Real Work · Case studies
Explore five enterprise AI case studies alongside a reference architecture and a proprietary quality engineering framework. Each entry is clearly classified, so buyers can distinguish delivered client work from reusable Captivolt intellectual property.
Five client engagements
The engagements span AI governance, capability development, transformation guidance and governed agentic systems. Client identities remain confidential where required; the work is described at the level a buyer needs to judge its relevance.
Captivolt helped define the operating model, role architecture, capability framework, hiring validation, and ramp approach required to build repeatable AI/ML capability.
Captivolt developed an enterprise AI framework covering use-case intake, governance, risk classification, accountability, evidence, and leadership oversight.
Captivolt guided leadership on AI transformation priorities, use-case sequencing, operating model, value pools, and delivery readiness.
Captivolt led the architecture, engineering direction and assurance model for a governed enterprise intelligence and agentic AI platform spanning enterprise context, retrieval, orchestration, controls, evaluation and operations.
Captivolt put a release gate in front of a listed IT services company’s enterprise intelligence platform: a golden-question set, repeat-run evaluation and a go/no-go readout before launch.
Reference architecture
Evidence of how Captivolt designs production AI systems. Reusable reference architectures, not presented as client case studies.
A permission-aware architecture for enterprise knowledge and agentic retrieval.
A permission-aware architecture for enterprise knowledge ingestion, retrieval, grounding, orchestration, access control, evaluation, and observability.
Proprietary framework
Captivolt intellectual property used in delivery. It shows the assurance discipline applied to LLM, RAG and agentic systems; the productised versions are documented in full on their own pages.
A structured architecture for evaluating and monitoring production AI.
A structured architecture for testing and monitoring LLM, RAG, and agentic systems through evaluation datasets, prompt regression, retrieval testing, hallucination checks, and drift monitoring.
How evidence is presented
Real Work is not a gallery of unsupported claims. Client engagements, reference architectures and proprietary frameworks are labelled separately, so a buyer always knows what kind of evidence they are assessing.
The public page establishes the type of work, its scope and the nature of the supporting artefacts. Where client permission allows, anonymised artefacts and reference discussions can be made available under NDA.
Production AI Engineering & Assurance
THINK, BUILD, ASSURE and SCALE are the common operating model. The entries on this page show delivered engagements and the reusable architectures that support work across it.
Establish the skills, teams and operating capability required to sustain them.
Want to see the artefacts?
Anonymised artefacts and reference discussions are available under NDA where client permission allows.