How do you handle our data securely?

Your data stays in your infrastructure wherever possible; we design around data-residency and confidentiality constraints, and we're comfortable under NDA from the first call.

Build on APIs or train our own models?

APIs (Claude, GPT) win for most product features; custom or fine-tuned models pay off for specialized vision and domain tasks. The prototype phase answers this with data.

Which AI providers do you work with?

OpenAI, Anthropic, Google and open-source models. We choose per task on quality, cost and data rules, and design so you can switch later.

How do you keep running costs under control?

Model routing, caching and prompt optimisation, with a cost dashboard so spend is visible every week.

Can our engineers maintain it afterwards?

Yes. We pair with your team during the build and hand over documentation, evaluation sets and runbooks.

SolutionsAI Transformation

AI Transformation

Generative AI, vision, and MLOps, shipped into your product, properly.

Start with AI Transformation See the journey

Working prototype: 2 to 3 weeks
Production: 6 to 12 weeks
Engagement: Phased, milestone based

Who it's for

Product teams that want AI capabilities in production, not slideware, not perpetual pilots.

Product teams ready to ship AI features to real users
Companies stuck in pilots that never reach production
Businesses with valuable data and no way to use it
Teams needing vision, generative AI or model operations

The outcome

AI features that survive contact with real users: grounded, evaluated, monitored, and cost-controlled. From first prototype to automated retraining, your product gains capabilities competitors will still be demoing.

What you get

AI opportunity map and roadmap
Prototype tested on your own data
Production AI features with guardrails
Evaluation suite and quality monitoring
MLOps pipeline for retraining and releases
Cost and performance dashboards

What's inside

4 services. One team. One plan.

Each part is delivered by the specialists who run that service every day, coordinated under one lead so the pieces fit together from the start.

AI & Data

Generative AI Development

Build & deliver generative-AI solutions for adaptive design, content, image generation, and rapid prototyping, so your teams ship faster with less overhead.

Explore service

AI & Data

Computer Vision Solutions

Build detection, classification, and image-to-image pipelines for real use. Optimize inference for speed, accuracy, and cost on GPU/CUDA.

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AI & Data

MLOps & Model Deployment

Operationalize PyTorch models with containerized monitoring. Automate training and rollouts using Docker and GitHub Actions.

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AI & Data

AI ChatBot Development

Design and deploy LLM-powered chatbots tailored to your domain. Add RAG over your data, tool use, and analytics for fast, accurate answers.

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The journey

How AI Transformation comes together.

A clear sequence with something you can see at every stage, and a named lead accountable for the whole thing.

Opportunity mapping

Week 1

Workshop your product and data to find where AI creates real leverage, ranked by feasibility and impact, not hype.

Prototype against real data

Weeks 2 to 3

A working proof-of-concept in one to two weeks, evaluated on your actual data so the go/no-go is evidence-based.

Productionize

Weeks 4 to 9

Guardrails, evals, cost controls, and monitoring turn the prototype into a feature you can put in front of customers.

MLOps foundation

Weeks 8 to 11

Deployment pipelines, versioning, and drift monitoring so models improve safely instead of decaying quietly.

Expand the roadmap

Ongoing

With the first capability live, the next items on the map get cheaper, infrastructure and learnings carry forward.

Outcomes

What changes for you.

FAQs

Before you ask.

Ask us something else

Yes, that's the point of opportunity mapping. If the honest answer is 'not yet' or 'not there', you'll hear it, with reasoning. Failed pilots cost more than honest maps.