Trace is an AI orchestration platform that connects to existing SaaS tools and builds a live context graph of organizational entities to coordinate adaptive AI agents and human reviewers. It lets teams define and run governance‑controlled workflows—such as contract extraction or lead qualification—while providing real‑time alerts, SLA monitoring, and a full audit trail for transparent, scalable automation.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Modern enterprises rely on fragmented tools and manual processes to coordinate AI agents and human decisions across departments, leading to bottlenecks, inconsistent governance, and limited scalability of automation.
Solution
Trace provides an AI orchestration layer that connects to existing SaaS tools (e.g., Slack, HubSpot, Google Drive) and builds a context graph of entities across the organization. Adaptive AI agents execute defined workflows—such as contract term extraction, lead qualification, or invoice reconciliation—while routing outputs to human reviewers based on configurable confidence or policy gates. The platform monitors workflow health, flags SLA risks, and logs every decision for full traceability, enabling teams to scale automated work without expanding headcount.
Target Audience
Target users are operations, sales, finance, and HR teams in mid‑size to large organizations that need to automate repetitive tasks while retaining human oversight.
Features
- Context‑aware agents that ingest data from common business applications and maintain a live graph of 10K+ entities
- Visual workflow builder with built‑in governance gates (confidence thresholds, approval SLA monitoring)
- Real‑time alerts for blocked runs, overdue approvals, and policy violations
- Full audit trail and traceability of AI actions and human interventions
- Seamless embedding into existing collaboration tools, eliminating the need for separate interfaces
- Automated pre‑deployment checks to ensure critical quality standards are met