Fragment
AcquiredFragment deploys AI agents that autonomously resolve procurement and accounts payable exceptions, such as invoice mismatches and purchase order changes, directly within a company's existing ERP systems. The platform's Autonomous Context Engine builds an operational understanding of a business's data, policies, and institutional knowledge, enabling agents to execute resolutions without requiring data migration or system replacement. Customers like Graybar, which processes roughly 3 million invoices annually with 80% requiring resolution, have seen significant reductions in manual exception handling.
- AI Agents
- Enterprise Software
- Artificial Intelligence
- Software Only
- Financial Technology
Funding
Raised to date
$56MRaised 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.
across 2 rounds
DCEW+3- Seed
- Series A
Incomplete round history. $56M of the $56M reported total isn’t matched to a known round (2 known rounds have undisclosed amounts).
Founders
Product
Problem
Enterprise procurement and accounts payable workflows suffer from high exception rates—averaging 22% for invoices—because critical context is lost when work is distributed across shared services, global teams, and disconnected systems. Resolving these exceptions requires time-consuming cross-functional investigations that span multiple countries and systems, driving up operational costs and slowing cycle times.
Solution
Fragment deploys AI agents that autonomously resolve invoice exceptions, PO mismatches, and supplier disputes by learning from a company's existing systems, documents, and tribal knowledge. The platform's Autonomous Context Engine (ACE) builds operational understanding across ERP, procurement tools, and institutional expertise, enabling agents to reason and execute resolutions deterministically within the system of record. Fragment layers on top of existing infrastructure without requiring re-architecture or data migration, and its Continual Learning Engine improves resolution accuracy over time by tracking supplier reliability, approval patterns, and root causes of exceptions. The platform delivers measurable results within weeks, with customers seeing roughly 50% reductions in exceptions and rework and $5-10M of costs impacted on average.
Target Audience
Primary customers are enterprises running source-to-pay operations through shared services, global business services, or BPO models, particularly procurement, accounts payable, and finance leaders seeking to reduce exception handling costs and cycle times.
Features
- Autonomous Context Engine that builds understanding across ERP, procurement tools, documents, and undocumented team knowledge
- Agents execute resolutions directly within the system of record rather than merely suggesting next steps
- Continual Learning Engine that reinforces successful resolution strategies and recognizes new exception patterns over time
- Governed, explainable actions with complete audit trails showing the rationale behind every automated decision
- SOC 2 compliant with production-grade reliability, redundancy, monitoring, and failover built in from day one
- No rip-and-replace deployment that connects to existing systems without changing how they work
- Handles from ten to ten million transactions without performance degradation or added infrastructure