AI Engineering & Deployment

Useful agents.
Inside your workflow.

Build agents around your tools, repositories and knowledge. Deploy into your environment, then measure how well they do the work.

Discuss an AI project

01Engineering capability

From context to action.
With the infrastructure behind it.

Agents & tool integration

Agents that work across selected repositories, documentation, tickets and internal systems. We build the software and integrations needed to retrieve context and act within agreed permissions.

Agent development · retrieval · APIs · workflow integration

Execution infrastructure

The tooling around the agent: isolated execution, permission boundaries, state, retries and traceable activity. Human review stays where the workflow needs it.

Agent harnesses · tool execution · approval points · observability

Evaluation & deployment

Evaluate representative tasks, understand failures and deploy into an agreed test or production environment. Refine behavior using evidence from real use.

Task evaluations · release checks · deployment · operational improvement

02Working alongside your team

A delivery engagement.
From first task to real use.

Define the task

Understand the people, systems and constraints. Select a real workflow and agree how its current performance will be measured.

Build in context

Work with your team to integrate tools, develop the agent and engineer the supporting software. Agree action boundaries before enabling them.

Deploy & observe

Evaluate the system, then deploy with the access, monitoring and rollback controls your environment requires.

Improve against evidence

Measure successful completion, time to reviewed results, intervention, rework and cost. Use those observations to decide the next iteration.

What stays with your team

The agreed source code and integrations, evaluation cases, deployment configuration, operating guidance and a practical handover.

03Deployment choices

Fit the environment.
Respect the data boundaries.

Model selection, access controls, data handling and operating constraints are part of the engagement. Private hosting alone does not establish security.

Approved hosted models

Use hosted model services within agreed data-handling policies. Define what can leave your environment, which tools the agent can use and how activity is recorded.

Private cloud

Deploy into your cloud environment with its identity, networking, storage and operational controls. Evaluate model and infrastructure choices against the task.

On-premises & offline requirements

Scope operation within your own infrastructure. Where offline use is required, validate model capability, hardware, dependencies and update procedures before committing to the design.

Start with your system

Which workflow should
work better?

Start with the task, the tools your team uses and the outcome you want to change.

Scope an AI engagement