Software vendor
Numeric
Numeric, with offices in San Francisco, offers close management, reconciliation, flux and cash tools, with AI agents that draft rules for review.
Function
- Ownership
- Not stated
Not stated in the sources read. The contracting entity is Velocity Labs Inc., a Delaware corporation doing business as Numeric; EDGAR shows no registrant under either name.
- Headquarters
- United StatesSourceLicensee Terms and Conditions (Terms)
- SEC reporting
- NoSourceSEC EDGAR company search and submissions API results for vendor names (queries listed in the excerpt)
- Audit firm
- No
- Subprocessor list
- Not stated
- Contract terms
- SourceLicensee Terms and Conditions (Terms)
Offering · Software
Numeric (close management, reconciliation, flux and cash)
Numeric describes itself as a data platform for finance teams that covers the close, revenue recognition and cash management, either alongside an existing ERP or as a replacement for it. SourceNumeric home page (The data platform for finance teams)
Its close product combines a checklist, in-platform reconciliations against the general ledger with materiality thresholds, anomaly detection and flux analysis whose explanations are auto-drafted. SourceClose Management (product page)
Numeric states that models never compute a number or draft a journal entry: agents draft rules and propose treatments for review, and approved rules do the accounting. SourceAI at Numeric (product page)
Where it fits
CoE analysis Fits the close and reconciliation work of finance teams that want a close layer on top of their ERP, or a replacement path. SourceNumeric home page (The data platform for finance teams)
CoE analysis Its own terms say AI outputs are not tested or guaranteed and must be independently reviewed, which places the control burden on the reviewer. SourceLicensee Terms and Conditions (Terms)
What it does
- Offering type
- SoftwareSourceNumeric home page (The data platform for finance teams)
- Workflow areas
- SourcesNumeric home page (The data platform for finance teams)Close Management (product page)Our Pricing Plans (pricing page)
- Close
- Reconciliation
- Treasury and cash
- Revenue and billing
- ERP core
- Customer size
- Not stated
AI
- Kind of AI
- SourcesClose Management (product page)AI at Numeric (product page)Our Pricing Plans (pricing page)
- Built into the platform
- Agents and automated actions
- Established machine learning and rules
- Suggests or acts
- Suggests and actsSourcesClose Management (product page)AI at Numeric (product page)
- Availability
- Not stated
Product pages describe the features without a release status.
- Models named
- Not stated
Control
- Who controls it
- SourcesClose Management (product page)AI at Numeric (product page)
- Customer finance team
- Individual user
Commercial
- Pricing model
- Quote onlySourceOur Pricing Plans (pricing page)
- Public price
- Not stated
Evidence
- Independent outcomes
- None found in the sources read.
- Sources read
- Vendor-stated 8
- Independent market evidence None read
- Independently verified outcomes None read
- Pricing 2
- Known limitations 1
- 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.
Agentic flux analysis
Auto-drafts flux explanations enriched with context from the finance tech stack, external files and prior periods.
- Kind
- Built into the platform
- Suggests or acts
- Suggests
- Who controls it
- Not stated
- Availability
- Not stated
Control: Not stated.
SourceClose Management (product page)Rule-drafting agents
Agents read a record, propose a treatment and bring it for review; once approved, the rule applies to similar records, and models never compute a number or draft a journal entry.
- Kind
- Agents and automated actions, Established machine learning and rules
- Suggests or acts
- Suggests
- Who controls it
- Individual user
- Availability
- Not stated
Control: A user approves each proposed rule.
SourceAI at Numeric (product page)Reconciliation auto-submission
Reads reconciliation sources, evaluates them against the GL with materiality thresholds and can auto-submit reconciliations.
- Kind
- Established machine learning and rules
- Suggests or acts
- Acts
- Who controls it
- Customer finance team
- Availability
- Not stated
Control: Users set materiality thresholds; whether auto-submission uses AI is not stated.
SourceClose Management (product page)Cash matching and bank statement parsing
Automated cash matching (vendor-stated up to 95% of bank transactions) and AI parsing of bank statements for cash reconciliations.
- Kind
- Established machine learning and rules, Built into the platform
- Suggests or acts
- Acts
- Who controls it
- Not stated
- Availability
- Not stated
Control: Not stated.
SourceOur Pricing Plans (pricing page)App Studio and Numeric MCP
App Studio plans, writes and compiles apps against live financial data from a plain-language request; the MCP exposes financial data to external models.
- Kind
- General-purpose assistant, Agents and automated actions
- Suggests or acts
- Not stated
- Who controls it
- Individual user
- Availability
- Not stated
Control: Not stated beyond the user making the request.
SourceAI at Numeric (product page)
Questions to ask
For this offering
- Which model providers process our data for flux drafting, rule drafting and App Studio, and where is the subprocessor list published?
- When reconciliations are auto-submitted, what logic decides submission, who is recorded as preparer, and can a reviewer still be required?
- How is an approved rule versioned, and can we see every record a rule was applied to after approval?
- Section 2.6 of the terms says AI outputs are not tested or guaranteed; what validation evidence will Numeric provide before we rely on auto-drafted flux explanations?
- Is our data, including corrections to drafted rules, used to train or improve any model?
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-09 Is each output generative or deterministic, what does any confidence score mean, and can a past output be reproduced and explained?
- 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?
- DQ-27 For reconciliation and matching: what rules and tolerances govern automatic matches, can a match be undone, and how are unmatched and partly matched items shown?
- DQ-30 For close commentary and reporting: are the figures in generated variance explanations, narratives or charts calculated by the system or produced by the model, and can each statement be traced to the records it rests on?