Standard Forensics provides an AI execution layer for disputes and investigations, enabling practitioners to direct AI agents that plan, execute, and verify analytical work under human supervision. The platform emphasizes defensibility with full audit trails, human-readable scripts, and outputs traceable to their source, allowing one practitioner to manage multiple workstreams simultaneously.
Funding
Funding not disclosed
Founders
Product
Problem
Disputes and investigations are high-stakes, deadline-driven, and unjustifiably manual. Productions arrive incomplete, scripts are rebuilt, and quality control takes longer than the analysis it covers, while ad hoc client requests strain project resources. Patterns worth testing and threads worth pulling often go unpursued, leaving the limitations slide longer than the findings.
Solution
Standard Forensics provides an AI execution layer for disputes and investigations, where agents plan, execute, check, and package analytical work under human direction and review. The platform lets one practitioner direct a team of agents with a few sentences, increasing capacity and enabling teams to pitch work they would otherwise pass on. Agents pause when judgment is required, allowing users to adjust scope, test theories, and substantiate claims without stopping work. Every output traces to its source, scripts remain human-readable, and agents verify from multiple angles, ensuring full audit trails that are reviewable as work happens.
Target Audience
Primary users are forensic investigators, data analytics teams, and legal professionals handling disputes, investigations, and regulatory matters who need defensible, auditable analytical workflows.
Features
- AI agents that plan, execute, check, and package analytical work under human direction
- Human-readable scripts and outputs traceable to their source for defensibility
- Full audit trails reviewable in real time, suitable for multiple audiences including counsel and regulators
- Natural-language direction lets one practitioner manage multiple agents and workstreams
- Network-isolated execution of customer-data analytical code for security
- No use of customer data for AI/ML training, with encryption in transit and at rest