AI Engineering
We build agents, RAG systems and automation that run against real enterprise data.
Production AI Engineering & Assurance
Practitioner-led AI engineering for enterprises that need outcomes, not theatre.
We work with organisations moving from experimentation to enterprise AI capability.
Who Captivolt is
Captivolt is a production AI engineering and assurance company. We engineer the systems (agents and RAG, automation, the enterprise context layer they reason over), and we prove how they behave, govern the risk, and build the capability to sustain them.
- Company
- Captivolt Technologies
- What we do
- Production AI Engineering & Assurance
- Based in
- Pune, India
- Method
- Diagnose → Design → Build → Assure → Transfer
Why Captivolt exists
Getting an answer out of a model is no longer the hard part. What stops enterprise AI is everything around it: governed data, permissions, evaluation, security, ownership, and a team able to run the system after the first release.
Captivolt exists to engineer that part: to take AI from a working demonstration to a system an enterprise can operate, evidence and govern, and to leave the capability with the client when the engagement ends.
Engineering philosophy
Enterprise AI succeeds when engineering discipline, governance, and people are designed together. Models are the easy part; the operating system around them is the work.
Production over prototypes
We design for the environment a system has to survive in (real data, real permissions, real load and a named owner) rather than for the demonstration.
Evidence over claims
A claim about quality, risk or value is only as good as what can be inspected. Where the evidence cannot be shown yet, we say so instead of filling the gap.
Governance by design
Controls, ownership and oversight are engineered into the lifecycle from the first architecture, not added before an audit.
Measurable quality
AI quality is measured against agreed datasets and thresholds before release and watched after it, not judged in a demo.
Capability transfer
Every engagement is designed to end with the client’s team able to operate and improve what was built, without us.
The practitioners
AI Quality & Governance
We prove how a system behaves before release, and keep watching after it.
Enterprise Software & Platform
We connect AI to the systems of record and platforms it has to live in.
Capability Transfer
We leave your team able to operate and improve what we built.
Intellectual property
Assets Captivolt has built and brings into engagements. They are owned by Captivolt; this is not a claim of patents or registered marks.
VeriCore AI Evaluation Studio
ProductA quality and evaluation layer for LLM, RAG and agentic systems.
AegisIQ AI Governance Workbench
ProductAI inventory, risk classification, approval workflows and control evidence.
Agentic RAG Accelerator
AcceleratorPermission-aware retrieval, grounding and citation over governed enterprise knowledge.
AI Automation Blueprint
BlueprintA model for redesigning fragmented workflows into AI-assisted, human-governed automation.
AI-Native SDLC Blueprint
BlueprintAI embedded across requirements, design, development, testing, release governance and support.
Enterprise AI QE Architecture
Proprietary frameworkA repeatable way to test LLM, RAG and agentic systems before and after release.
Agentic RAG Framework
Reference architectureThe reference architecture behind governed retrieval, from ingestion to a cited answer, with evaluation wired in.
Diagnose → Design → Build → Assure → Transfer
MethodThe stages every engagement is organised around, from diagnosis to handing the capability over.
Location and delivery model
Based in Pune, India, serving enterprise clients globally.
- How engagements run
Organised around one lifecycle (diagnose, design, build, assure, transfer), so it is clear at any point what is being delivered and what comes next.
- Who does the work
Senior engineers from the first conversation, with no layer between the client and the people building the system.
- Where systems run
In the client’s environment (their cloud, a private VPC or a hybrid estate), under their identity and access controls.
- How engagements end
With the client’s team operating what was built: runbooks, standards and ownership transferred, rather than a dependency.
- What each agreement sets
Where the work is done, on-site presence, working-hours overlap, subprocessors and model providers are agreed per engagement, not stated on this website.
Trust
- We design for client-controlled data environments.
- We support deployment into client cloud, private VPC and hybrid estates.
- We design access control and data classification into AI systems.
- We separate evaluation, governance, and monitoring concerns.
- We do not publish client proof without permission.
- Reference discussions may be available under NDA where permitted.
See how we work, on your problem.
A structured first conversation about what you are trying to build, govern, or scale.