A demo is easy. A dependable AI system is not.
Most AI projects stall between a promising notebook and something people can rely on every day. The model is only one layer — the data feeding it, the service wrapping it, the interface using it and the monitoring watching it all decide whether it works.
We build every one of those layers as one team. Before anything ships we agree what "good enough" means, measure against it, and keep measuring after launch so quality is proven rather than assumed.
- Custom ML model training & deployment
- Large language model fine-tuning
- RAG systems connecting AI to your data
- Multi-step agentic AI workflows
- AI-powered search & recommendations
- Computer vision & NLP pipelines
AI Solutions services
Pick one piece or the whole stack — every engagement is scoped to what your product needs.
Custom ML models
Classification, prediction and recommendation models trained on your data and evaluated against a clear baseline.
LLM fine-tuning
Adapt open and commercial language models to your domain, tone and tasks when prompting alone is not enough.
RAG systems
Retrieval-augmented generation that answers from your documents and databases, with citations users can check.
Agentic AI workflows
Multi-step agents that call your tools and APIs, with guardrails and human approval where it matters.
Computer vision & NLP
Image, video and text pipelines — detection, extraction, classification and semantic search.
AI search & recommendations
Semantic search and personalised recommendations built on vector databases and embeddings.
Our approach to AI Solutions.
A clear sequence, agreed up front, so you always know what happens next and why.
Define the business problem and what a successful answer looks like
Audit the data you have — volume, quality, gaps and privacy constraints
Build a quick baseline so every improvement can be measured
Choose the model approach: prompting, RAG, fine-tuning or a custom model
Add guardrails, evaluation suites and human review where needed
Deploy as a production service with monitoring and drift alerts
Measured, not assumed
Every model ships with an evaluation suite and a baseline, so quality is a number you can track.
Grounded in your data
Answers and predictions come from your own content and systems, not generic internet knowledge.
Built to run
Production APIs, monitoring and documentation — not a notebook you have to rebuild later.
What our AI work covers
Prompt engineering
Structured prompts, few-shot examples and output schemas that make LLM behaviour predictable.
Evaluation suites
Automated test sets that score accuracy, grounding and safety on every change.
Vector databases
Pinecone, pgvector and ChromaDB set up for fast, relevant retrieval at scale.
Guardrails & safety
Filters for privacy, prompt injection and unsafe output, with escalation to a human.
Human-in-the-loop
Review and approval queues so experts confirm high-stakes answers before users see them.
MLOps
Versioned models, reproducible training and monitored deployments with rollback.
From first call to production.
Weekly demos, clear milestones and access to the code from day one.
Discover
We dig into your problem, goals, data and existing systems before proposing anything.
Architect
We design the AI stack, database schema, API structure and interface, then agree scope and timeline.
Build
Focused sprints covering backend, AI models and frontend together — with a demo every week.
Deploy
We ship to production with CI/CD, monitoring and documentation, then hand everything over.
AI Solutions: common questions.
Can’t find your answer? Ask us directly — we reply within 24 hours.
Should we fine-tune a model or use RAG?
Usually RAG first: it keeps answers grounded in your latest content and is cheaper to update. Fine-tuning helps when you need a specific style, format or behaviour that prompting and retrieval cannot achieve. We test both against your data before recommending one.
Can you work with our private data securely?
Yes. We can deploy models in your own cloud account, use providers with no-training data agreements, and add privacy filters so sensitive data is handled according to your policies.
How do you know the AI is good enough?
We agree success criteria up front, build an evaluation set from real examples and measure every version against a baseline. Results are shared with you, not just a demo.
Which models and providers do you use?
Whatever fits the problem — OpenAI, Anthropic, open-source models via HuggingFace, or custom PyTorch models. We stay provider-neutral so you are not locked in.