Infrrd vs Proof (2026)
The decision is which end of the mortgage back office you are buying for, because these two barely compete: Infrrd reads the loan file, Proof executes and seals the closing. Infrrd is extraction infrastructure, template free document processing across more than a thousand document types, with mortgage products that run federal disclosure tolerance tests at the point of upload and route exceptions to a person, a Leader placement from both Gartner and Everest Group in 2026, and a named customer, State National, whose intake spans forms from 2,100 insurance companies. Proof binds a verified identity to the transaction itself, and its two strongest positions are structural rather than claimed. Licensure sits inside the product, with commissioned notaries carrying personal liability and a state bond in a network the platform holds to each jurisdiction's notarial law, accepted across all fifty states. And its outcome evidence is quantified to the role, 157 minutes saved per closing for title agents, up to seven days off funding cycles for lenders, 185 billion dollars in closings secured. The fifteen axis grid separates them most sharply on oversight: Proof's notarial path carries a legally required human who is personally accountable for the act, while Infrrd publishes no criterion for what decides that a field is not an exception, so everything its models are confident about flows straight to delivery.
- Document intelligence is the purchase. Extraction across origination, quality control, post close and servicing, with tolerance tests under the federal disclosure rule run at upload and tamper resistant logs kept for regulators and investors, automates the reading work Proof never touches.
- You want to test accuracy before you buy. A public self serve demo lets you upload your own hardest document and see what the system reads, so your evidence is generated rather than quoted, and the service level is a published customer choice on a stated range from fifteen minutes to twenty four hours.
- Your document estate crosses lines of business. The same platform handles insurance carrier forms, claims and medical reports alongside mortgage files, with patented list splitting for multi policy documents, which suits an operation consolidating extraction onto one vendor.
- The risk you are retiring is repudiation. Cryptographic sealing binds a verified identity to the executed instrument so the transaction can be defended in court later, and in the notarial path a commissioned individual with a state bond is personally answerable, a named human and a statutory remedy that no software vendor's terms provide.
- Regulatory acceptance is the gating question. The platform encodes and enforces each state's notarial law, is accepted across all fifty states, and claims conformance to a named federal identity assurance level and the mortgage industry data standard, so eligibility conversations start from statute rather than assurance.
- You bill by the deal, not the seat. Pay per transaction pricing is published as the model even though the rate is not, which fits closing volume that moves with the rate cycle, and a free account lets a title professional create and send transactions before any commercial conversation.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Infrrd and Proof are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Infrrd | Proof | |
|---|---|---|
| Primary category | Lending & Banking Operations | Lending & Banking Operations |
| Founded | 2015 | Not published |
| Headquarters | San Jose, California, United States | Not published |
| Website | www.infrrd.ai | www.proof.com |
Side by Side
| Axis | I Infrrd |
P Proof |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
The short version of each
Infrrd
Infrrd supplies template free document extraction across more than a thousand document types, with mortgage products that run federal disclosure tolerance tests at the point of upload and route exceptions to a person, a Leader placement from both Gartner and Everest Group in 2026, and a named customer whose intake spans forms from 2,100 insurance companies. The AI FinTech Index records that it names the one to two percent human error baseline and claims better than human accuracy without publishing its own rate, that no criterion is published for what decides a field is not an exception, and that its audit trail records what was extracted rather than whether it was correct.
Source: AI FinTech Index, 2026
Proof
Proof binds verified identity to transactions and seals closings through a network of commissioned notaries carrying personal liability and state bonds, held to each jurisdiction's notarial law and accepted across all fifty states, with outcomes quantified to the role: 157 minutes saved per closing for title agents, up to seven days off funding cycles, and 185 billion dollars in closings secured. The AI FinTech Index records its oversight as structural, a legally accountable human inside the product, while noting no accuracy figure is published for its identity proofing, no route exists for the signer its checks wrongly reject, and its knowledge based challenge questions draw from consumer credit files, so thin file signers fail disproportionately.
Source: AI FinTech Index, 2026
Common questions
Is Infrrd better than Proof for mortgage operations?
They barely compete: Infrrd reads the loan file, and Proof executes and seals the closing. Infrrd is extraction infrastructure across more than a thousand document types with mortgage products running federal disclosure tolerance tests at upload. Proof binds a verified identity to the transaction, with commissioned notaries in a network accepted across all fifty states. A mortgage operation could plausibly run both at different stages of the same loan. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
What makes Proof's strongest positions structural?
Structurally, which is rarer than a claim. Licensure sits inside the product: commissioned notaries carry personal liability and a state bond, held to each jurisdiction's notarial law, so the platform's oversight includes a legally required human who is personally accountable for the act. Its outcomes are quantified to the role, 157 minutes saved per closing for title agents, up to seven days off funding cycles, 185 billion dollars in closings secured. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
What evidence does Infrrd publish?
Infrrd holds a Leader placement from both Gartner and Everest Group in 2026 and names State National, whose intake spans forms from 2,100 insurance companies. It names the one to two percent human error baseline and claims better than human accuracy without ever publishing its own rate, and its audit trail records what was extracted rather than whether it was correct. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
What is the failure mode at Proof's automated layer?
Proof publishes no accuracy figure for its identity proofing and no route for a signer its checks wrongly reject, whose closing then does not complete that day. Its knowledge based challenge questions are drawn from consumer credit files, so thin file signers fail disproportionately, which is the demographic edge of this page. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
How does Infrrd route exceptions?
Infrrd publishes no criterion for what decides that a field is not an exception, so everything its models are confident about flows straight to delivery. The question to put to each vendor in writing is the same: what decides that an automated result gets human eyes. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified August 23, 2026. No vendor pays for placement.
How does the AI FinTech Index grade Infrrd and Proof?
Both are graded on the same fifteen capability axes from public sources, each grade traceable to its artifact. The AI FinTech Index records the sharpest separation on oversight, a legally accountable human inside Proof's notarial path against an undisclosed exception criterion at Infrrd, and the shared silence as each automated layer's unmeasured failure mode. The index publishes no composite score and declares no winner.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Lending & Banking Operations page.
The shared silence is the automated layer's failure mode. Proof publishes no accuracy figure for its identity proofing and no route for a signer its checks wrongly reject, whose closing then does not complete that day, and its knowledge based challenge questions are drawn from consumer credit files, so thin file signers fail disproportionately.
Infrrd names the one to two percent human error baseline and claims better than human accuracy without ever publishing its own rate, and its audit trail records what was extracted rather than whether it was correct. Ask each vendor, in writing, what decides that an automated result gets human eyes.