Decide whether it is worth building,then decide how
Demos are easy; production is not. It fails on data supply, evaluation, cost and security boundaries. We start by testing whether a use case is feasible, then deliver an application you can audit, operate and budget for.
Assess before you build
Each candidate use case gets a feasibility and return assessment: is the data there, how will quality be measured, what does a wrong answer cost. Budget stops going to use cases that were never going to work.
Built on governed data
Knowledge bases and training data come from classified assets, and permissions follow the user, so a model never sees what the person asking cannot.
Quality and safety, both measured
An evaluation set and a baseline before launch; hallucination rate, unauthorised retrieval, cost and latency monitored after it. When something goes wrong the request can be replayed, traced to the step that failed and rolled back.
AI capability domains
Six domains from strategy through to production — a single use case, or a platform and the operations around it.
Frequently asked questions
Common questions on model choice, output quality and self-hosted deployment. If yours is not here, talk to the AI team directly.
Most enterprise use cases do not require training. Start with retrieval augmentation to supply domain knowledge, use an evaluation set to confirm whether the bottleneck is model capability, and only then consider fine-tuning. Self-hosting depends on data sensitivity and compliance; the assessment compares the cost of API calls, private inference and fine-tuning.
Customer story
Reallysec Builds a Modern AI-Driven Search Platform for Cisco
Enterprise search on Cisco.com rebuilt with Elastic: 73% faster, with 90% of requests handled automatically.