Search across internal knowledge
Help employees find information across policies, manuals and operational documents. Retrieval should respect existing access rights and show the source behind an answer.
Technology
AI development and document automation
An AI system is useful when it improves a real workflow. We start with the documents, decisions and users involved, then design a solution whose answers and actions can be checked.
Discuss this project
Problems we can help solve
Document processing and assisted decisions
Extract relevant information from incoming files, classify requests and prepare work for human review. Define which decisions stay with your team and how uncertain results are handled.
What the project can deliver
- A map of data sources, permissions and integration requirements.
- A working prototype with a representative evaluation set and agreed quality criteria.
- An integrated workflow with logging, human review and operating documentation.
Questions before development
Do we need to train our own model?
Not necessarily. Retrieval, existing models and conventional software may solve the problem. A prototype helps compare quality, operating cost and data-handling requirements before choosing an approach.
Can confidential data stay in our environment?
Deployment and model choices depend on your access rules, infrastructure and contractual requirements. These constraints should be agreed before any sensitive data enters a prototype.
How is quality assessed?
We define representative tasks, expected outcomes and unacceptable mistakes with your team. Evaluation covers answer quality, source attribution, access controls and the behavior of the complete workflow.
Next step
Discuss this project
Tell us what needs to change. We will start with the task, existing systems and the outcome you want.
Start a project