AI engineering

AI agents and copilots, grounded in your business

We build production AI that does real work: agents that use your tools, copilots grounded in your data, and retrieval systems you can trust, on Azure OpenAI, inside your own tenant.

Start with a use case
AI
Why teams work with us

AI that ships, not AI that demos

Anyone can wire up a chatbot. We build AI systems that run in production, grounded in your data, connected to your tools, and safe to put in front of users.

Grounded in your data

Retrieval-augmented generation answers from your documents, wikis and databases, with citations, not made-up confidence.

Agents that take action

Not just chat. Agents plan, call your tools and APIs, and complete real multi-step work end to end.

Private and governed

Run on Azure OpenAI inside your own tenant, with evals and guardrails so you can trust what goes out.

The AI stack

Model-agnostic, built on the best tools

We pick the right model and tooling per task, GPT, Claude and Gemini, orchestrated with retrieval, vector search and the Model Context Protocol.

What we build

From copilots to autonomous agents

The patterns that are actually delivering value right now, applied to your business.

Agentic workflows

Autonomous agents that plan, use tools and complete multi-step processes like triage, research and scheduling, with a human in the loop where it matters.

Copilots & assistants

In-product copilots grounded in your docs and data that answer questions and take actions for staff and customers, 24/7.

RAG over your knowledge

Search and answer over your documents, wikis and databases with grounded, cited responses your team can trust.

Tool & system integration

Connect models to your real systems with the Model Context Protocol and secure APIs, so AI can read and act, not just chat.

Document processing

Extract, classify and summarise document-heavy workflows, from invoices to contracts, at a fraction of the manual cost.

Evals & guardrails

Test, monitor and constrain model behaviour so quality is measured and bad outputs are caught before users see them.

Lives in your stack

Agents that plug into the tools you already use

Through MCP and native connectors, our agents read and act across the systems your team works in every day.

We even built our own AI platform: Siesta AI

The platform where you can:

  • Connect and organize all your data & tools
  • Build AI assistants and agents tailored to your workflows
  • Deploy securely and stay in full control
Explore Siesta AI
Siesta AI platform
How we deliver

From use case to production in weeks

We start narrow, prove value on a real workflow, then harden and scale, no year-long science projects.

1
Discover

We find the highest-value use case and define what good looks like, with measurable success criteria.

2
Prototype

A working prototype on your real data in weeks, so you can feel the value before committing further.

3
Harden

We add retrieval, guardrails, evals and monitoring, and integrate securely into your systems.

4
Scale

We roll out to production, measure impact, and expand to the next workflow.

FAQ

The questions everyone asks

We're model-agnostic, GPT, Claude and Gemini, via Azure OpenAI or direct APIs. We pick the right model per task on quality, latency and cost, and can run everything privately in your Azure tenant.

No. With Azure OpenAI your prompts and data stay in your tenant and aren't used to train foundation models. We design for data residency, privacy and access control from the start.

Retrieval-augmented generation grounds answers in your own sources with citations, and we add evals and guardrails that measure quality and constrain bad outputs before users ever see them.

No. Most of the value comes from connecting models to the data and tools you already have. We start with one focused use case and expand from there.

Ready to put AI to work on something real?

Bring a workflow that's slow, expensive or repetitive. We'll show you what agents can do with it.

Start with a use case