Zentis AI — the agentic AI platform for regulated industries

ZENTIS AI · INCUBATED BY TECHVANTAGE.AI

The Agentic OS for Regulated Industries

Gives regulated businesses a way to automate complex decisions without losing the ability to explain them

Request a demo See how it works

Trusted & Recognized By

ANTHROPICAI for humanity
NVIDIAINCEPTION PROGRAM
Future of AI
Voice AI

ZENTIS AI · INCUBATED BY TECHVANTAGE.AI

The Agentic OS for Regulated Industries

Gives regulated businesses a way to automate complex decisions without losing the ability to explain them

Request a demo See how it works

Trusted & Recognized By

ANTHROPICAI for humanity
NVIDIAINCEPTION PROGRAM
Future of AI
Voice AI

Built for the work that can't afford to be wrong.

One platform. Different domains. Governance that adapts to each mission-critical workflow. A single governed operating model, customized for the distinct risks of every function.

01Banking & Credit

Banking & Credit

Make faster credit decisions without sacrificing accountability. Confidence in every credit decision, today and years later.

Supervisors can see not just what decision was made, but why it was made, based on the evidence and policies applied.

Banking opportunities

Banking & Credit

Applications

Portfolio Risk Review
SME & Retail Credit Underwriting
Full Population Audit Testing

BFSI does not reject AI. It rejects AI it cannot audit.

Three things stop a promising pilot from reaching production, and none of them is model accuracy.

Nobody can show the check

Compliance is asked to approve a decision it cannot reconstruct. Without a visible challenge step, the honest answer is no.

The same input drifts

Language models are probabilistic. Regulated finance is not. By the fiftieth tool call, small variances have become a different decision.

Evidence arrives too late

Logs get reconstructed into an audit trail after the fact. A regulator wants the record that existed when the decision was made.

Three numbers every deployment is built to move.

General, deliberately. The exact figures are pilot-specific, and we'd rather show you a real one than promise a generic one.

Decision cycle time

How long a complex case takes from first notification to a released decision, measured against the queue it used to sit in.

Audit finding rate

How often a reviewed decision needs correction after the fact, once the adversarial record is part of the file from the start.

Complex case throughput

How many of the cases that used to escalate to your most senior person now get a first pass the moment they land.

The system built to move them

Accelerate digital transformation with our ecosystem: 168 named agents handling specialized workflows, 28 pre-built solutions for common use cases, 6 universal design patterns, and deep expertise across 4 BFSI verticals. Purpose-built for the financial industry.

Named agents

Pre-built solutions

Universal archetypes

BFSI verticals

Product tour

See AgentFlow in action

Watch the runtime govern an AI workflow end to end — every decision captured, every rule on the record.

Intelligent solutions for every workflow.

Portfolio risk review for a specialty carrier — workflow screenshot

Insurance · United KingdomPortfolio risk review for a specialty carrierA quarterly guessing game could become a same-day, defensible call.Learn more

Full population audit testing for a retail bank — workflow screenshot

Banking · Middle EastFull population audit testing for a retail bankTen percent sample coverage could become a full population review, in less time than the sample used to take.Learn more

SME and retail credit underwriting — workflow screenshot

Banking · GlobalSME and retail credit underwritingAn eight day wait for a credit decision could become the same afternoon.Learn more

First notice of loss, automated across channels — workflow screenshot

Insurance · IndiaFirst notice of loss, automated across channelsA claim reported today could be classified and routed within minutes, not days.Learn more

New business quoting for a commercial brokerage — workflow screenshot

Insurance Brokerage · GlobalNew business quoting for a commercial brokerageA three day wait for a quote could become something you hand a client before the call ends.Learn more

See more use cases

This is what the solution looks like from where you sit.

Not a diagram. Three surfaces you'd actually open, in the order you'd actually use them: build the team, watch it work, then go under the hood when you need to.

01 · Zara, the consultant

Describe the process. Watch the team get built.

You type what you want automated. Zara interviews you the way a consultant would, catches where your SOP contradicts itself, and fills every seat with a named agent, live, while you watch.

Every seat comes with a vetted alternate
Reasoning shows up in the chat as it happens
A live cost and time estimate before anything runs

Describe the process. Watch the team get built.

02 · Zen Pilot, the runtime

Watch it run, and watch it explain itself.

Real time execution observability. Every deployed workflow, every case moving through it right now, and every decision, explained in a sentence you can actually read, not a log you have to translate.

A dashboard of every deployed workflow, versioned and live
Every case moving through the runtime, right now
Every decision explained in plain language, as it happens

Watch it run, and watch it explain itself.

