Teams cannot find answers across approved knowledge sources
How we startKnowledge assistant pilot
Build a bounded retrieval experience with source references and representative answer checks.
Build AI around a clear business use case
We help teams define practical AI use cases, prepare the permitted data, build the right retrieval or workflow layer, and evaluate whether the system is ready to operate.
At a glance
Where we can help
Recommendations need to fit an existing user or business decision
How we startUse-case and data fit
Define the user task, available sources and a practical way to evaluate a pilot.
Automation needs a bounded workflow and a way to check results
How we startRecommendation or workflow pilot
Prototype a specific recommendation or automation step within an existing workflow.
Methods and integrations
Selected to fit the approved data and the pilot task.
- Approved knowledge and enterprise data sources
- Semantic search, vector retrieval and RAG
- MCP and n8n workflow integrations
Test a knowledge assistant
Pilot goalPilot a cited answer workflow with an evaluation set
Read the insight- Challenge
- Answers are scattered across approved knowledge sources
- Recommended approach
- Check access, extraction and retrieval against real questions
Explore the foundations
The team and data work behind the approach
What responsible delivery includes
- A named user, use case and acceptable operating boundary
- Permitted repositories, owners and access rules
- Data completeness, freshness, provenance and deletion requirements
- Retrieval or integration design appropriate to the use case
- A representative evaluation set and measurable acceptance criteria
- Human review, monitoring and escalation where the risk requires it
What it does not imply
A bounded AI engagement is not a promise of enterprise-wide transformation, unrestricted access to every dataset, foundation-model training or guaranteed business outcomes.