Make your AI pay
Trim the estate: duplicate tools, idle licences and AI nobody owns. Every rupee goes to the systems that earn it.
The AI control platform for regulated financial institutions
Know which AI pays.
Know which AI pays.
Cut the rest.
Prove control.
Every regulated lender and financial institution is using AI. RoTrust shows what each system costs, what it earns and what it risks, so you can back the winners and stop paying for the rest.
For CFOs, COOs, CROs and CISOs.
AI ledger · per yearSample · fictional lender
AI systemCostsEarnsOur call
Credit scorecard v4ML model · Credit risk
₹2.4 Cr
₹21 Cr
✓ Back it
Customer chat assistantLLM app · Consent gap found
₹3.8 Cr
₹8.6 Cr
✕ Fix first
Offer ranking modelML model · Growth
₹2.2 Cr
₹1.6 Cr
◐ Review
Meeting-notes assistantShadow AI · No owner
₹1.4 Cr
₹0
✕ Cut it
You could save₹2.0 Cr a year
Cut the meeting-notes assistant (₹1.4 crore). Fix or drop the offer model, which costs ₹60 lakh more than it earns.
01 · The question
Everyone is using AI. Who is getting paid back?
The bill comes every month. The proof does not. Until each AI system has a ledger, you are guessing which ones to keep.
Get your AI X-rayOnly 13%
of CIOs say their enterprise has realised significant or transformational value from AI.
Gartner global survey of CIOs and technology executives, published 19 October 2026.Only 12%
of organisations consistently assess AI value against what it costs.
KPMG Global AI Pulse Q3 2026: 2,131 senior leaders, 20 countries.₹40.9 crore
Average cost of a data breach in Indian financial services. Unsanctioned AI adds about ₹1.79 crore to a breach across India.
IBM Cost of a Data Breach Report 2026, India.
02 · Know which AI pays
You can't see what each AI earns. Now you can.
One ledger for every AI system. What it costs. What it earns. What to do next.
- Cost. Every AI you pay for: SaaS tools, cloud AI, vendor features and models you build. Plus the people who run them.
- Earns. The extra money it brings in or saves, compared with what would have happened without it.
- Risk. Open gaps and live alerts, priced in.
Offer ranking modelCustomers who took another loan · sample
Earns₹1.6 Cr
Costs₹2.2 Cr
Net−₹60 L
Our call◐ Review
4 more repeat loans in every 100 customers is worth ₹1.6 crore a year. The model costs ₹2.2 crore, so it loses ₹60 lakh. One open gap. Sample · fictional lender.
03 · Keep the return
Earn more. Spend less. Stay in control.
Three jobs. One platform. One record behind all of them.
Let agents do the legwork
AI agents find systems, gather evidence and chase owners. Your team reviews and approves, instead of doing it by hand.
Enforce your rules, live
Kavach, our enforcement layer, checks every AI action against your policies, regulator rules and customer consent. Watchdog, our monitor, flags drift and vendor incidents.
Kavach in action
A customer says no. The offer does not go out.
Kavach sits in front of your AI and checks every action against three sets of rules. It can alert you or stop the action.
- Your rules. Approved uses, limits and who may act.
- Regulator rules. Applied as rule packs you can update.
- Customer consent. Checked against purpose at the moment of action.
- Plus guardrails for chat and voice bots, and a kill switch for any system.
Scenario · fictional lender
10:02:11
Consent record
Customer withdraws consent for promotional calls and messages.
10:04:37
Customer chat assistant
Queues a promotional top-up message to the same customer.
10:04:37
Kavach check
Checked: your policy, regulator rule, customer consent. Consent for promotion: withdrawn.
Stopped. The message is held.
The reason is logged. The owner is alerted. The customer never sees the offer.
04 · How it works
Find. Measure. Protect. Prove.
Four steps on one record. A named person approves anything that changes your systems.
Find
DiscoveryEvery model, bot, agent and vendor feature, including the ones nobody told you about.
Measure
AI ledgerCost, value and risk for each, in numbers your CFO can check.
Protect
Kavach and WatchdogConsent checks, guardrails and drift alerts run in front of your AI.
Prove
EvidenceOne evidence pack, ready for whoever asks.
↺Every result sharpens the next assessment. New systems and changes go round again.
05 · Prove it
Prove it to anyone who asks.
The same evidence answers every one of them. Rule packs for RBI FREE-AI and DPDP are included. Your own policy loads as a pack of its own.
Is our AI paying its way, and is it under control?
Show me the list and the evidence.
How do you govern AI that touches our customers?
What AI do you run, and what does it risk?
Which rules apply, and are you meeting them?
- Each record shows what was decided and why, in plain words.
- Lineage shows where the data came from and where it went.
- Packs export for the board, audit and supervisory review.
- Record
- EV-2041 · sample, fictional data
- Asset
- Customer chat assistant
- Owner
- Head of customer service
- Rule
- Consent must cover the purpose
- Decision
- Stopped by Kavach
- Reason
- Promotional message queued after consent was withdrawn
- Time
- 10:04:37 IST
- Lineage
- Consent store → Customer chat assistant → Message queue
Start here
Get your AI X-ray.
A read-only scan of the AI you run. You get back one view of what is there, what it costs, where the gaps are and which systems look worth backing.
- A list of every AI system, found and owned.
- What each one costs, and a first view of what it earns.
- The gaps a reviewer would raise, ranked.
Prefer a walk-through first? Say so in the note below.