🎯 Our Approach to Accuracy
You shouldn't have to take any system's word for it — so we don't ask you to. Our approach to accuracy rests on four principles:
Traceability
Every answer is traceable back to the underlying transaction or settlement data it came from, so a human can verify it directly.
Continuous Benchmarking
Accuracy is measured against a curated set of real-world banking and payments query scenarios, and re-validated on every release.
Uncertainty-Aware Design
The system is built to recognize when it isn't confident, asking for clarification or deferring to raw data instead of guessing.
Customer-Specific Validation
Before go-live, a formal accuracy validation exercise runs against each customer's own data, with results documented and shared.
🔁 How We Validate
Accuracy isn't a one-time checkbox — it's a practice we run continuously. We maintain a curated library of real-world banking and payments query scenarios, reflecting how the platform is actually used day to day across the payments ecosystem.
Every release is checked against this library before it reaches production, and results are reviewed as part of that release process. This regression testing runs on an ongoing basis, not just at launch, so accuracy is a property we maintain release over release rather than a claim we made once and moved on from.
🧭 Designed to Know What It Doesn't Know
Financial data can be ambiguous — a query might be underspecified, or the underlying data might not support a single confident answer. Rather than fabricating a number to fill the gap, DeepSpent is designed to recognize that uncertainty and respond accordingly: asking a clarifying question when a query could reasonably mean more than one thing, or surfacing the underlying raw data so a person can make the final call.
We treat "I'm not sure" as a valid and important answer in its own right — one that protects the integrity of every other answer the system gives with confidence. This is a product philosophy that shapes how DeepSpent is built, not an exception-handling afterthought bolted on at the edges.
For a bank or fintech risk team, this matters as much as raw accuracy: a system that knows the boundaries of what it can answer confidently is easier to trust than one that always produces a number.
✅ Validation Before Go-Live
Before DeepSpent goes live with a new customer, we run a formal accuracy validation exercise against that customer's own data — not a generic benchmark, but the actual transaction and settlement data the system will be answering questions about in production.
Results are documented and shared with the customer, and reviewed together before the platform moves into production use. This gives every customer direct, first-hand evidence of how the system performs on their own data, ahead of it answering questions their teams rely on.
Run Against Your Data
Validation uses your own transaction and settlement data, not a generic sample set.
Results Documented
Findings are written up and shared directly with your team.
Reviewed Before Go-Live
Sign-off happens together, before the platform moves into production use.
❓ Frequently Asked Questions
How do you measure the accuracy of DeepSpent's answers?
We maintain a curated set of real-world banking and payments query scenarios and benchmark answers against it continuously. Every release is re-validated before it ships, so accuracy is checked on an ongoing basis, not just at launch.
What happens when the system isn't sure of an answer?
DeepSpent is designed to recognize uncertainty rather than guess. When a query is ambiguous or the data doesn't support a confident answer, the system asks a clarifying question or shows the underlying raw data so a person can make the final call.
Can we validate accuracy against our own data before go-live?
Yes. A formal accuracy validation exercise runs against your own transaction and settlement data before go-live, with results documented and reviewed with your team ahead of production use.
Do you publish specific accuracy numbers?
We don't publish accuracy figures on this page — evaluation results are specific to each customer's data and use case. Detailed, customer-specific accuracy and evaluation reports are available through a sales conversation.
How often is accuracy re-tested?
With every release. Any change that could affect how the system answers a question is checked against our benchmark scenarios before it reaches production, so accuracy is maintained continuously rather than tested once.
Can every answer be traced back to the underlying data?
Yes. Every answer is designed to be traceable back to the source transaction or settlement data it's based on, so a human reviewer can verify it directly.
✉ Get the Full Picture
Want the detailed, customer-specific accuracy and evaluation report?
Request the Accuracy & Evaluation Report
Specifics are shared under NDA as part of a sales conversation, tailored to your own use case and data.