Enterprise AI solutions & assistants
We turn document processing, information extraction, domain-specific summarisation, knowledge search and data-grounded assistants into usable products.
The problem we address
A fluent answer is not always a correct one. An intelligent system needs clear sources, visible data limitations and an appropriate review process, all within the user’s actual workflow.
Deliverables we can define
- Structured extraction from documents and reports
- Search and answers grounded in authorised sources
- Quality evaluation with real examples and human review
- Access controls, event logging and model version management
Example applications
What do we review before starting?
Document samples, authorised sources, confidentiality and acceptance criteria are reviewed before choosing a model. Sensitive decisions can remain subject to human approval.
Practical guides for this solution
Where should enterprise AI begin?Confidentiality and evaluation in enterprise AIEnterprise AI assistants: from internal knowledge to cited answersQuestions about this solution
01How does an enterprise assistant use internal knowledge?
One design retrieves authorised sources and generates an answer with references, commonly called RAG. User permissions, document versions and unanswerable questions belong in design and evaluation. Architecture and hosting follow organisational information policy.
02How is AI output quality evaluated?
Use agreed examples, explicit criteria and expert review: extraction accuracy, valid citations, uncertainty and access controls. A demonstration does not guarantee organisation-wide performance; acceptance tests should cover real conditions and exceptions.
Connected capabilities.
The capabilities your solution needs, working together.
Your next challenge.
Our next collaboration.
An idea, a complex workflow or data waiting to become a decision. Let’s talk about it.