What we build
Agents. Connected data.
Intelligent workflows.
Commission an AI application, connect the information it needs, or engineer the workflow around its output. We assess new capabilities before committing to implementation.
Three areas of
applied AI engineering.
Project scopes to explore with us. We separate what IMFIT demonstrates from capabilities that need new feasibility work, implementation and evaluation.
Give software a task.
Define its limits.
Scope assistants and agents that interpret information and use permitted tools. Establish when a fixed workflow is sufficient, what needs approval and how results will be evaluated.
New agent work begins with feasibility. IMFIT does not establish a deployed autonomous agent.
Data integrationConnect information
with its context.
Bring records, documents and existing software into an AI application through APIs, retrieval or MCP where appropriate. Define access, source ownership and update behavior.
IMFIT evidence: approved knowledge, versions and source references. MCP delivery is not demonstrated.
Intelligent workflowsTurn interpretation
into controlled action.
Combine model output, business rules, application state and human review. Agree how interrupted work, failures and handovers should behave.
IMFIT evidence: stale-response checks and staff-takeover controls. Each new workflow needs its own tests.
Start with one task.
Leave with a build decision.
Bring an example of the work, the systems involved and the person responsible for the result. We first discuss fit, then agree the scope and fee for discovery.
- What you receive
- A system assessment, proposed implementation, acceptance cases, dependencies and a priced next step.
- What we check
- Whether existing tools already solve it, whether the data is usable, and whether a simpler workflow is sufficient.
- When to continue
- Proceed only when the task, access, ownership and evaluation criteria are clear. A new agent or model experiment must earn its next stage.
A clear scope.
A reviewable result.
Work directly with the founder, from the first technical review through implementation and handover. Scope and availability are agreed individually.
- 1 / Understand
Inspect the existing system.
Review the task, current tools, sample data and access. Recommend configuration when it is enough.
- 2 / Define
Agree what “done” means.
Set the deliverables, exclusions, acceptance cases, fees and milestones before implementation.
- 3 / Build
Review working software.
Develop a bounded feature, demonstrate progress and test both useful outputs and failure cases.
- 4 / Handover
Leave an operable system.
Deliver agreed source code, configuration, test evidence and operating notes. Define ongoing support separately.
Before you commission the work.
Do we need to replace our existing application?
We assess your current tools and data first. The scope may be an integration, a feature in existing software or a new application. A replacement is considered only when the requirements justify it.
How are projects priced?
Discovery is scoped and priced first. Implementation fees follow the agreed technical scope, dependencies and acceptance criteria. There is no universal subscription or revenue-share fee. Changes and ongoing support are agreed separately.
What happens to the code and running costs?
The proposal states which project source code and configuration you receive and the terms for existing components. Hosting, model, messaging and other vendor costs are identified separately. Ownership and licensing terms are agreed before work begins.
What if an existing tool already solves it?
The recommendation can be configuration or an existing product. A discovery does not require you to commission custom development.
Scoped AI engineering
Have a task in mind?
Let’s assess the fit.
Start with the work, the information it needs and the question you want to answer.