Support & Knowledge

Support answers that cite a real source, not a guess.

The risk with AI-assisted support is confident invention. We build RAG assistants grounded in your own documentation and ticket history, using Spring AI's QuestionAnswerAdvisor pattern against your own vector store, so every answer traces back to a real source your team can verify.

Talk to an architect about your support toolingSee a related case study
01  How we solve it

What this looks like in practice

01

Retrieval grounded in your own documentation, tickets and knowledge base — not the model's general training data — using Spring AI's QuestionAnswerAdvisor pattern against your own vector store.

02

Every answer traces back to a real source your team can verify, instead of a confident-sounding invention.

03

Built inside the Spring Boot systems you already run, so the assistant has access to real ticket and account context, not just static documents.

04

Evaluation against a fixed test set of real support questions before release, so accuracy is measured, not assumed.

05

Guardrails and escalation paths for questions the assistant shouldn't answer alone — it's a force multiplier for your support team, not a replacement for judgment.

02  Proof

The same grounded-RAG pattern, in production

The retrieval and grounding pattern here is the one behind OptaAI, our AI-native sourcing platform — Spring AI, OpenAI embeddings and Elasticsearch vector search, with citations grounded in real data. Read the case study →

03  FAQ

Support & knowledge AI — frequently asked questions

How do you stop the assistant from making things up?
Retrieval-augmented generation grounds every answer in your actual documents and tickets via a vector store — the model is instructed to answer from what it retrieves and cite it, not to answer from general knowledge. We also evaluate against a fixed test set before release to catch failure modes early.
Can this integrate with our existing ticketing system?
Yes — the usual pattern is to index your existing documentation and historical tickets, then expose the assistant either inside the ticketing tool itself or as a standalone internal tool, depending on your workflow.
Is this the same technology as the OptaAI sourcing platform?
The underlying pattern is the same — Spring AI, retrieval-augmented generation, grounded citations — applied to support and knowledge content instead of a product catalogue.

Talk to an architect about your support or knowledge tooling.

Tell us what your team keeps looking up manually — we'll map what's worth grounding first.

Talk to an architect