A product is what you can run after we leave.
The gap between a working AI demo and a working AI product is wider than most teams expect. The demo proves feasibility. The product survives real users, real load, real edge cases, and real change over time. That gap is where most AI projects stall.
We close it deliberately. Every AI product we build ships with the architecture, evaluation, and operational scaffolding that production requires. Not as a hardening phase tacked on at the end. As the way we build from week one. The eval harness exists before the first feature does. The monitoring is wired before deployment. The fallback behaviour is designed into the user experience, not bolted on after an incident.
The result is a product your team can operate after we hand over. Not a prototype that needs another six months of work to be safe to ship. Not a black box only the build team understands. A working AI feature in your stack, with the artifacts to keep it running.
End-to-end AI builds across the patterns that work in production
Every product we build is shaped by the AI patterns that actually survive production. Selected for fit, designed for operability.
AI-native applications
Web and mobile applications where AI is the core value proposition. Conversational interfaces, copilots, generation, classification, summarisation. User experience designed around model behaviour, not despite it.
AI-powered features
A new AI capability inside an existing product. Search that understands intent, copilot inside a workflow, intelligent triage on inbound work. Designed to slot into your stack without rewriting it.
Knowledge and document AI
Production RAG over your knowledge base, document understanding, structured extraction, contract analysis, support deflection. Grounding evaluation, citation discipline, and refresh tooling included.
Agentic systems
Bounded agents that complete multi-step tasks, call tools, and act on behalf of users. Designed with audit trails, deterministic fallbacks, and human checkpoints where stakes are high.
Generative interfaces
Image, text, and structured-content generation with user-facing controls, safety review, and brand and tone consistency built in.
Production hardening
AI products built by another team that need eval coverage, monitoring, or reliability work before they can be trusted at scale. We pick up the build and ship the operational layer.
Discovery, build, ship, hand over
Productized phases with named deliverables. You see what each phase produces before you commit to the next.
One to two weeks. Product framing, data assessment, model and architecture selection, eval methodology, scoped delivery plan. You finish with the build plan and a clear go or no-go.
Four to eight weeks. Working AI product in your stack, integrated with your auth, data, and existing systems. Eval harness, monitoring, and fallback paths in place. Weekly demos, shared backlog.
One to two weeks. Production rollout, observability wiring, alert thresholds calibrated against real traffic, runbook walk-through, baseline measurement against the eval harness.
Full handover with architecture docs, ADRs, eval methodology, and runbooks. Or a retainer where the team that built it keeps iterating alongside yours so the patterns transfer.
A product, not a prototype
Every engagement produces the same set of artifacts. The shape is consistent so your team knows exactly what they get.
Productized AI delivery, not bespoke consulting cycles
A small focused team, named deliverables, transparent scope, and AI-first DNA. The model is the differentiator.
Production from week one
Eval harness exists before the first feature does. Monitoring is wired before deployment. Fallback behaviour is designed into the user experience. Production-ready is not a phase we add at the end. It is how we build.
AI patterns by default
Prompt and retrieval architecture, latency budgets, cost ceilings, evaluation methodology, drift detection, agent boundaries, human-in-the-loop checkpoints. These are the floor of every project, not skills we have to recruit for.
Senior team, no handoffs
A small senior team owns design, engineering, and delivery together. Decisions move fast because the people making them are also the people building.
Transparent partnership
Weekly demos, shared backlog, acceptance criteria written down before the work starts, decision logs maintained throughout. You always know where we are and what is shipping next.
Where to look next
Deeper detail on the engagements and approach behind this work.
Common questions
How is an AI product development agency different from a software agency that does AI?
A software agency typically built their model around web and mobile work and added AI as a service line. The team structure, delivery cadence, and engagement size reflect that origin. An AI product development agency is calibrated around AI delivery patterns from day one. Eval methodology, prompt and retrieval architecture, production monitoring, and AI risk patterns are how we work, not capabilities we had to bolt on.
How long does it take to ship a working AI product?
A working AI prototype in two to four weeks. A production-ready AI product in eight to twelve weeks for most builds. Complexity, integration scope, and regulatory requirements can push the timeline; we tell you the realistic shape during Discovery before you commit to a build.
Do you build the AI yourself or use existing models?
Both, depending on what fits. Most production AI products are built on top of frontier model APIs or open-weight models with retrieval, prompting, and orchestration layers around them. Custom model training is occasionally the right call but rarely the first answer. We choose what is best for the product, not what is most interesting technically.
What if our project needs a long-term engineering partner, not just a build?
After the build engagement, you can transition to a retainer where the team that built it keeps iterating alongside yours. Or you can take full handover with documented architecture and runbooks and run it in-house. The choice is yours.
Do you publish pricing?
No. Every AI product is bespoke and the scope drives the cost. Our packages publish week-by-week cadence, named deliverables, and exit ramps so you can compare scope transparently. Pricing is set per project against the agreed scope.
Can we start with a small engagement to see if it works?
Yes. The Discovery Sprint exists for exactly that. One to two weeks, scoped delivery plan at the end, and a clear decision point before any larger build. If we are not the right fit, we will say so on the first call.
Ready to ship a working AI product?
Book a free 15-minute feasibility triage. We will scope what your AI product actually looks like and give you an honest read on timing and shape.