03 / AI Integration
Connect useful AI to your documents and existing tools. Test a focused search or assistance task with approved data, review answer quality and cost, then decide what is ready to integrate.
Connect the knowledge you have to the tools you use.
What we measure togetherSuccess measures defined for your project.
A perspective from the industry
IBM explains how connecting a language model to external knowledge can improve its answers and make the source of information clearer.
IBM TechnologyWatch on YouTubeIndependent educational video. No partnership or endorsement of Musée is implied.
Process illustration, not a client result. Your approved scope defines the deliverables.
01 / The scope
Start with approved data and a named evaluator. Agree access, provider terms, budget limits and human fallback. A useful prototype does not establish that every answer is correct or that all business workflows should use AI.
A task brief, approved data sources and evaluation criteria; provider choice follows quality, privacy and running costs.
A scoped prototype tested on representative questions, including missing information and incorrect answers.
Connect only the tools and data in the approved scope, with access rules and a human fallback.
A record of limitations, usage guidance and agreed costs. Production monitoring and support are specified separately.
Document deliverables, approval points, revisions and exclusions so completion can be checked against the proposal.
02 / From idea to execution
A small use case with clear limits.
We map your current systems, identify the highest-value integration points, and define what success looks like. This phase produces a clear technical spec and project plan.
We design the technical architecture, select AI models and providers, and validate the approach with a targeted proof of concept on real data.
We build the integration, connects it to your systems, and implements all production requirements including error handling, monitoring, and cost controls.
Rigorous testing with real-world scenarios, load testing, and edge case validation. We deploy to production with rollback capability and monitor closely during the initial period.
Post-deployment, we analyze performance data, optimize prompts and configurations, document everything, and hand off to your team with full knowledge transfer.
03 / The right fit
You have experimented with ChatGPT or built a proof of concept, but the gap between a demo and a production system feels enormous. We bridge that gap with production-grade architecture, error handling, and monitoring.
Hiring an ML engineer for a single integration project does not make sense. We bring the expertise you need for the duration you need it, and we transfer knowledge so your team can maintain what we build.
The AI landscape changes weekly. New models, new providers, new capabilities. We cut through the noise, evaluate what actually matters for your use case, and recommend the approach that balances performance, cost, and reliability.
04 / FAQ
Yes. We review your applications, APIs, and access permissions, then design the integration around the tools you want to keep.
We test suitable providers against your task, data requirements, response quality, speed, and running cost. The choice follows the evidence from those tests.
Define fallback and review rules in the scope. Test wrong, missing and conflicting answers before release. Sensitive decisions stay with an authorised person; model responses are not guaranteed facts.
Connect the bigger picture
04 / 09AI automation for a defined business workflow: receive a request, prepare a response, ask for approval and update the right system. Establish the manual baseline before testing the automated version.
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07 / 09A business website or web application with a clear customer journey and editable content. Agree the pages, forms and integrations, then test mobile use, accessibility and search access before handover.
Learn MoreMusée / AI Integration
Start with approved data and a named evaluator. Agree access, provider terms, budget limits and human fallback. A useful prototype does not establish that every answer is correct or that all business workflows should use AI.
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