SERVICES

Everything it takes to put AI to work.

These capability areas are rarely needed one at a time, so we cover the span from the first design decision to the running system, and the review and training around it.

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01

AI Engineering & Application Development

We build the product, not a prototype that stalls at the demo.

Agents, retrieval systems, internal tools and the application around them, built to survive real users rather than to impress in a controlled run.

  • Agent and workflow design, including where a model should not be in the loop at all
  • Retrieval and data pipelines built against your real sources, not a sample set
  • Evaluation, fallbacks, cost control, and the admin surface someone has to operate it from
  • The full-stack application: the parts around the model that make it usable
02

AI Security & Governance

Autonomous systems act on their own. We make those actions attributable, permissioned and reviewable.

The controls that let you put an agent into a real business and still answer for what it did, designed before somebody asks you to account for a decision.

  • Agent identity and scope: which system acted, on whose behalf, and what it was allowed to touch
  • Policy and approval design, including which decisions a person must still sign
  • An evidence trail that holds up when auditors, a regulator or your own board asks how a decision was reached
  • Where relevant, this is where our Bulwark product fits into your architecture
03

AI Infrastructure & Deployment

Model serving that behaves like infrastructure: predictable latency, sane failure modes, a bill you can forecast.

The layer between your application and the models it depends on, hardened for the ordinary emergencies that take AI features down in production.

  • Inference architecture across hosted and self-hosted models, with routing and failover between them
  • Self-hosted open models on dedicated GPU infrastructure, including NVIDIA hardware, for workloads that need a controlled environment rather than a public API
  • Deployment, observability and capacity planning, so cost and latency are measured rather than guessed
  • Hardening for provider outages, timeouts, rate limits and retry storms
  • Where relevant, this is where our AI Agent Inference product fits in
04

AI Integration & Automation

Most of the value is in the connection, not the model.

We wire AI into the systems your business already runs on, and take the handoffs between people and software from implicit to explicit.

  • Integration with the estate you actually have: CRMs, finance and ticketing systems, data warehouses, internal APIs
  • Process automation where the human checkpoints are designed in, not bolted on
  • API development for the surfaces your own teams and tools need to call
  • Migration off the pilot, so what worked once runs on a schedule with someone accountable for it
05

Corporate Training & Enablement

Bring your own teams up to speed on building and operating AI safely.

Practical training tailored to your stack and your risk, for the engineers who build and the leaders who have to answer for it. Working sessions, not a slide deck we read to you.

  • Hands-on engineering workshops: agents, retrieval, evaluation, and the failure modes that bite in production
  • Governance and AI-risk training for leadership, in plain language tied to real decisions
  • Secure-AI practices: identity, permissions, oversight and evidence as everyday habits
  • Delivered against your own systems and use cases, so the team leaves able to apply it
HOW WE ENGAGE

Three ways to work with us.

Project delivery

A defined build with a clear outcome. We scope it, deliver it in short increments against a working environment, and hand over something your team can operate.

Embedded support

We work alongside your engineers in one specific area, inference architecture, agent governance or evaluation, filling a gap rather than replacing the team.

Advisory and review

A focused engagement to pressure-test an architecture, review a system before it goes live, or decide whether to build at all. Sometimes the useful answer is not yet.

HOW WE WORK

Five stages, and we will tell you if you should stop at the first one.

The order matters. Most of the expensive mistakes in AI projects are made before any code is written.

  1. 01

    Discover

    The workflow and the constraint first, not the model. What decision is being made, who owns it, what happens when it is wrong, and what a reviewer expects.

  2. 02

    Design

    Architecture, data flow, permissions and the human checkpoints. We name the failure modes up front and decide which ones the system must survive on its own.

  3. 03

    Build

    Engineering in short increments against a working environment, with your team in the loop. You see the thing running, not a status deck describing it.

  4. 04

    Validate

    Evaluation against your data, adversarial testing of the agent boundaries, and a review of behaviour under load and under failure.

  5. 05

    Deploy

    Into production with monitoring, an evidence trail and a runbook, and a plain account of what we would do next.

GET STARTED

Tell us what you are building.

One conversation is usually enough to know whether we can help.

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