Production architecture developed
Agentic RAG, evaluation and governance architectures built for real estates, not demonstrations.
Production AI Engineering & Assurance
Enterprise AI engineered for production.
Captivolt designs and builds AI agents, enterprise RAG, intelligent automation, context and data platforms, and AI-native applications, with evaluation, governance, security and human control designed into the system.
We connect AI to real enterprise data, real workflows and real accountability, then evaluate how it behaves before and after release.
- Enterprise AI Agents & RAG
- AI Automation
- Enterprise Context & Intelligence
- AI Quality, Governance & Security
- VeriCore AI Evaluation
- AegisIQ Governance
- AI-native Software Engineering
Video · 2 min
Production AI beyond the model.
One business question, followed through the ten layers a production system adds around the model, from enterprise inputs and identity to guardrails, human authority, evaluation, observability and audit.
Production AI beyond the model · 1:58
Read the video as text
- The simple prompt. A question is typed into the Captivolt Intelligence Console: “Which customer account should we prioritise this quarter?” “Getting an AI answer is easy.”
- “Trusting it inside an enterprise is not.”
- The model reveal. A user, the model and an answer. “The model is only one part of the system.”
- Layer 1 of 10, enterprise inputs. CRM, documents, contracts, support, data platforms and people converge toward the intelligence layer. “Enterprise context changes the answer.”
- Layer 2 of 10, identity and access. Account history and contract terms pass the access gate; unrelated HR records are refused. “Right user. Right context. Right boundaries.”
- Layer 3 of 10, retrieval. Semantic, keyword, business facts, freshness and access are ranked and selected down to the top account signal and the latest contract clause. “Retrieve what matters — not everything available.”
- Layer 4 of 10, context engineering. Facts, retrieved evidence, relationships, business signals, policies and state are assembled into a context manifest. “Context is engineered.”
- Layer 5 of 10, the agent. The model works from the context manifest, tools, policies and the task to analyse, reason and recommend. “AI becomes an enterprise capability.”
- Layer 6 of 10, guardrails. Access control, content policy, action limits and data protection: a proposed external email is stopped at the boundary, and an update to the account note passes. “Control what AI can know, say and do.”
- Layer 7 of 10, human authority. Reading and recommending are permitted by policy; executing requires human approval. “Automation does not remove accountability.”
- Layer 8 of 10, evaluation. Groundedness, citation coverage, relevance and policy compliance pass, and task success is checked. “Trust must be measured.”
- Layer 9 of 10, observability. The request is traced from user to agent, retrieval, model, tool and response, with its latency, sources, errors and policy events. “Trace what happened across the system.”
- Layer 10 of 10, compliance and audit. A sealed audit evidence record of who asked, what was asked, the data used, the policy, the model route, the action, the approval and the time. “Turn AI behaviour into evidence.”
- Return to the question. The answer now arrives with a recommendation, its rationale, its evidence, a confidence level, a recommended action (schedule an executive review) and an approval status awaiting the user.
- The full system: enterprise, context, intelligence, control and assurance, leading to the business outcome.
- “Production AI is a system. Not just a model.”
- Captivolt: production AI engineering and assurance. THINK → BUILD → ASSURE → SCALE.
Proof
What we can show you today.
No invented figures. Each of these is something built, delivered or demonstrable.
Enterprise AI frameworks delivered
Including a board-credible governance framework for an NSE-listed company.
AI-QE architecture
A repeatable way to test LLM, RAG and agentic systems before and after release.
Governed RAG reference implementation
Permission-aware retrieval, grounding and citation, with evaluation wired in.
Practitioner-led delivery
Senior engineers in the room from day one; no layer between you and the people building it.
AI-native SDLC, measured
Delivery productivity gains from an AI-native software lifecycle.
The production AI challenge
The model is only the beginning.
Enterprise AI creates value when it can be trusted with real business work.
That trust must be engineered. AI must use the right enterprise context, respect permissions, ground its answers in evidence, operate within defined guardrails, and remain observable, measurable and accountable.
Captivolt brings data, context, models, agents, workflows, evaluation, security, governance and human oversight together as one production system, so enterprises can deploy AI with confidence and scale it reliably.
Production AI architecture
Four layers, wrapped by the disciplines that make them safe.
Enterprise systems, the context built on them, the models and agents that reason over it, and the applications that act, with evaluation, governance, security and observability across all four rather than after them.
Evaluation · Governance · Security · ObservabilityVeriCore + AegisIQ wrap every layer
LAYER 01Enterprise SystemsERP · CRM · ITSM · HRMS · databases · data platforms · document stores · APIs
What we build here
We engineer against the systems you already run, through the APIs where they exist and the integration work where they do not, and a call carries the permissions of whoever made it.
LAYER 02Context & KnowledgeIngestion · indexing · unstructured knowledge · metadata · permissions · semantic modelling · lineage
What we build here
Permission-aware retrieval, semantic modelling and lineage, so an answer can be traced back to a source the caller is allowed to see. This is the layer a document dump into a vector store skips.
LAYER 03Models & AgentsBusiness intent · LLMs · tools · agents · workflows · human approvals · policies
What we build here
Models chosen per task rather than per vendor, agents given explicit tools and a defined authority to act within, and an approval step on the actions that are hard to reverse.
LAYER 04Applications & ActionsEnterprise agents · dashboards · workflow automation · enterprise applications · audit trail
What we build here
The workflows and applications that act on the output, with an audit trail per action rather than per conversation, so what was done, by which agent and under whose authority, is answerable afterwards.
Buyer pathways
What are you trying to move forward?
