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.

You do. Code lives in your repository and infrastructure accounts from the start.

Services / AI development

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.

Discuss your AI project

The problem

01 — Context

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

02 — Method

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

03 — Capability

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

04 — Applications

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

05 — System

Web / mobile app

API layer

AI service

Model providers or self-hosted LLM

PostgreSQL + Redis

Your business systems

Python / FastAPILaravelNext.jsNuxtFlutterPostgreSQLRedisClaude/Gemini/OpenAI APIsllama.cpp / vLLM

Process

06 — Delivery

Understand

Understand the business problem and the outcome you need.

Architect

Choose the AI architecture, integrations and technical approach.

Prototype

Validate the critical workflow before building around it.

Build

Develop the product in milestones you can review.

Validate

Test quality, performance, reliability and edge cases.

Launch

Deploy the production system and monitor it.

Improve

Optimise using real usage and feedback.

Questions

07 — FAQ

Start here

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.