Context before output
Connect the sources that make an answer relevant to the business and visible to its reviewer.
Production AI & RAG Integration
AI and RAG integration for business workflows.
We integrate generative AI, retrieval-augmented generation (RAG) and APIs into a defined business workflow, with source references, evaluation, human review and monitoring.
Let’s talkThe answer is only useful if the workflow can use it.
A promising prototype still needs the right sources, system connections and review process. The opportunity is to help people complete a specific task, with a clear understanding of where AI helps and where human judgment belongs.
We start with the task and the person responsible for it. We connect the relevant information, build the workflow and evaluate its behavior against the situations your team expects to encounter.
A useful way forward
People in control
Define review, exceptions and access boundaries around the consequences of the task.
Learning through evaluation
Use representative cases to understand quality, failure modes and the limits of the workflow.
See the thinking in context
Explore an assistant grounded in company knowledge
A reference design connects the business situation to the system behind it. Explore the workflow and the decisions that shape it.
Explore this design- 1Ask
- 2Find the source
- 3Answer
What is our delivery policy? Search checks Word documents, PDFs, database records, and the company wiki, then retrieves Delivery policy PDF, section 2. Standard orders ship within two business days after confirmation. The answer cites that source.
Only searches documents the employee can access.
When you want the detail
Shape the work
around your business.
The right scope depends on your systems and priorities. These are the areas we can explore together.
Where engineering can help
- LLM and API integration
- Retrieval-augmented generation (RAG) and document workflows
- Evaluation, human review, and guardrails
- Backend integration and monitoring
Planning the engagement
Fixed-scope integration centered on a bounded workflow; discovery may be hourly when requirements are still uncertain.
Teams with a defined workflow and business owner that need to move from prototype to an operable implementation.
Building for team ownership
Your team receives source code, configuration guidance, evaluation assets, operating notes, and a technical walkthrough.
- Integrated AI workflow
- Evaluation cases
- Guardrails and failure handling
- Backend services
- Monitoring approach
- Documentation and handoff
A little more context
Questions worth exploring
Understand the scope, delivery approach, and responsibilities for production ai & rag integration
Can Varveta move an AI prototype into a business workflow?
Yes. Production AI integration connects a defined workflow to the required backend services and LLM or other APIs. The engagement includes an evaluation approach, guardrails, failure handling, monitoring, and team handoff.
Can generative AI use company documents to support answers?
Retrieval and document workflows are part of the integration scope. The company knowledge assistant reference design shows permission-filtered retrieval and answers linked to sources. The actual sources, access requirements, evaluation cases, and controls are agreed for your workflow.
What should be defined before an AI integration project starts?
Start with a bounded workflow and a business owner. We agree the requirements, evaluation approach, integrations, and handoff before implementation. When requirements are still uncertain, discovery may be scoped hourly before a fixed-scope integration.
Let’s connect the idea to a next step.
Tell us what is changing in your business and where you want to go.
Let’s talk