{"id":154896,"date":"2026-06-16T09:21:10","date_gmt":"2026-06-16T17:21:10","guid":{"rendered":"https:\/\/xira.com\/p\/2026\/06\/16\/blackboiler-launches-veris-pairing-its-deterministic-redlining-with-generative-ai-in-microsoft-word\/"},"modified":"2026-06-16T09:21:10","modified_gmt":"2026-06-16T17:21:10","slug":"blackboiler-launches-veris-pairing-its-deterministic-redlining-with-generative-ai-in-microsoft-word","status":"publish","type":"post","link":"https:\/\/xira.com\/p\/2026\/06\/16\/blackboiler-launches-veris-pairing-its-deterministic-redlining-with-generative-ai-in-microsoft-word\/","title":{"rendered":"BlackBoiler Launches Veris, Pairing Its Deterministic Redlining With Generative AI in Microsoft Word"},"content":{"rendered":"

BlackBoiler, a company that has spent over a decade building automated redlining technology, this week launched Veris, a new platform that takes its original deterministic editing engine and supercharges it with generative AI and an agentic, chat-based interface. Running directly inside a Microsoft Word add-in, Veris allows contract-review teams to negotiate and mark up agreements [\u2026]<\/p>\n

BlackBoiler<\/a>, a company that has spent over a decade building automated redlining technology, this week launched Veris, a new platform that takes its original deterministic editing engine and supercharges it with generative AI and an agentic, chat-based interface.<\/p>\n

Running directly inside a Microsoft Word add-in, Veris allows contract-review teams to negotiate and mark up agreements without ever leaving the document.<\/p>\n

Alongside the launch, BlackBoiler is rolling out two new subscription tiers aimed at expanding its reach beyond its traditional enterprise base. The Starter tier, designed for solo reviewers, runs $1,250 a year. The Pro tier, built for recurring team reviews, costs $3,000 per user per year.<\/p>\n

In a demonstration for LawSites, Daniel Broderick<\/a>, co-founder and CEO of BlackBoiler, said that Veris was developed as a response to a recurring request from customers. While they valued the precision and consistency of BlackBoiler\u2019s data-driven editing, he said, they wanted it delivered through a more interactive experience and with faster setup.<\/p>\n

\u201cThey wanted that in a more agentic experience with faster onboarding,\u201d he said.<\/p>\n

Combining Two Approaches<\/h3>\n

BlackBoiler\u2019s original system predates the current generation of large language models (LLMs). Instead of relying purely on predictive text, it uses an organization\u2019s historical data \u2014 actual examples of past contract markups \u2014 to drive its edits.<\/p>\n

Veris retains that deterministic foundation while incorporating LLMs \u201cwhere necessary,\u201d Broderick said. It includes strict controls over what data is sent to the models, backed by a robust validation layer to fact-check the output.<\/p>\n

That validation process is a core featue of Veris. The system statistically analyzes every suggested edit, measuring how dramatically a sentence changes and tracking specific word additions or deletions. It then cross-references those changes against similar edits BlackBoiler has processed in the past.<\/p>\n

The goal is to limit hallucinations and to \u201conly edit what needs to be edited and not more than what needs to be edited,\u201d Broderick said.<\/p>\n

Robert Moore<\/a>, BlackBoiler\u2019s director of sales, said the determinism is what distinguishes Veris\u2019s approach from using an LLM alone.<\/p>\n

He noted that two people giving the same input to a general-purpose model can receive different outputs, whereas Veris grounds edits in a company\u2019s own standard. A \u201cjudge\u201d component validates a suggested edit by tracing it back to the examples a customer provided.<\/p>\n

Automating Playbook Setup<\/h3>\n

The faster onboarding that Veris enables comes largely from its ability to automate the work of building a playbook \u2014 the master set of rules that governs how an organization wants contracts edited. Broderick said the company has automated a curation step that previously required involvement from humans employed by BlackBoiler.<\/p>\n

In the demonstration, Broderick built a playbook by uploading a single marked-up contract and having the system extract rules from it. Users can also do this by uploading a policy document or a written description of how they want to handle specific risks, or they can simply describe a rule directly.<\/p>\n

In the demo, Veris pulled roughly 20 rules from the sample document, displayed matching rules from BlackBoiler\u2019s master rule libraries where they existed, and let the user accept, reject or revise each one.<\/p>\n

From there, Veris runs an \u201cenhancement loop\u201d entirely in the background. For each rule, Veris:<\/p>\n