One solution for policy, payouts and performance.
PayPrompt digitizes your payout policy, however fragmented, runs it at volume, and shows every agent, agency and dealer exactly where they stand. Rules change in days without engineering. Every calculation carries its audit trail.
From payout processing to performance governance.
Most payout setups are administrative engines: they compute what was earned, eventually. PayPrompt runs the payout, then uses the same policy logic to run performance. It sits on top of your LOS, LMS, CRM and HRMS. Nothing gets ripped out.
Excel, scripts and frozen tools
- Policy lives in spreadsheets and PDFs circulated on email
- Every rule change queues behind ops and IT
- Payees find out what they earned after the month closes
- Disputes answered by reconstructing the calculation by hand
- Configuration frozen at implementation; the tool gets thrown out when policy moves
PayPrompt
- Policy modeled, versioned and changed by business teams
- Changes simulated on real data before rollout
- Every payee sees target, standing and accrued earnings live
- Every calculation carries its inputs, policy version and approver
- Policy treated as the product, built to survive change
We have been using the incentive management system developed by Osian at Tata Capital for our collections, DSA, and employee payouts. The system has streamlined our payout process, ensuring quicker turnaround times and enabling our business users to innovate on incentive plans swiftly.
Built for the way policy actually behaves.
Payout policy in lending changes several times a month, and fragments down to individual dealerships. Every system that treats policy as configuration frozen at implementation eventually gets thrown out.
PayPrompt treats policy as the product: business teams model rules, versions are tracked, changes ship in days.
- Payout TAT down from up to T+25 days to T+4
- Payout queries down 60 to 70% across implementations
- Version-controlled traceability on every rule change
Test the policy before the policy tests you.
Change pay for a thousand collectors and you change behaviour, budgets and trust in one move. PayPrompt runs the new policy against real historical data first: who earns more, who earns less, what it costs.
Roll out when the numbers say so, not when the quarter forces you.
The engine runs the payout. Then it runs performance.
Once the engine runs the policy, the same logic powers the field experience: live target-vs-actual, earnings as they accrue, nudges tied to the payout math rather than CRM noise, and contests that run off the engine instead of an ops team's weekend.
This visibility layer is rolling out on top of the production-proven engine. An agent who can see the payout for the next resolution behaves differently from one waiting for a month-end surprise.
The agent app: earnings as they accrue, contests off the engine, live target vs actual.
Sits on top of what you already run.
PayPrompt ingests from your LOS, LMS, CRM, HRMS and payment rails. Disbursal, resolution, sales and hierarchy data arrive in whatever shape those systems produce; the ingestion layer does the massaging, so a messy extract is our problem, not a blocker. It also leaves behind structured payout and performance data, the foundation for the analytics your teams keep asking for.
Written for the CFO who signs the payout file.
Confidence in the number is the product. Osian is an ISO 27001 and SOC 2 compliant company, and everything below exists so that any calculation, from any cycle, can be defended months later.
Maker-checker workflows
No payout moves on one person's say-so. Approval chains match your delegation of authority.
Immutable calculation history
Every line item keeps its inputs, formula and approver. Nothing is recalculated over.
Policy versioning
Every rule change is a new version with an effective date. Old cycles stay priced by old versions.
Audit-ready records
Records structured for internal and regulatory review, exportable per payee, per cycle, per rule.
Running where it's hardest.
Tata Capital runs sales and collections payouts on PayPrompt across employees, DSAs, agencies, call centers and RMs: 3,000+ payees, 10,000+ agencies, 500+ live policy rules. Payout TAT down from up to T+25 to T+4.
Deployed where earlier tools couldn't survive the pace of policy change.
Read the case studiesPlatform questions, answered.
How policy changes ship, what the data has to look like, and what the engine does when a payout is questioned.
How do business teams change payout policy without code?
Payout policy in PayPrompt is modeled in a no-code builder. Business teams edit slabs, rates and rules directly, scope them by geography, role and product, and publish a new version with an effective date. Changes ship in days, and old cycles stay priced by the versions they ran on.
What data does PayPrompt need, and what shape does it have to be in?
PayPrompt runs on disbursal, resolution, sales and hierarchy data from your LOS, LMS, CRM and HRMS, plus files such as CSV, Excel and JSON. Extracts arrive in whatever shape those systems produce; the ingestion layer does the cleaning and mapping, so a messy file is our problem, not a blocker.
Does PayPrompt replace our LOS, LMS or CRM?
No. PayPrompt sits on top of the systems you already run. It reads from your LOS, LMS, CRM and HRMS, computes payouts and performance against your policy, and pushes approved payouts into your ERP. Nothing gets ripped out, and no source system changes how it works.
How does maker-checker approval work for payouts?
No payout moves on one person's say-so. Every cycle passes through an approval chain that matches your delegation of authority, across maker, checker, business, HR and finance roles. Payouts can be held and unheld at transaction and invoice level, and every approval is recorded against the line item.
Can PayPrompt handle clawbacks, penalties, capping and boosters?
Yes. Clawbacks, penalties, capping and boosters are rules in the policy builder, not customizations. Business teams attach them to any policy, scope them by product, role or geography, and version them like every other rule, so each cycle can show exactly which rule applied and why.
Can every dealer or agency have its own grid and targets?
Yes. PayPrompt scopes policy down to the individual dealer or agency, each with its own grid, rates and targets. Policy that fragments by dealer is treated as the normal case, not an exception, which is the point where spreadsheet-based payout setups usually break first.
Can payouts run daily, weekly or ad hoc instead of monthly?
Yes. Payout cycles in PayPrompt are configurable: monthly, weekly, daily or ad hoc. Shorter cycles run on the same policy engine, the same approval chains and the same audit trail as monthly runs, so paying faster does not mean giving up governance.
What does the audit trail on a payout calculation include?
Every line item keeps its input data, the policy version that priced it, the formula applied and the approver who cleared it. Calculation history is immutable: nothing is recalculated over. Records export per payee, per cycle and per rule, structured for internal and regulatory review.
How do field teams and agencies see their targets and earnings?
Every payee sees target versus actual and earnings as they accrue, at daily, weekly and monthly frequencies, instead of waiting for the month to close. Nudges go out over WhatsApp, email and SMS, and contests with leaderboards run off the same payout engine.
What happens when a payee disputes a payout?
Queries are logged against the line item, and the answer comes from the record: input data, policy version, formula and approver. Adjustments and out-of-policy overrides are logged too. Across implementations, this visibility cuts payout queries by 60 to 70%.
Bring one policy. We'll bring it back running.
A working POC on your own data and your own rules, typically inside 10 days.