Software vendor
Esker
Esker sells source-to-pay and order-to-cash software with AI agents for AP, cash application and credit; it delisted in 2025 after a tender offer.
Function
- Ownership
- Not stated
- Headquarters
- Not stated
- SEC reporting
- NoSourceSEC EDGAR company name lookups and filing histories for private and deregistered vendors in segment B
- Audit firm
- No
- Website
- Subprocessor list
- Not stated
- Contract terms
- Not stated
Offering · Software
Esker Office of the CFO suite (source-to-pay and order-to-cash) with Synergy AI Agents
Esker describes its Synergy AI Agents as an intelligence layer embedded across its source-to-pay and order-to-cash suite, with agents for accounts payable, cash application, credit management, customer inquiries, sourcing and procurement. SourceAgentic AI in Finance: Meet Esker Synergy AI Agents
Esker says the agents combine document AI, machine learning, generative AI with retrieval from business data, RPA and orchestration. SourceAgentic AI in Finance: Meet Esker Synergy AI Agents
Users can work through Microsoft Teams, Slack or chat to approve remittances or trigger workflow actions, while Esker says teams stay in control of approvals, exceptions and policy-driven actions. SourceAgentic AI in Finance: Meet Esker Synergy AI Agents
Where it fits
CoE analysis Payables and receivables on one vendor's platform, for organizations that want AP and AR agents under one governance model.
CoE analysis Approvals made through chat tools need the same evidence and segregation of duties as approvals made in the application.
What it does
- Offering type
- SoftwareSourceAgentic AI in Finance: Meet Esker Synergy AI Agents
- Workflow areas
- SourceAgentic AI in Finance: Meet Esker Synergy AI Agents
- Accounts payable
- Receivables and collections
- Spend and procurement
- Customer size
- Not stated
AI
- Kind of AI
- SourceAgentic AI in Finance: Meet Esker Synergy AI Agents
- Built into the platform
- Agents and automated actions
- Established machine learning and rules
- Suggests or acts
- Suggests and actsSourceAgentic AI in Finance: Meet Esker Synergy AI Agents
- Availability
- Generally availableSourceAgentic AI in Finance: Meet Esker Synergy AI Agents
Agents are described as current; no preview or beta label on the page read. Availability by agent or edition is not stated.
- Models named
- Not stated
Control
- Who controls it
- SourceAgentic AI in Finance: Meet Esker Synergy AI Agents
- Customer finance team
- Vendor
Commercial
- Pricing model
- Not stated
- Public price
- Not stated
Evidence
- Independent outcomes
- None found in the sources read.
- Sources read
- Vendor-stated 5
- Independent market evidence None read
- Independently verified outcomes None read
- Pricing None read
- Known limitations None read
- Coverage
- Directory record
- Last verified
Security and control
- Single sign-on
- Not covered in this record
- User provisioning
- Not covered in this record
- Role-based access
- Not covered in this record
- Administrator controls for AI
- Not covered in this record
- Audit logs
- Not covered in this record
- Certifications
- Not covered in this record
- Data retention
- Not covered in this record
- Data residency
- Not covered in this record
- Training on customer data
- Not covered in this record
AI features
As the vendor or firm describes them. Notes in small print record what the sources leave open.
Accounts Payable Agent
Gives AP teams instant access to invoice information and helps approve invoices faster.
- Kind
- Agents and automated actions
- Suggests or acts
- Suggests and acts
- Who controls it
- Customer finance team
- Availability
- Generally available
Cash Application Agent
Improves matching rates, speeds reconciliation and helps resolve remittance exceptions.
- Kind
- Agents and automated actions, Established machine learning and rules
- Suggests or acts
- Suggests and acts
- Who controls it
- Vendor
- Availability
- Generally available
Credit Management Agent
Helps teams assess and prioritize credit risk earlier.
- Kind
- Agents and automated actions
- Suggests or acts
- Suggests
- Who controls it
- Customer finance team
- Availability
- Generally available
Conversational finance (Teams, Slack, chat)
Users ask questions, retrieve information and trigger workflow actions, including approving remittances, in natural language.
- Kind
- Built into the platform, Agents and automated actions
- Suggests or acts
- Acts
- Who controls it
- Individual user
- Availability
- Generally available
Generative AI and retrieval
Summarizes communications, generates responses and recommends next steps, grounded in business data through retrieval.
- Kind
- Built into the platform
- Suggests or acts
- Suggests
- Who controls it
- Vendor
- Availability
- Generally available
Questions to ask
For this offering
- Which large language models and providers do the agents use, and where is our data processed?
- Which actions can an agent or a chat command take without an approval in Esker, and is segregation of duties enforced for chat approvals?
- How is a remittance approved in Teams or Slack recorded, and is that record exportable?
- Does retrieval draw on documents that could contain instructions to the model, and how is that risk controlled?
- Which agents are included in our edition, and how are they priced?
From the diligence question set
- DQ-02 Which AI features depend on models, data or services from third parties, and how do you assess and monitor those providers?
- DQ-06 Does each feature suggest or act? Can it create, post, approve or change records, and where is that limit enforced?
- DQ-07 Who can change each feature's configuration, prompts, thresholds, data connections or model: your company, our administrators or individual users? Can we restrict and log those changes?
- DQ-10 What data does each feature read, from which modules, documents or outside sources, and what data can it not see?
- DQ-18 Can a feature reach data or take actions beyond the permissions of the user it works for, and how is it protected against instructions hidden in the documents or data it reads?
- DQ-16 Is our data, including inputs, outputs, documents and corrections, used to train or improve any model, yours or a third party's? Is that off by default, and is the commitment in the contract?