What We Deliver
Practical AI that solves real business problems — not just demos.
We build AI-powered features and machine learning models for businesses that want to automate, personalise, or make better decisions at scale. From integrating GPT/Claude APIs into your product to training custom models on your proprietary data — we deliver working software, not slide decks.
Our AI work spans LLM integrations (OpenAI, Anthropic, Gemini), RAG systems for knowledge bases, recommendation engines, computer vision, predictive analytics, and intelligent process automation. We specialise in moving AI from prototype to production.
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How We Work
Our Process
Discover
We scope the business problem first — not the technology. We identify what data you have, what outcome you need, and whether AI is actually the right solution before any code is written.
Design
We build and test a prototype on a representative dataset. You see results and validate the approach before we productionise anything.
Build
We build the production system with proper data pipelines, model versioning, monitoring, and APIs your existing product can call.
Launch & Support
We monitor model performance, handle retraining schedules, and provide ongoing support as your data evolves.
Our Work
Recent AI & Machine Learning Projects
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Design and Development
FAQ
Common Questions
Do I need a large dataset to use machine learning?
Not necessarily. For many use cases, pre-trained models (GPT, Claude, vision transformers) can be fine-tuned or prompted effectively with small datasets. We assess what's available and recommend the most practical approach — which is often fine-tuning or RAG rather than training from scratch.
How much does an AI integration cost?
Integrating an existing LLM API into a product typically costs $5,000–$20,000 depending on scope. Custom model training and production ML systems are larger engagements starting from $20,000. We scope all projects before quoting.
Can you integrate AI into our existing product?
Yes. We've added AI features to existing web apps, mobile apps, and back-office tools. We expose AI functionality via APIs so your product can call it without a full rewrite.
How do you ensure AI outputs are accurate and safe?
We implement output validation, confidence thresholds, human-in-the-loop fallbacks, and prompt engineering guardrails. For regulated contexts we add audit logging and explainability. We don't ship AI features without appropriate safeguards.
Who owns the model and data after the project?
You do. All models, weights, training data pipelines, and code are transferred to you on project completion. We do not retain access to your data after handover.