V7

What it is

V7 is an enterprise AI automation platform, sold as V7 Go, for work that lives in documents. Its comparison page describes it as built for "document-intensive workflows in regulated industries including finance, legal, insurance, and real estate." The reader it is written for is an operations lead, analyst or deal team at a firm that reviews contracts, filings, earnings calls or claims in volume, and that has the budget for a vendor-led rollout. It is not a tool for a founder building a first chatbot, and the pages do not say where the company is based.

It sits in the chatbot-builders category because you build agents on it without writing code. The home page puts the idea this way: "Use Agents created by Industry experts or create your own." Those agents read documents and do a defined piece of work, so it is closer to an AI analyst than a customer-facing chat widget.

What you build and how

The starting point is a library. The home page says "+280 Agents to start building with," and the names show the audience: an AI Contract Audit Agent, a Deal Agreement Analysis Agent, an AI Operational Due Diligence Agent, an AI Tear Sheet Generation Agent, a Zoning Code Analysis Agent, a Vendor Risk Assessment Agent, a Portfolio Monitoring Agent, a Broker Opinion of Value Agent and a Marketing Material Compliance Agent. There are also agents named for specific job titles, such as treasury management sales officers, vendor risk managers, transfer pricing specialists, surety bond processors, structured finance analysts, revenue recognition accountants, title examiners, reserving actuaries and property tax consultants. The comparison page, written by the company, says the platform provides "over 300 pre-built specialized AI agents," so the exact count depends on which page you read; both describe a large role-specific library.

You can also build your own. A guide on the company blog describes the approach as no-code: instead of writing Python or configuring APIs, "users interact with a visual interface to define goals, set business rules, and connect tools." The guide walks through steps for building an agent, including setting up integrations through Zapier, a no-code tool that works on triggers and actions, and then using the agent and refining it over time. It points to a video in which a V7 team member builds a simple data extraction agent. The same guide stresses a design habit that fits the product: workflows should include human-in-the-loop checkpoints, especially for high-stakes decisions where expert oversight matters, and agents should support people rather than replace them.

The comparison page gives the product's building blocks. V7 Go has "a visual workflow designer with conditional logic and branching," so you can model a review process with decision points rather than a single prompt. It also has "visual grounding that traces every AI output to its exact source location in documents," which the page describes as AI citations for compliance. The company says the platform supports 1M+ token context windows for large document bundles and reports 95-99% accuracy on document processing benchmarks. Those are the company's own figures, and the page does not describe how the benchmarks were run.

Implementation is part of the product. The comparison page describes "white-glove service with solution engineers who build custom integrations with legacy systems" and design industry-specific agents tailored to compliance requirements. This is a different model from self-serve tools where you sign up and build alone. The home page's closing call to action is to see it on your data in 30 minutes, which points to a guided demo as the way in.

Its home page frames the whole offer as "Build AI workflows on everything your firm knows. Go beyond standalone AI with the context, integrations and controls needed to run complex work from start to finish," under the heading "Precision AI for Institutional Workflows" and the line "Build once. Deploy across teams. Improve over time."

What the pages say about results, security and cost

The home page leans on customer evidence, written in the customers' words and attributed to firms or roles:

• A head of AI at an insurance brokerage said the project "would've cost us $2M and 18 months," and that V7 "had us live in six weeks."
• Alaris Acquisitions said "Our workflows were live in days, not months."
• Pinsent Masons said "We're processing hundreds of complex deal documents a week. What used to take a team now takes an afternoon."
• Star Mountain Capital, described as a $5B alt asset firm, reported "21x faster processing. 54% fewer errors," and screening 5x more opportunities with the same team.
• A real estate innovator said it replaced three separate tools and cut workflow costs by 40%.

Case summaries on the same page describe claims assessment where daily throughput rose from about 45 claims to 60 claims, a 33% capacity increase, and a credit fund that cut earnings monitoring from hours to minutes while processing 200+ earnings calls a year. These are the company's marketing claims, not independent measurements.

On security the home page lists: no training on your data, encrypted end-to-end, audited and penetration-tested, fine-grained access controls, an in-house security team, and audit logs across every workflow. It does not name a certification such as SOC 2 on the home page, and it does not give a hosting region.

Pricing is not published as a price list. The comparison page describes the model as a base platform fee plus user licenses and usage-based data processing charges, and says pricing is "tied to document volume rather than per-operation credits." No plan names, free plan or trial are listed, so expect to talk to the company to get a quote.

Who it suits and who it doesn't

V7 suits a mid-sized or large firm in finance, legal, insurance or real estate with a repeatable document process: diligence, contract review, claims, monitoring earnings calls. The ready-made agents shorten the path to a first workflow, visual grounding gives reviewers a way to check each answer against the source, and the solution-engineer service suits teams without an in-house AI group.

It is a weak fit for a solo builder, a small shop that wants a free tier, or anyone who needs a website chat widget, a form tool or a general automation app. Pricing requires a conversation, the rollout is vendor-led, and the pages describe document work rather than open-ended conversation. If your goal is a customer-facing chatbot, a simpler builder will get you there faster.