We join a small number of teams as a hands-on partner. The work is feasibility, prototypes, architecture and software that ships.
AI products. Built, not PowerPointed.
01
AI feasibility and product discovery
We help you judge a problem before anyone builds a platform around it. That covers the model options, the data you would need, the integration, the risks and what it would cost to run. A proof of concept is part of the work when a small build would answer the question. Sometimes the useful conclusion is that AI should stay out.
Technical feasibility for the problem you actually have.
A plain comparison of models and providers.
Constraints, risks and the data involved.
What integration would ask of the current system.
A realistic view of operating cost and complexity.
A proof of concept when code would teach more than a memo.
02
AI product engineering
This is implementation work. We put a model into a real workflow, decide what happens when the output is wrong and keep the product from depending on a single provider. Retrieval, structured output and tool use belong here when they serve the task. So do evaluation and a plan for watching the feature once people are using it.
Language-model integration.
An architecture that can change provider.
Workflows with a defined job.
Retrieval, structured output and tool use where they help.
Evaluation, failure handling and operational visibility.
Deployment thought through before a demo hardens into the product.
03
Rapid prototyping
We build a narrow prototype around one real workflow. It should be good enough to try, and small enough to change or set aside. You get an early read on the technology and a straight recommendation about what to do next, including stopping.
One workflow, built well enough to try.
Technical questions answered in code.
A short loop between what you see and what changes.
Little extra machinery.
A clear recommendation for the next step.
04
Software architecture and engineering
We help teams shape a system and then build it: APIs, data, web applications, backend services, tests and the operational basics. Day to day that includes TypeScript and Node.js, React, C# and .NET, PostgreSQL and SQL Server, cloud deployment, OpenTelemetry, OpenFeature and integration work. We were part of the OpenFeature governance board. Fintech and payments are part of the background. The engagement is still about the problem in front of you.
Architecture another engineer can follow.
APIs and backend systems.
Web applications.
Testing and automation.
Observability and integration.
Room for the system to change after launch.
How an engagement tends to go
A useful order of work, not a process every project has to march through. A feasibility question may end once we know enough.
01
Understand
Clarify the problem, the constraints and what a good result looks like.
02
Validate
Test the approach with research or a focused prototype.
03
Build
Develop the software with the testing and structure it needs to last.
04
Improve
Learn from real use and refine the work.
Who it is for
Product teams, startups, scaleups and fintech companies. Also businesses that want a practical answer on where AI belongs. You do not need an engineering background to write. The conversation gets specific once we are in it.
Consulting and contract engineering are remote, and we take on a small amount at a time. We don't publish rates, packages or a promise of spare capacity. Write with the problem and we will say whether we can help.
