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Service

Generative AI that ships into your stack, not a standalone demo

Copilots, retrieval-augmented search, document intelligence and custom LLM workflows, designed around your data and delivered into the tools your teams already use.

What is included

What you get

Scope is agreed with you up front and tailored to your situation. These are the building blocks.

Retrieval-augmented search (RAG)

Ask questions across your documents, wikis and tickets and get grounded answers with sources.

Copilots and assistants

Role-specific assistants for support, sales, engineering or operations, wired to your systems.

Document intelligence

Extract, classify and summarise contracts, invoices, reports and forms at volume.

Evaluation and guardrails

Test sets, quality metrics, prompt and model versioning, and safeguards for sensitive data.

Who it is for

Is this right for you?

Product and engineering teams that want LLM capabilities in production, with quality they can measure and costs they can predict.

Typical uses

Common starting points

  • An internal knowledge assistant over company documents
  • Automated first-draft responses for support or sales teams
  • Pulling structured data out of unstructured paperwork
  • Adding natural-language search or summarisation to an existing product
How we deliver

From first call to results

Short feedback loops and working output at every step, in overlap with your time zone.

01

Scope

Pick one use case, define success measures and review the data involved.

02

Prototype

A working version on your real data to test quality early.

03

Harden

Evaluation, security, monitoring and integration with your systems.

04

Launch & tune

Roll out, watch real usage and improve based on what you see.

Questions

Frequently asked

Which models do you work with?

We choose per use case across leading commercial and open models, based on quality, cost, latency and your data-residency needs.

Can our data stay private?

Yes. We design for your constraints, including private deployments and clear rules on what is sent to any external model provider.

How do you handle wrong answers?

Through grounding in your sources, measurable evaluation sets, citations, and human review where the stakes require it.

Next step

Tell us what you are trying to achieve

Share a few details and we will reply with an honest view on whether and how we can help.

Contact Qualsys