Customers
Measured outcomes from five implementations
Eleven plants, eleven compliance regimes, one register
Auto components and engineering, 11 plants across 7 states
A component manufacturer replaced plant-level spreadsheets with a single scoped register, and discovered fourteen expired licences and three plants operating under the wrong state thresholds.
- expired licences found in the first pass
- 14expired licences found in the first pass
- obligations found that Central-only tracking had missed
- 2,900obligations found that Central-only tracking had missed
- to assess a new notification across all plants
- 4 wks → 3 daysto assess a new notification across all plants
- statutory roles named to people who had left
- 6statutory roles named to people who had left
Schedule M readiness turned from an opinion into a number
Formulations and API, 6 manufacturing sites, 40 export markets
A formulations manufacturer decomposed the revised Schedule M into unit-level obligations and tracked readiness per site, including at its contract manufacturers.
- unit-level obligations from one schedule
- 340unit-level obligations from one schedule
- to produce a board-ready readiness position
- 3 wks → 1 dayto produce a board-ready readiness position
- contract manufacturers found materially behind
- 2 of 4contract manufacturers found materially behind
- across quality, environment and labour
- 1 registeracross quality, environment and labour
DPDP and CERT-In delivered to engineering as backlog items
Global capability centre, 6 delivery centres, 14,000 employees
A capability centre separated its processor and fiduciary obligations under DPDP, rebuilt incident response around the six-hour clock, and pushed obligations into Jira with acceptance criteria.
- reporting clock met with escalation, evidenced
- 6 hrsreporting clock met with escalation, evidenced
- processor and fiduciary, correctly separated
- 2 registersprocessor and fiduciary, correctly separated
- labour obligations brought under one view
- 5 stateslabour obligations brought under one view
- obligation delivery to engineering teams
- Jira-nativeobligation delivery to engineering teams
The first complete list of what licence each of 420 outlets held
Food and general retail, 420 outlets, 19 states
A retail chain built its first outlet-level licence register, forecast EPR liability in advance, and moved dark patterns review into the product release process.
- outlets found operating on an expired licence
- 31outlets found operating on an expired licence
- EPR liability forecast and avoided in year one
- ₹1.4 crEPR liability forecast and avoided in year one
- outlets with a complete licence register, from zero
- 420outlets with a complete licence register, from zero
- dark patterns check, moved out of legal
- Design-stagedark patterns check, moved out of legal
Strong on RBI, weak on the Factories Act — until both were in one register
NBFC, 240 branches, 16 states
A lender brought RBI master directions, DPDP, state labour obligations and branch licensing into a single register, and found its non-financial compliance was substantially worse than its financial compliance.
- of obligations found had no named owner
- 61%of obligations found had no named owner
- branches scoped for state labour obligations
- 240branches scoped for state labour obligations
- cyber frameworks reduced to one control set
- 3 → 1cyber frameworks reduced to one control set
- alerting on master direction amendments
- Same dayalerting on master direction amendments
In their words
Shorter observations
“The first honest answer to "what applies to this plant" took us three weeks to produce manually. It now takes a click, and it is more complete than the answer we produced.”
“Our named occupier had never been shown the specific obligations he could be prosecuted for. Showing him changed how the site ran within a month.”
“We stopped arguing about whether we had seen a notification and started arguing about which of our sites it landed on. That is a much better argument to be having.”
“The copilot saying "the rules for that state have not been notified yet" is the feature that made our legal team trust the rest of it.”
A note on these numbers
Every figure here is measured from the customer’s own platform data, comparing a defined period before and after implementation, and each has been reviewed by the customer before publication. Where a number looks implausibly good it usually reflects a very poor starting position rather than an unusually good outcome, and the case study says so.
What these pages do not show is the implementations that went slowly. The most common cause is organisational data — a CMDB that turns out to be substantially wrong, or a product master whose granularity does not match how regulation distinguishes products. If you want to talk to a customer who found it harder than expected, ask us and we will introduce you.
Talk to a customer, not just to us
For serious evaluations we will introduce you to a reference in your sector with a comparable footprint — including one who will tell you where it was harder than they expected.