We build generative AI, machine learning and automation into real products — grounded on your own data, wrapped in evaluation and guardrails, and run with the same operational rigour we bring to every platform.
Each of these is something we build end to end — not a slide, and not a wrapper around someone else’s API with no evaluation behind it.
Copilots, assistants, and content engines built on leading foundation models — with prompt architecture, human-in-the-loop review, and cost controls.
Retrieval-augmented generation over your documents, tickets and databases so answers stay current, grounded, and traceable to a source.
Tool-using agents and workflow automation that clear repetitive back-office work, integrated with your ERP, CRM and support desk.
Forecasting, scoring and recommendation models trained on your history, deployed behind versioned APIs and watched for drift.
The plumbing AI depends on — pipelines, feature stores, and CI/CD for models, so every release is reproducible and observable.
Evaluation harnesses, guardrails and audit trails that keep AI features safe, explainable, and defensible to regulators.
A prototype that answers well in a meeting is not the same as a system that answers well for ten thousand users, on messy data, at a cost you can defend.
Roughly how the first three months run.
We pick per project rather than defaulting to one vendor — the right model for the task, the budget, and the data-residency rules you operate under.
Yes. We build production generative AI systems including domain-tuned copilots and chat assistants, retrieval-augmented generation over your own documents and databases, tool-using AI agents, and intelligent document processing. Every build includes evaluation, guardrails, and monitoring.
We ground models on your own content using retrieval, so answers cite a source and stay current as your data changes. Before launch we run evaluation suites that score accuracy against real cases from your domain, and we monitor quality and drift after release.
OpenAI, Anthropic Claude, Google Gemini, and open models such as Llama — deployed via Azure OpenAI, AWS Bedrock, or Vertex AI. Our tooling includes LangChain, LlamaIndex, Hugging Face, PyTorch and TensorFlow, with Pinecone, pgvector or Milvus for vector search.
No. We use enterprise API tiers and deployment options that exclude your data from provider training, and we can run open models inside your own cloud tenancy where data residency or contractual terms require it.
It depends on data readiness and scope. A focused assistant or automation pilot is typically a 6–10 week engagement; a platform-level programme runs longer. We scope during discovery and give a fixed proposal before any build commitment — including the expected inference running cost, not just the build cost.
Tell us the outcome you need. We’ll tell you honestly whether we’re the right team, and what it would take — usually within one business day.
