AI Engineering
Make AI part of everyday work.
We build AI applications that access company knowledge, cite sources and work with your tools. From prototype to real use, we design for quality, access boundaries and human control.
Answers grounded in sources
Use the request form in the employee portal. Manager approval is required.
Source: Employee handbookIs this service right for you?
01You need answers grounded in your documents.
02You want to connect an AI prototype to real systems.
03You want an assistant to handle repetitive tasks within clear limits.
01 /
What this service covers
01Company knowledge and RAGMake knowledge accessible.
Company knowledge and RAG
We bring documents into a knowledge layer for retrieval and grounded answers.
Work involved
- RAG and data preparation
- Enterprise AI assistants
- AI search and source citations
What you receive
A source-grounded assistant with defined access rules.
02Agents and tool connectionsMove from answers to controlled actions.
Agents and tool connections
We define which tools agents can use and within what boundaries.
Work involved
- AI agents
- MCP and API connections
- Human approval steps
What you receive
Tool-connected task flows with limited permissions.
03Application integrationBring AI into existing work.
Application integration
We integrate assistants into portals and applications your teams already use.
Work involved
- AI-powered applications
- Identity and role integration
- Private AI application connections
What you receive
AI features integrated into your existing user experience.
04Evaluation and improvementTest quality with examples.
Evaluation and improvement
We assess answers, failures and feedback against realistic tasks.
Work involved
- Answer-quality evaluation
- Error and feedback flows
- Usage and cost tracking
What you receive
An evaluation set and improvement priorities.
02 / Example in practice
Support with cited sources
Illustrative scenario; not a client project.
- 01
Need
Support staff repeatedly search documents for common questions.
- 02
Approach
We build an assistant that searches approved sources and drafts responses.
- 03
Resulting structure
Staff review the sources and approve the proposed answer.
03 /
How we work
- 01
Scope
Define user tasks and data boundaries.
- 02
Prototype
Test the approach with sample data.
- 03
Integration
Connect the application to tools and identity systems.
- 04
Evaluation
Check acceptance criteria and prepare deployment.
04 /
Concrete outputs
Deliverable details and scope are agreed during discovery around your needs.
- 01
A source-grounded assistant with defined access rules.
- 02
Tool-connected task flows with limited permissions.
- 03
AI features integrated into your existing user experience.
- 04
An evaluation set and improvement priorities.
05 /
Frequently asked questions
How do you address answer accuracy?
We use source citations, example question sets and task-specific evaluations. Critical decisions and actions include human approval.
Can it connect to our current application?
We assess APIs, authentication and data access to define integration scope.
Which model do you use?
We evaluate models against language, task, data constraints, cost and hosting requirements rather than committing to one provider.
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AI Engineering
Let’s define the next step.
Share your current situation and priorities so we can find a suitable starting point together.