From a promising prompt to an AI product people rely on.
End to end development of AI powered products, from ideation and model selection through deployment and monitoring.
We take AI products and AI apps from a rough idea to a live, monitored system. That means choosing the right models, designing an API first architecture around them, and standing up the evaluation and MLOps needed to keep quality high once real users arrive.
A short, evidence based answer to whether AI can do the job well enough, before you fund a build.
Frontier APIs, open weight models or fine tuning, chosen on measured quality, cost and privacy.
The product around the model, including APIs, retrieval, guardrails and UI, built so models can be swapped without a rewrite.
The plumbing that keeps quality high after launch, like evals, tracing, versioning and cost controls.
We don't bolt AI on at the end of a project. It's part of how we research, design, build, test and document every engagement, which is how a small senior team moves quickly without cutting corners. A person reviews everything the models produce.
We prototype on your real samples in the first sprint and score every model or prompt change against an evaluation set, so the go / no-go rests on evidence.
With your permission, briefs, tickets, call notes and analytics exports go into a private workspace. LLMs map requirements, edge cases and open questions, so nothing said in a workshop gets lost. People run the workshops; the model keeps the record.
First-pass screens, flows and copy variants are generated from the spec, then shaped by our designers. Decisions happen on a clickable prototype instead of a document, and the final design system stays human-made.
Engineers work with AI coding assistants for scaffolding, tests and refactors. Every change is read by a person and scanned by Caudilysis, our own static analysis platform, before it merges.
AI features get an evaluation set built from real examples, and every model or prompt change is scored against it. Everything else gets generated test cases that an engineer reviews and keeps in the pipeline.
Documentation, runbooks and release notes are drafted with AI and checked by the team. After launch, monitoring and usage data feed straight into the next iteration.
The indicative shape of a first engagement. Phases overlap on purpose, so we start building while discovery is still sharpening the plan.
This is indicative only. Every engagement is scoped with you before it starts.
You have a use case that works in a demo but not yet in production.
You need evidence on model choice, cost and privacy before committing.
You want your team to own and run the system after handover.
Not sure it fits? A free assessment will tell you
# Service manifest service: AI Product Development category: AI & Automation deliverables: • AI feasibility assessment • Model selection & training • API first AI product architecture • MLOps pipeline setup stack: • Claude API • LangChain • Vector Databases • Python • LLM Orchestration • Model Fine tuning phases: 4 next_step: free assessment
AI app development is building a web or mobile application where a model does core work, such as answering questions over your data, drafting content or making predictions. On top of normal app engineering it adds model selection, retrieval, evaluation, guardrails and monitoring, which is where most of the risk and effort sits.
We are model agnostic. That covers frontier APIs such as Claude, open weight models you can self host, and fine tuned variants. The choice comes from benchmarking on your task, weighing quality, latency, cost and data residency needs.
We build an evaluation set from real examples early in the project and score every prompt, model or retrieval change against it. That gives you a quality number that moves with the product instead of a gut feel.
Yes. We design for your constraints, whether that means zero retention API settings, private deployments of open weight models or retrieval over data that never leaves your cloud, and we document exactly where data flows.
Tell us what you're building, improving or automating. We'll come back with an honest view of where AI product development fits and the fastest path to production.
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