Useful AI belongs inside a working product.
From a knowledge assistant to a document-processing workflow, the model is only one part of the product. We connect AI to the interface, permissions, data and operations it needs.
Build assistants, search and prediction into the way people work.
A convincing demo can still fail when information changes, users ask unfamiliar questions or a system must explain its answer. Production work starts by defining those conditions and testing against them.
A clear scope.
Tangible outputs.
Generative AI applications
Assistants, summarization, drafting and extraction with explicit source access and review workflows.
RAG and semantic search
Document ingestion, retrieval, access filtering and source-linked answers, evaluated on representative questions.
Machine learning workflows
Forecasting, classification, NLP and computer-vision solutions scoped around data availability, labeling and measurable evaluation.
Production integration
Application APIs, error handling, monitoring, usage controls and a plan for model and prompt changes.
Make the next decision clearer.
What to bring: Representative user questions, approved knowledge sources, access rules and examples of unacceptable responses.
What we can scope first: An integration approach and evaluation set covering answer quality, source attribution, latency and operating cost.
We agree the deliverables, access requirements and commercial terms before starting.
Discuss this starting pointThe questions
worth answering.
- What does a correct result look like for this task?
- Does the system need fresh knowledge, learned behavior or ordinary search?
- What latency and operating cost will the workflow tolerate?
- Who reviews ambiguous or consequential outputs?
Can AI use our internal documents?
It can, when the selected architecture and data permissions allow it. Retrieval must respect access rights, source freshness and the limits of the material.
Do we need to train our own model?
Often there are simpler options. We compare existing models, retrieval, prompting and task-specific training against your evaluation criteria.