Solution design
Architecture, model and tooling choices with the reasoning, data flows and integration points.
AI Services
AI Application Development
Build and ship custom AI applications and integrations — scoped tightly, tested against real data, documented for whoever maintains them.
What it is
When no product does what you need, we build it. Typically that is an application that combines a language model with your own data and a workflow — a document processing pipeline, an internal assistant grounded in your content, a classification or triage service behind an existing queue.
We scope narrowly on purpose. A well-defined application that handles one workflow properly beats a platform ambition that never reaches production. Evaluation is part of the build, not an afterthought: we agree accuracy and failure criteria before writing code, and we test against your real, messy data.
Who it is for
- Organizations with a specific workflow no off-the-shelf product fits
- Teams with a promising prototype that needs to become supportable software
- Companies needing an AI capability integrated into existing systems
- Leaders who want an application their own team can maintain afterward
What is included
The deliverables below are the default scope. We adjust them to your situation during scoping and confirm the final list in writing.
Evaluation criteria
Accuracy, latency, cost and failure-handling targets agreed before the build starts.
Working application
Built, tested against representative real data, and deployed to your environment.
Human oversight design
Where a person reviews, overrides or is notified, and how corrections feed back.
Security and privacy review package
Data handling, retention, access control and logging, documented for your reviewers.
Handover package
Source, deployment instructions, runbook, evaluation harness and known limitations.
Process and timeline
Indicative timings for a typical engagement of this type. Your scope, environment and availability of stakeholders will move them.
-
Scope and design
Requirements, architecture, evaluation criteria and a clear definition of done.
-
Prototype
A narrow slice against real data, to prove the approach before full build.
-
Build
Iterative build with regular demos and continuous evaluation.
-
Harden and review
Security review, performance work, error handling and documentation.
-
Deploy and hand over
Production deployment, monitoring, hypercare and handover to your team.
Outcomes we aim for
These are the goals we design the engagement around. They are objectives, not guarantees, and the result depends on decisions and effort on your side as much as ours.
- A working application in production rather than a prototype in a notebook
- Measured accuracy against criteria set before the build
- Documentation and a runbook that make internal ownership realistic
- An evaluation harness you can re-run when models or data change
Questions
About ai application development
Which models do you build on?
We choose per project based on your data sensitivity, hosting constraints, latency needs and cost. We are not tied to one provider, and model choice is documented as a reversible decision wherever we can keep it that way.
Who owns the code?
You do, on delivery, along with the documentation and evaluation harness. We will confirm this in the engagement agreement.
Can you take over an existing prototype?
Often, yes. We start with a short assessment of whether hardening it is cheaper than rebuilding, and we tell you honestly which one it is.
Is ai application development the right next step?
Tell us the symptom rather than the solution. If a different service fits better — or none of ours does — we will say so.