Solutions
THINK → BUILD → ASSURE → SCALE
One journey from AI ambition to operating capability. Most engagements travel through several stages.
Signature differentiation
AI quality is an engineering discipline, not an opinion.
Traditional testing is necessary and insufficient for probabilistic systems. We evaluate response and retrieval quality, grounding, hallucination, tool selection, task completion, policy adherence, escalation, safety, cost, latency, regression and drift, before release and after it.
Start here · VeriCore
AI Release Readiness Review
Three weeks, fixed scope, fixed fee. One AI system tested the way your customers will use it, and a go/no-go decision you can take to your board.
- Week 1
Scope and golden set
One AI system, 100–300 real questions from your business owners, and agreed pass thresholds.
- Week 2
Repeat-run evaluation
Every question run repeatedly and scored for grounding, consistency, intent handling, data leakage, refusals and latency.
- Week 3
Go / no-go readout
A ranked defect register with root causes, a remediation plan, and a readout for your CEO or CTO.
Works on LLM applications, enterprise RAG and AI agents. Runs inside your environment, with findings mapped to RBI, SEBI, DPDP and ISO/IEC 42001 expectations.
Why Captivolt
Anyone can pick the same tools. The difference is what holds them together.
Every credible AI engineering company can work with the major models, vector stores, clouds and orchestration frameworks, so none of that is a reason to choose one. These six are, and it is the combination that matters, not any single one of them.
Production-first engineering
Systems are designed around the environment they have to survive in, not the one a demo runs in.
- Enterprise data
- Permissions
- APIs
- Workflows
- Security boundaries
- Deployment models
- Operating constraints
AI Quality Engineering
AI is systematically evaluated, not accepted because a demonstration looked convincing.
- Datasets
- Regression suites
- Scorecards
- Release gates
- Monitoring
Governance integrated into engineering
Not a compliance exercise bolted on after deployment. Controls, ownership, evidence and oversight are engineered into the lifecycle.
- AI system register
- Risk classification
- Approval workflows
- Control evidence
- Human sign-off
Enterprise Context Engineering
Trustworthy context is engineered rather than retrieved. We combine the sources and the rules that make an answer defensible.
- Facts
- Documents
- Vectors
- Relationships
- Semantics
- Metadata
- Provenance
- Permissions
Reusable IP
VeriCore, AegisIQ and our RAG architectures mean no engagement restarts from zero.
- VeriCore
- AegisIQ
- Agentic RAG Accelerator
- AI Automation Blueprint
- AI-Native SDLC Blueprint
Capability transfer
What your teams keep when we leave, instead of a permanent dependency on us.
- Architecture
- Code
- Runbooks
- Controls
- Evaluation systems
- Operating procedures
- Knowledge transfer
Why not just use OpenAI, Microsoft, or Google directly?
AI platforms provide powerful capabilities. Captivolt helps enterprises apply those capabilities inside real operating environments, designing the architecture, integrations, evaluation, governance, security, workflows, and capability transfer required for production use.
Products & Accelerators
Reusable IP, applied inside the work.
Our own evaluation, governance and architecture assets, so no engagement restarts from zero.
Real Work
Anonymised engagements, and what changed.
Every case study opens with the result, then shows the constraints, the architecture and what the client owns now.
- THINK
- ASSURE
Enterprise AI Framework for an NSE-listed Company
Real anonymised engagement- Client 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 delivered
- Governance framework · use-case intake workflow · risk classification · accountability model · evidence requirements · oversight cadence.
- What changed
- A listed company moved AI from scattered initiative to a governed operating capability its board can oversee.
- AI governance workflow
- Risk classification model
- Use-case intake design
- Evidence model
- BUILD
- ASSURE
Enterprise AI QE Architecture
Proprietary framework- 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 delivered
- Evaluation architecture · dataset design patterns · regression suite structure · scorecard model · monitoring approach.
- What it provides
- AI quality became evidence rather than opinion: one repeatable architecture for testing LLM, RAG and agentic systems before and after release.
- AI-QE lifecycle
- Evaluation scorecard (concept)
- Regression suite design
- THINK
- SCALE
Building AI/ML Capability for a Multinational Enterprise
Real anonymised engagement- Client 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, so capability could not be hired or grown consistently.
- What Captivolt delivered
- Operating model · role architecture · capability framework · hiring validation design · onboarding and ramp model.
- What changed
- A multinational enterprise can now define, validate and grow AI/ML capability the same way in every team, using its own people.
- Capability model
- Role architecture
- Validation scorecards
- Onboarding & ramp system
- BUILD
- ASSURE
The Release Gate That Held an Enterprise AI Launch
Real anonymised engagement- Client context
- A listed IT services company was preparing to launch an enterprise intelligence platform built on agents, RAG and a knowledge graph.
- Challenge
- The platform performed well in demonstrations. No one had tested whether it gave the same evidence-backed answer every time it was asked.
- What Captivolt delivered
- Golden-question set · repeat-run evaluation harness · evidence-binding and ranking checks · ranked defect register · go/no-go readout.
- What changed
- The gate found unstable evidence binding and ranking that ignored question intent. Launch was held for root-cause fixes before any client saw them.
- Release gate
- Golden-question set
- Defect register
- Go/no-go readout
Industries
Where this work lands.
Four sectors where governed, evidenced AI is not optional.
Captivolt Point of View
Three ideas we are willing to be judged on.
Not a service list. These are the positions the rest of this site argues from.
Building AI is becoming easier. Engineering AI that can be trusted in production is not.
Start with a structured conversation, or use the AI Readiness Diagnostic to find where your organisation should begin.