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Real Work

Real work. Honest proof.

We show proof carefully: real anonymised engagements where confidentiality requires it, reference architectures where the work is proprietary, and artefact-led examples that demonstrate how we think and build.

All real work: 5 entries

All
THINK
BUILD
ASSURE
SCALE
  • THINK
  • SCALE

Building AI/ML Capability for a Multinational Enterprise

Real anonymised engagement

Captivolt helped define the operating model, role architecture, capability framework, hiring validation, and ramp approach required to build repeatable AI/ML capability.

  • Capability model
  • Role architecture
  • Validation scorecards
  • Onboarding & ramp system

CONTEXT

A multinational enterprise needed to move beyond isolated AI experiments to a repeatable, in-house AI/ML engineering capability.

CHALLENGE

Roles, skills, validation standards, and ramp practices varied by team — capability could not be hired or grown consistently.

WHAT CAPTIVOLT DID

Defined the operating model, role architecture, and capability framework; designed hiring validation and structured onboarding and ramp approaches.

ARTEFACTS DELIVERED

Operating model · role architecture · capability framework · hiring validation design · onboarding and ramp model.

WHAT CHANGED

The organisation gained a consistent, documented model for defining, validating, and growing AI/ML capability across teams.

EVIDENCE AVAILABLE

Anonymised artefacts and reference discussion available under NDA where client permission allows.

RELATED FRAMEWORKS

5-Gate Talent Validation Model · Capability frameworks

  • THINK
  • ASSURE

Enterprise AI Framework for an NSE-listed Company

Real anonymised engagement

Captivolt developed an enterprise AI framework covering use-case intake, governance, risk classification, accountability, evidence, and leadership oversight.

  • AI governance workflow
  • Risk classification model
  • Use-case intake design
  • Evidence model

CONTEXT

An NSE-listed company required a board-credible framework to take AI from initiative to governed operating capability.

CHALLENGE

AI activity was growing faster than the governance, accountability, and evidence structures needed to oversee it.

WHAT CAPTIVOLT DID

Developed the enterprise AI framework: use-case intake, governance workflows, risk classification, accountability model, evidence requirements, and leadership oversight cadence.

ARTEFACTS DELIVERED

Governance framework · use-case intake workflow · risk classification · accountability model · evidence requirements · oversight cadence.

WHAT CHANGED

Leadership gained a structured, execution-ready framework with named ownership and defined oversight.

EVIDENCE AVAILABLE

Reference available under NDA where client permission allows.

RELATED FRAMEWORKS

AegisIQ · VeriCore

  • THINK

AI Transformation Guidance for Enterprise Leadership

Real advisory engagement

Captivolt guided leadership on AI transformation priorities, use-case sequencing, operating model, value pools, and delivery readiness.

  • Priority & sequencing model
  • Value pool map
  • Readiness criteria

CONTEXT

An enterprise leadership team needed senior, vendor-neutral guidance on where and how to invest in AI.

CHALLENGE

Competing use cases, unclear sequencing, and uneven delivery readiness across the organisation.

WHAT CAPTIVOLT DID

Advised on transformation priorities, use-case sequencing, value pools, operating model, and readiness criteria for delivery.

ARTEFACTS DELIVERED

Prioritised use-case portfolio · sequencing model · value pool analysis · operating model recommendation · readiness criteria.

WHAT CHANGED

A prioritised, sequenced AI agenda that leadership could fund and govern with confidence.

EVIDENCE AVAILABLE

Advisory references available on request.

RELATED FRAMEWORKS

AI Readiness Diagnostic · Method: Diagnose → Transfer

  • BUILD
  • ASSURE

Enterprise AI QE Architecture

Proprietary framework

A structured architecture for testing and monitoring LLM, RAG, and agentic systems through evaluation datasets, prompt regression, retrieval testing, hallucination checks, and drift monitoring.

  • AI-QE lifecycle
  • Evaluation scorecard (concept)
  • Regression suite design

CONTEXT

GenAI systems routinely pass demos and fail in production — because they are not tested like enterprise software.

CHALLENGE

LLM, RAG, and agentic systems need evaluation disciplines that traditional QE does not provide.

WHAT CAPTIVOLT DID

Engineered VeriCore: evaluation datasets, prompt regression suites, retrieval testing, hallucination and grounding checks, and drift monitoring — pre-release and post-release.

ARTEFACTS DELIVERED

Evaluation architecture · dataset design patterns · regression suite structure · scorecard model · monitoring approach.

WHAT CHANGED

A repeatable testing architecture that turns AI quality from opinion into evidence.

EVIDENCE AVAILABLE

Architecture walkthrough available.

RELATED FRAMEWORKS

VeriCore · AI-Native SDLC Blueprint

  • BUILD

Agentic RAG Framework

Reference architecture

A secure architecture for enterprise knowledge ingestion, retrieval, grounding, orchestration, access control, evaluation, and observability.

  • RAG reference architecture
  • Permission model
  • Evaluation loop design

CONTEXT

Enterprises need knowledge systems that answer accurately, respect permissions, and can be observed and improved in production.

CHALLENGE

Naive RAG implementations leak data, hallucinate, and degrade silently.

WHAT CAPTIVOLT DID

Engineered a reference architecture covering ingestion, indexing, permission-aware retrieval, grounding, agent orchestration, evaluation, and observability.

ARTEFACTS DELIVERED

Reference architecture · permission-aware retrieval model · grounding and traceability design · evaluation and observability loop.

WHAT CHANGED

A production-grade pattern that teams can adopt, extend, and operate independently.

EVIDENCE AVAILABLE

Architecture walkthrough available.

RELATED FRAMEWORKS

Agentic RAG Accelerator · VeriCore

Want to see the artefacts?

Anonymised artefacts and reference discussions are available under NDA where client permission allows.

Request a Reference Discussion