03 · Zen Studio, the builder

Open the team up. Set exactly how far it's allowed to go.

Where Zara drafts by talking, Studio is where engineering opens the draft, sets the guardrails node by node, and versions it before it ever goes live.

A visible inspector per node: prompt, temperature, token limits
Guardrails you toggle, not settings buried three menus deep
Every workflow versioned, published, and reproducible

Open the team up. Set exactly how far it's allowed to go.

Every decision runs the full cycle. No exceptions.

This is what Zen Pilot is actually running when you watch a case move. Proprietary, enforced by the runtime, not a policy a workflow can quietly skip.

Source

Pulled from the system of record. Hashed at ingest. No AI at this layer, on purpose.

Process

Specialist agents reason in parallel, each one bound to a schema contract it can't quietly slip.

Adversarial

A different model tries, on purpose, to break the conclusion the first pass reached.

Release

A named human signs off. Nothing leaves the cycle without one.

The check, written where you can actually read it.

Most platforms hand you a confidence score and call it explainable. This hands you the sentence that produced it.

01
A rationale, not a score
"All KYC checks passed. Medical conditions were assessed using the MedicalLoadingCalculatorTool…" A sentence a reviewer can actually sign off against.
02
The numbers underneath it
Risk level, risk score, the exact node that produced the figure, and what it used to get there.
03
Built while the work happens
The defence file assembles live, not reconstructed from logs three months after a regulator asks.

Built for the scrutiny BFSI demands.

Every decision carries the regulation it was tested against, the rule that decided it, and the adversarial record that tried to break it.

SOC 2

GDPR

ISO 42001

Our Global Business Partners

stripe

In the Spotlight

Product news, conference write-ups, and where agentic AI in BFSI is actually going — straight from the team.

Latest

Deepfakes, fake CEOs, fake love: the new age of AI crimeZentis AI joins the Insurtech Clinic on the future of agentic AI in insuranceBFSI has spent years testing automation. Now it finally worksZentis AI at the Insurance Innovators Summit 2025Why explainability is becoming the backbone of trust in financial AIAI and India's Viksit Bharat vision: can startups be the game-changers?Building AI systems regulators can trustAgentic AI in BFSI: From Static Systems to Self-Directed IntelligenceDeepfakes, fake CEOs, fake love: the new age of AI crimeZentis AI joins the Insurtech Clinic on the future of agentic AI in insuranceBFSI has spent years testing automation. Now it finally worksZentis AI at the Insurance Innovators Summit 2025Why explainability is becoming the backbone of trust in financial AIAI and India's Viksit Bharat vision: can startups be the game-changers?Building AI systems regulators can trustAgentic AI in BFSI: From Static Systems to Self-Directed Intelligence

16 Dec 2025NewsDeepfakes, fake CEOs, fake love: the new age of AI crimeFor most people AI has meant convenience and productivity. A quieter shift is underway, and the same technologies are being turned the other way.Read story

28 Nov 2025NewsZentis AI joins the Insurtech Clinic on the future of agentic AI in insuranceZentis AI proudly participated in the latest Insurtech Clinic, a forum created to help insurtech founders strengthen their product narratives, validate their market fit, and receive direct feedback from industry leaders. The session brought together experts with deep experience across underwriting, claims, audit, and core insurance operations. Representing Zentis AI, Deviprasad Thrivikraman, Managing Director, sharedRead story

25 Nov 2025NewsBFSI has spent years testing automation. Now it finally worksAutomation in banking and insurance has been stuck in a loop of pilots and proofs of concept. What broke the loop.Read story

All news from the newsroom

Frequently Asked Questions

Is this for business teams or engineering teams?

Both, at different points. Zara builds the agent team through conversation with the person who owns the process, and hands back three controls they can operate: behaviour sliders, agent pairing, and model choice per agent. Engineering owns integration, deployment, and the model layer.

How is this different from a workflow tool with an LLM in it?

What happens when a model is wrong?

Can a run from six months ago be reproduced?

Does anything leave our perimeter?

Which regulators are covered?

How long does a new solution take?

In the Spotlight

Product news, conference write-ups, and where agentic AI in BFSI is actually going — straight from the team.

Latest

Transition from static task automation to self-governing AI agents, built natively to uphold rigorous institutional safety, compliance, and enterprise trust

The Agentic OS for Regulated Industries

Transition from static task automation to self-governing AI agents, built natively to uphold rigorous institutional safety, compliance, and enterprise trust

The Agentic OS for Regulated Industries

Built for the work that can't afford to be wrong.

Make faster credit decisions without sacrificing accountability. Confidence in every credit decision, today and years later.