Introduction

DataSnipper, an end-to-end agentic automation platform for audit and finance teams, was expanding its Excel Agents to take on common audit procedures such as vouching revenue, tracing batch payments, testing access controls and recalculating royalties. These agents needed to work across source documents and workbooks and produce results auditors could trace and defend.

DataSnipper is committed to not training on client data, so testing its agents on client files was never an option, and generic benchmarks wouldn't reflect the work its users actually do. The team needed expert-validated evaluation data built around real audit workflows, with scenarios that scaled in complexity and checks on both the answers and the evidence behind them.

What we Built

micro1 partnered with DataSnipper to build an evaluation pipeline powered by our data engine that produces complete synthetic audit scenarios, validated by finance experts, so DataSnipper can test its agents rigorously while keeping client data out of the process entirely.

Process

1. Scope the engagement with DataSnipper’s team, aligning on priority audit procedures, difficulty definitions, and success criteria
2. Generate synthetic files, golden answers, and rubrics from DataSnipper's scenario specifications
3. Structure scenarios into easy, medium and hard tiers based on the number of documents and the complexity of the procedure
4. Generate documents that reconcile with each other (apart from planted findings) and vary in format the way real audit evidence does, from ERP exports to scans, to invoices. Also, generate golden output files.
5. Run automated quality checks on every package before finance experts validated the answer key, evidence and rubrics

micro1 delivered

Finance experts with hands-on accounting experience, selected from ~3,000 invitees and managed through delivery.

Synthetic source documents, input workbooks, expert-validated golden outputs and grading rubrics for every task

Field-level bounding boxes that anchor each expected value to its exact location in the source document 

Tasks in DataSnipper's own scenario format, ready to plug directly into its evaluator

Key Results

micro1 helped DataSnipper test its Excel Agents on realistic, expert-validated audit work without relying on client data, extending its client data commitment from training to testing.

• Delivered an expert-validated, reusable evaluation dataset of ~100 tasks across audit and finance use cases
• Enabled DataSnipper to measure agent performance across easy, medium and hard scenarios as complexity scales
• Accelerated iteration cycles by pairing synthetically generated drafts with targeted expert validation

Enterprise Impact

DataSnipper gained a reusable evaluation set it can rerun with future releases, validated by finance professionals and designed to reflect the documents and edge cases its customers deal with in practice. Like its approach to training, none of it relies on client data.

Why This Worked

DataSnipper gained a reusable evaluation set it can rerun with every new agent release, validated by finance professionals and designed to reflect the documents and edge cases its customers deal with in practice. Like its approach to training, none of it relies on client data.

Synthetic by Design, Validated by Experts

Synthetic generation handled volume and variety, while finance experts confirmed every answer key was one an auditor would sign off on.

Consistent by Construction

Building each scenario from a shared set of entities meant documents agreed by default, and every discrepancy was intentional.

Traceability Built In

Bounding boxes tie each expected value to its source, mirroring how DataSnipper's product works and making evidence as testable as the answer.

Conclusion

Using the micro1 platform, DataSnipper built a rigorous way to evaluate its AI agents on realistic, expert-validated audit scenarios, keeping customer data out of testing just as it keeps it out of training.