluation and monitoring are engineered as part of the product, not added after a demo.

Why it matters

A language model on its own does not know your policies, contracts or customer records, and it can produce confident but unsupported answers. Grounding responses in retrieved sources, limiting what an agent is allowed to do and testing behaviour against real cases is what separates a dependable production system from a prototype nobody trusts.

Core scope

  • RAG assistants and search over your documents and data
  • Tool-using AI agents with scoped permissions
  • Evaluation, guardrails and human review
HOW WE DELIVER

A deliberate approach.

  1. 01

    Select one workflow, define success criteria and build an evaluation set from authorised, representative examples

  2. 02

    Design ingestion, chunking, embeddings, hybrid search and permission-aware retrieval for your sources

  3. 03

    Implement agent tools, approval steps, guardrails and fallbacks around consequential actions

  4. 04

    Launch with tracing, quality and cost monitoring, and re-run evaluations whenever models, prompts or data change

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ILLUSTRATIVE SYSTEM / CONNECTED DELIVERY LAYERS

TECHNICAL FOUNDATIONS

Tools for the work.

  • OpenAI API
  • Anthropic Claude
  • LangChain
  • Pinecone
  • PostgreSQL
  • Redis
  • Python
  • TypeScript
DESIGN PRIORITIES

What we design toward.

  • Answers grounded in cited sources
  • Agent actions that are permissioned and auditable
  • Measured quality before and after launch
WHERE IT FITS

Typical use cases.

Common situations where teams bring us in for this work.

  1. 01

    Internal knowledge assistant

    Answer staff questions from policies, SOPs, product documentation and resolved tickets, linking to the source passages and respecting existing document permissions.

  2. 02

    Customer support drafting

    Draft replies from order, account and knowledge-base records for a support agent to review, edit and send, with rules that block unsupported promises.

  3. 03

    Document intake and extraction

    Classify incoming invoices, applications or contracts, extract structured fields and route low-confidence cases to a person instead of guessing.

  4. 04

    Sales and operations copilots

    Summarise account history, prepare meeting briefs or update CRM records through approved tools rather than broad access to your systems.

COMMON QUESTIONS

Questions buyers ask.

Straight answers about scope, risk and ownership before you start a project.

01

What is the difference between a RAG application and an AI agent?

A RAG (retrieval-augmented generation) application retrieves relevant passages from your own content and passes them to a language model so it can answer with that context. An AI agent goes further: it can decide to call tools, such as searching a database, creating a ticket or updating a record, over several steps. Many production systems combine both, using retrieval for grounding and a small set of permissioned tools for action.

02

Can an AI agent safely take actions in our systems?

Yes, when the design limits what it can do. We give agents narrowly scoped tools instead of broad credentials, validate inputs and outputs in ordinary code, require human approval for consequential or irreversible actions and log every tool call. Instructions found inside documents or messages are treated as untrusted content, because prompt injection is a known risk for LLM applications.

03

How do you measure whether a RAG system is accurate?

Before launch we build an evaluation set of representative questions with expected answers and sources. We test retrieval (did it find the right passages?) separately from generation (did the answer stay faithful to them?), include questions the system should decline, and keep a held-out set so tuning does not simply fit familiar examples. The same tests run again whenever the model, prompts or index change.

04

Which models do you use, and can our data stay private?

We choose models per use case, including OpenAI and Anthropic models through their APIs, based on quality, latency, cost and data-handling terms. We minimise the data sent to any provider, review retention settings, keep access control inside your application and can design for self-hosted open-weight models where your policy requires it.

05

How long does a first production release take?

It depends on data readiness and integrations, so we scope it after discovery rather than quote a generic timeline. A focused first release covers one workflow, one set of sources and an evaluation baseline, then expands once quality holds in real use.

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LET'S BUILD TOGETHER

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