Cognaize vs V7 Go (2026)
The decision is whether your documents leave the building. Cognaize downsizes and fine tunes finance specific models so they run on premise or in a private cloud, which means credit agreements, loan applications and trustee reports need never reach the vendor at all, and it grades A on deployment and data residency and A on safety and data stewardship in the AI FinTech Index. V7 Go routes each task to whichever frontier model performs best on it, which is a real capability argument and also means customer documents traverse infrastructure the vendor does not own; it grades C on both, and the reason is architectural rather than careless. Neither names a regulator, statute or instrument anywhere, and both grade C on regulatory status while their customers run rating inputs, underwriting and onboarding identity checks on the output. If the documents cannot leave your estate, this is not a comparison. If they can, V7 Go buys you a workflow designer your own analysts configure themselves.
- The documents cannot leave your estate. Models are downsized and fine tuned to run on premise or in a private cloud, which the company links directly to accuracy and security, and most platforms in this segment cannot offer that because their architecture depends on external model interfaces.
- Your definitions differ from everyone else's. A separate class of verifier models validates extracted values against your institution's own definitions and rules before a person sees them, which a generic accuracy check cannot do, since the same figure means different things at different firms.
- The material is genuinely hard. Coverage aims at credit agreements, financial reports, ESG disclosures, loan applications, regulatory filings and trustee reports rather than standardised forms, on finance specific models trained across more than 1.3 million financial documents with human experts in the loop by design.
- Your analysts should build the workflow rather than file a ticket. Users assemble production lines visually without engineering, setting confidence thresholds and routing rules that push uncertain extractions to a person, with human review available as a configurable node rather than an improvised process.
- You want model choice treated as an empirical question. The platform routes between multiple frontier providers depending on which performs better on a given task, and the company discusses that comparative performance publicly rather than committing the buyer to one provider's behaviour.
- Every output has to be checkable against the page. Citations trace each result visually back to its exact location in the source document, so a wrong extraction is findable by the person relying on it rather than remaining latent inside a spreadsheet nobody reopens.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. Cognaize and V7 Go 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
| Cognaize | V7 Go | |
|---|---|---|
| Primary category | Capital Markets & Research AI | Capital Markets & Research AI |
| Founded | Not published | Not published |
| Headquarters | New York, New York, United States | London, United Kingdom |
| Website | www.cognaize.com | www.v7labs.com |
Side by Side
| Axis | C Cognaize |
V V7 Go |
|---|---|---|
| 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
Cognaize
Cognaize extracts decision ready information from complex unstructured financial documents including credit agreements, financial reports, ESG disclosures, loan applications, regulatory filings and trustee reports, for banks, insurers, asset managers, data providers and credit rating agencies. It combines neuro symbolic agents, finance specific language models trained on more than 1.3 million financial documents, and a distinct class of verifier models that validate extracted values against each institution's own definitions. The AI FinTech Index grades it A on deployment and data residency, A on safety and data stewardship, A on autonomy and oversight, A on model risk management and A on operational evidence, documenting six of the nine regulatory axes the index tracks. Its models are downsized specifically so they can run on premise or in a private cloud, which most platforms in this segment cannot offer. Regulatory status, security certifications, governance and bias, and commercial transparency are graded C.
Source: AI FinTech Index, 2026
V7 Go
V7 Go is an agentic document workflow platform sold into document heavy industries with private markets, insurance and finance among its named verticals, covering information memorandum analysis, diligence questionnaire completion, portfolio monitoring, filing analysis and onboarding extraction. The AI FinTech Index grades it B on autonomy and oversight, B on model risk management, B on liability and recourse, B on model supply chain and B on operational evidence, documenting four of the nine regulatory axes the index tracks. Users assemble workflows visually without engineering, setting confidence thresholds and routing rules that send uncertain items to human review, and every output carries a citation tracing it to its exact location in the source document. The platform routes between multiple frontier models by task, and names those providers. GLBA posture, safety and data stewardship, regulatory status, security certifications and deployment residency are each graded C.
Source: AI FinTech Index, 2026
Common questions
Is Cognaize better than V7 Go for document extraction?
The architectures decide it before the features do. Cognaize builds finance specific models small enough to run inside your own environment, aimed at the hardest financial material, credit agreements, trustee reports, ESG disclosures and regulatory filings, with verifier models checking extracted values against your own definitions. V7 Go is a configurable agentic workflow platform that routes tasks to whichever frontier model performs best, spanning private markets, insurance and onboarding alongside four other industries. If your constraint is that documents must not leave your estate, Cognaize is the shortlist. If your constraint is that analysts must be able to build and change workflows themselves without engineering, V7 Go is. 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 much do Cognaize and V7 Go cost?
Neither publishes pricing, packaging or a basis of charge. The unit question is unusually open at V7 Go, because a customer builds their own workflows and can run anything from one extraction agent to a production line across hundreds of thousands of documents, and nothing indicates whether charge falls on pages, documents, agent runs, model tokens consumed or seats, a range wide enough to change whether the platform suits a small team at all. Cognaize is candid about why buyers hesitate, naming long proof of concept cycles and costs, without publishing terms. 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.
Can either one run inside our own infrastructure?
Cognaize can and V7 Go does not describe an option. Cognaize deliberately downsizes and fine tunes its models so they run on premise or in a private cloud, with the stated reasoning that financial firms are custodians of customer data under strict regulatory guidelines, and the AI FinTech Index grades it A on deployment and data residency for that. V7 Go publishes no hosting provider, region selection, residency commitment or private deployment option, and the question is compounded because the platform routes work to multiple external model providers, so documents traverse infrastructure the vendor does not own. With a London base and most customers in the United States, transfer arrangements are plainly in play and none is described.
How does the AI FinTech Index grade Cognaize and V7 Go?
Both are graded on the same fifteen capability axes, with every grade traceable to the public artifact it was read from and the date it was verified. Across the nine regulatory axes, Cognaize documents six at A or B and V7 Go four. The AI FinTech Index does not aggregate the axes into a composite score, so no overall winner is declared. The separation is concentrated in containment: Cognaize grades A on deployment and data residency, A on safety and data stewardship and A on autonomy and oversight where V7 Go grades C, C and B. Both grade C on regulatory status, neither naming a regulator, statute or instrument.
What happens if an extraction is wrong?
Both make an error findable and neither says what it owes you afterwards. Cognaize runs verifier models that test extracted values against your institution's own rules before delivery, with human experts reviewing through the interface, so a wrong figure can be traced to the document and the step that produced it. V7 Go attaches a citation to every output pointing at the exact location in the source, so an error is discoverable by the person relying on it, with configurable review as a second catch. Neither publishes a correction, notification or restatement process for an error found after a workflow has already produced a decision, and no accuracy or error rate is published by either. 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.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Capital Markets & Research AI page.
V7 Go is one line of a horizontal document platform that also serves legal, real estate, healthcare and logistics, and one independent assessment describes those document industries as secondary to the parent company's data annotation business; the company's own published comparison against a finance specific competitor concedes it does not match that rival's premium connections to market data and private company databases.
Its reported accuracy gains come from third party review that flags several claims as lacking public customer names or independent studies, so treat them as vendor reported. Cognaize names its customer types, including two of the three biggest credit rating agencies, but identifies no individual institution.