AI that ships to production, not slideware.
From LLM integration to AI agents and computer vision, we design, build and run intelligent features with senior, AI-augmented engineers who own delivery from prototype to scale.
- Production-grade, not POCs
- Evals & guardrails built in
- Your data stays yours
Production-grade
Evals & guardrails
Your data stays yours
Every kind of AI capability.
One senior team across the full AI stack, from data and models to the product features users actually touch.
LLM integration
Chat, RAG search and copilots powered by GPT, Claude and open models, wired into your real data.
AI agents
Agentic workflows that plan, call tools and complete multi-step tasks with human-in-the-loop control.
Computer vision
Image classification, detection and OCR for inspection, document and quality-control use cases.
Predictive ML
Forecasting, scoring and recommendation models trained on your historical data to drive decisions.
MLOps & infra
Pipelines, model serving, monitoring and retraining so your AI stays reliable in production.
AI-powered features
Summarisation, classification and automation embedded directly into your existing product.
How we ship AI that earns trust.
Use case & data first
We start from the business outcome and the data you actually have, picking the simplest model and architecture that solves it, instead of chasing hype.
Evals & guardrails
We measure accuracy, hallucination and cost with real test sets, and add guardrails and human-in-the-loop checks so the AI behaves safely under real traffic.
Ship, monitor & improve
We deploy with MLOps pipelines, watch quality and cost in production, and keep retraining and tuning prompts as your data and users evolve.
A modern, proven AI stack.
AI solution questions, answered.
How fast can we get an AI feature into production?
A focused prototype usually lands in 3–4 weeks, with a production rollout following once evals pass. We agree the success metric and milestones in a short discovery call first.
Do you build with LLM APIs or open-source models?
Both. We choose based on accuracy, cost, latency and data sensitivity, from GPT and Claude to open models you can self-host when privacy or budget demands it.
How do you keep our data private and the AI safe?
Your data stays in your environment, never used to train third-party models. We add retrieval guardrails, evals and human-in-the-loop review to control hallucination and risk.
How do you stop the model from getting worse over time?
We deploy MLOps monitoring for quality, drift and cost, with retraining and prompt-tuning loops, so accuracy holds up as your data and usage change.
Have an AI use case in mind?
Tell us the outcome you're after, we'll propose the right model, architecture and a senior AI team to ship it.