AI Development for Production-Ready Products
Most AI prototypes stall between the demo and the release. We design and build the full product around the model — backend, data, interface and operations — so it holds up with real users.
The problem
A chat demo built in a weekend is easy. Handling bad inputs, latency, cost, permissions, logging and failure cases is where AI products usually break.
Many teams also split the work across an agency for design, a freelancer for the backend and someone else for the AI layer. Nobody owns the whole system.
The approach
We take responsibility for the complete technical path: product architecture, AI integration, backend, application and deployment.
Model calls sit behind a proper service layer with validation, retries, observability and cost controls, so you can change models without rewriting the product.
What we build
- LLM-powered features
- Summarisation, extraction, classification and generation built into your existing product.
- AI backends
- FastAPI or Laravel services that wrap model providers with queues, caching and usage tracking.
- Web and mobile front ends
- Next.js, Nuxt and Flutter interfaces designed around AI latency and uncertainty.
- Self-hosted inference
- Open-weight models on your own GPU or cloud when privacy or cost demands it.
Use cases
- Add AI extraction to an existing document workflow
- Turn an internal prototype into a multi-user product
- Move from a single model provider to a provider-agnostic layer
- Run an open-weight model privately for sensitive data
Typical architecture
Web / mobile app
API layer
AI service
Model providers or self-hosted LLM
PostgreSQL + Redis
Your business systems
Technology
- Python / FastAPI
- Laravel
- Next.js
- Nuxt
- Flutter
- PostgreSQL
- Redis
- Claude, Gemini and OpenAI APIs
- llama.cpp / vLLM
Related work
Process
- 01
Understand
Understand the business problem and the outcome you need.
- 02
Architect
Choose the AI architecture, integrations and technical approach.
- 03
Prototype
Validate the critical workflow before building around it.
- 04
Build
Develop the product in milestones you can review.
- 05
Validate
Test quality, performance, reliability and edge cases.
- 06
Launch
Deploy the production system and monitor it.
- 07
Improve
Optimise using real usage and feedback.
Questions
Do you work with existing codebases?
Yes. We often add an AI service alongside an existing Laravel, Node or Python product rather than rebuilding it.
Which model provider should we use?
It depends on quality, latency, cost and data requirements. We usually build a provider-agnostic layer so the choice can change later.
Who owns the code?
You do. Code lives in your repository and infrastructure accounts from the start.
Further reading
Tell us what you want to build.
Send a short brief. We'll reply personally with questions or a suggested next step — usually a technical discovery call.