A complete AI product, not a prototype you still have to productise
Backend APIs, authentication, multi-tenancy, usage metering, billing hooks, admin dashboards, database design and cloud deployment in one engagement. You ship a product your customers can pay for, and you own every line of it.
The model was the easy part. The product around it is where the months go
Teams get a convincing prototype working in a notebook in a fortnight, then spend two quarters discovering what it takes to charge money for it: tenant isolation, per-seat usage limits, streaming responses that survive a dropped connection, prompt versioning, cost attribution, admin tooling and an audit trail.
We build that layer as a matter of course, because it is the part that determines whether your AI product is a line item or a business. The model provider becomes a swappable dependency rather than the architecture.
The same applies to internal copilots. A support or sales copilot that nobody adopts usually failed on permissions, latency and workflow fit, not on answer quality.
Why these projects fail
The gap between a working demo and a sellable product is where most AI products stall.
From scope to a product in the hands of users
Working software from sprint two, with the commercial mechanics built in rather than retrofitted.
Product and data modelling
User roles, tenant boundaries, entitlements and the data model, agreed before any code. This is what makes multi-tenancy safe rather than hopeful.
Backend and API layer
FastAPI services with authentication, rate limiting, streaming responses, background jobs and a clean OpenAPI contract your frontend or customers can build against.
Model gateway
A provider-agnostic layer with routing, fallback, caching, prompt versioning and per-request cost capture, so switching or mixing models is configuration rather than surgery.
Application interface
React or Streamlit interfaces for end users plus an admin surface for your team: tenants, usage, limits, prompts and system health.
Usage, limits and billing hooks
Per-tenant metering on tokens, requests and features, with quota enforcement and export into Stripe or your existing billing system.
Deployment and hardening
Containerised deployment on AWS, Azure or GCP with CI/CD, secrets management, logging, backup and a load-tested path to your expected volume.
Handover and support
Documented architecture, runbooks and a walkthrough with your engineers, followed by post-launch support and quarterly improvement cycles.
The layers between your customer and the model
The two highlighted bands are the ones teams skip in a prototype and then rebuild under pressure: the gateway that makes multi-tenancy safe, and the model layer that keeps providers swappable and cost visible.
What lands in your repository
A product you can operate, sell and hand to a new engineer without a translation layer.
Tools we build this on
Chosen per project against your data, latency budget and compliance requirements, not out of habit.
Backend and Frontend
Data and Storage
Models and Orchestration
Deployment
AI products we have shipped end to end
Numbers from live production systems, not projections.
A HIPAA-compliant clinical platform for a hospital network
Role-based access across every clinical and administrative role, a full audit trail on every response and a zero-downtime SLA, cutting manual clinical documentation 48%.
RegTech · Germany and EUA compliance platform reporting in three languages
Regulatory monitoring, gap analysis and board-ready reporting in English, German and French across eight jurisdictions, with an LLMOps governance layer keeping decisions auditable.
Retail · USAA dual-layer personalisation and forecasting platform
Real-time recommendations and demand prediction through AWS Bedrock under 100ms, lifting conversion 14% and demand forecasting accuracy 62%.
Product questions we answer on most first calls
What clients ask before starting a project like this.
Do we own the code?
Yes, completely. Every repository, pipeline and deployment configuration is yours, in your accounts, from the first commit. There is no licensed platform underneath and no dependency on us to keep running.
How fast can we get a sellable version in front of customers?
A focused first release typically takes 8 to 12 weeks including authentication, multi-tenancy, metering and deployment. You see working software from sprint two and can put it in front of design partners well before launch.
Can you build on top of the stack we already use?
Yes. We work inside your existing repositories, cloud accounts, CI and design system where they exist. Where they do not, we set them up in a way your team can take over.
How do you handle model costs at scale?
The model gateway captures cost per request and per tenant from day one, and routes simple calls to smaller models with caching where responses repeat. That is how one deployment reached 60% lower inference cost than proprietary API defaults.
Can the whole product run inside our own cloud account?
Yes, including the model layer. For clients with data residency requirements we deploy open-weight models inside the customer VPC so no request leaves the environment.
Have an AI feature that needs to become a product?
Bring the use case and whatever you have built so far. We will map the architecture, the commercial mechanics and a realistic path to launch on the call.