A former mergers-and-acquisitions lawyer at Greenberg Traurig today launched useABI, a legal AI platform for M&A lawyers that is designed to function not as just another AI assistant but as something closer to a working member of a deal team.
“With useABI, an M&A lawyer gets the purchase agreement markup, issues list, diligence report and diligence summary for a new deal in about 30 minutes,” said Abdulrasheed Ijaodola, the company’s cofounder and CEO. “The issues list comes back in under five minutes, at the quality you’d expect from a law firm associate, with no prompting.”
The platform also functions as a collaboration hub for partners, associates and specialists, maintaining the deal’s documents, issues, decisions and institutional knowledge.
“It’s multiplayer, so the whole deal team, partners and associates, works with the same associate on the same deal,” Ijaodola said.
Cofounders Adam Usman and Abdulrasheed Ijaodola.
Ijaodola most recently practiced M&A at Greenberg Traurig in New York, where, he told me, he closed 45 deals worth more than $15 billion, principally for private equity clients. He left the firm in July to focus on the company.
His cofounder Adam Usman, the company’s chief technology officer, has more than a decade of experience developing software and data systems, including in banking and financial services.
The two, both natives of Nigeria, met at a games night in New York. When Ijaodola told Usman about the product he was struggling to build, Usman followed up the next day with a 40-page architecture document.
During an exclusive briefing and demonstration last week, Ijaodola and Usman walked me through the product’s major functions, including its deal workspace, automated diligence review, issues-list generation, contract markups and tools for extracting knowledge from previously completed transactions.
They said that the product received its first major deployment last week in a pilot at an unnamed Am Law 100 firm, following multiple rounds of evaluation by the firm’s M&A attorneys.
From AI Tool to AI Teammate
At Greenberg Traurig, Ijaodola told me during our demonstration, he became frustrated that, despite having access to increasingly sophisticated legal AI products, none functioned as the equivalent of an experienced associate to whom he could assign work and expect a finished result.
“Everybody had an AI tool that could do all sorts of things, but nobody had a teammate,” he said. “Nobody had an associate that could produce work product without prompting, the way an associate on the team does.”
What those tools lacked, he believed, was the practical, contextual judgment that an M&A lawyer develops through handling transactions.
When a partner assigns an associate to prepare an issues list, the partner does not need to explain what an issues list should contain, how issues should be prioritized, or what positions the firm would typically recommend for that type of transaction.
Why not have AI behave similarly, Ijaodola wondered. It was not enough, he thought, just to give lawyers AI tools that they then had to adapt to each task. He wanted to give them a tool that already understood their work and could produce the appropriate deliverable.
To achieve that, he and Usman built useABI with a proprietary intelligence layer that incorporates knowledge of M&A practice and decision-making processes.
While the product uses frontier models from OpenAI, Anthropic and Google for their information-processing capabilities, it does not rely on their general legal knowledge alone, which the founders say is what enables it to generate work product tailored to M&A.
“This is not Claude giving the answer or ChatGPT giving the answer,” Ijaodola said. “We use the LLM as a processor. The intelligence is ours.”
A Workspace for Deals
In the demonstration, Ijaodola began by creating a new transaction, which automatically generated a dedicated deal workspace. Within that workspace, lawyers can add other team members, upload documents, establish transaction parameters and access the work product useABI generates.
The platform supports both buy-side and sell-side transactions. Lawyers can specify details of the deal such as the purchase price, whether the transaction involves a private equity sponsor or strategic buyer, financing arrangements, equity rollovers and other considerations.
Alternatively, they can upload a letter of intent, and the platform can extract relevant information to populate the deal context.
That context becomes the foundation for subsequent work. If, for example, the lawyer requests a markup of a purchase agreement, useABI takes into account the transaction structure, the client’s position, the firm’s preferences and information gathered during diligence.
It can also ask the lawyer targeted questions before generating the markup, such as whether to take an aggressive negotiating position on a particular issue or how to address a transaction-specific provision.
“Hi, I’m your associate and will be assisting you with this markup,” it prompted during our demo. “Do you have any initial positions or instructions for this markup?”
Lawyers can answer those questions in natural language or skip them and instruct useABI to use their team’s default positions.
These defaults are part of useABI’s “judgment layer,” which allows individual firms and teams to configure it according to their preferred approaches to M&A work.
During onboarding, firms answer a series of questions designed to capture how they handle particular transaction issues. The resulting configurations shape the platform’s recommendations and document generation.
The objective, Ijaodola said, is to give each firm an AI associate that reflects its own practices rather than simply applying generic negotiating positions.
From Documents to Deliverables
A central feature of useABI is its ability to generate many of the deliverables that M&A lawyers routinely prepare, including purchase agreement markups, issues lists, diligence reports, diligence summaries, supplemental information requests and transaction checklists.
During the demonstration, Ijaodola had the platform generate an issues list from a purchase agreement, which it finished in under two minutes. The list did not merely flag potentially problematic provisions. Rather, it presented the issues and suggested positions and explanations for why particular terms warranted attention.
Preparing a comparable issues list manually might take an associate three or four hours, Ijaodola said. Even allowing another 30 minutes for attorney review of useABI’s AI-generated list, the time savings could be substantial, he said.
The platform also supports the back-and-forth of negotiations. Lawyers can upload revised purchase agreements or redlines and generate updated issues lists identifying changes, newly introduced provisions and outstanding negotiating points.
For agreement markups, the system can incorporate instructions from the lawyer and produce a marked-up document reflecting the client’s desired positions.
Automated Due Diligence
Another component of useABI is a diligence workspace designed to process large volumes of transaction documents.
A firm can upload more than 100,000 documents, including folders exported from a virtual data room. The platform then extracts and organizes information from those documents into structured review tables.
Beyond extraction, useABI analyzes terms. If, for example, a contract contains a change-of-control provision, useABI would not simply identify the provision, but would also attempt to determine whether the planned transaction would trigger it.
The platform also generates narrative diligence reports highlighting issues across areas such as litigation, financing and commercial contracts, and it can generate a supplemental request list identifying documents or information that may be missing from the materials provided.
Ijaodola said that specialists in areas such as intellectual property, real estate and employment law can also participate in the same diligence workspace, reviewing documents relevant to their practices and contributing to the broader diligence process.
The aim, according to Ijaodola, is to connect diligence findings with the other work taking place on a transaction, so that information discovered during diligence can inform the issues list and purchase agreement markup.
Capturing Institutional Knowledge
Another notable feature of useABI, called “Insights,” is designed to make a firm’s prior M&A transactions available as a source of knowledge for future deals.
Law firms can upload purchase agreements from completed transactions, including agreements that originated outside the useABI platform.
The platform analyzes those documents across 71 categories of deal information – including transaction structures, purchase-price terms, rollover arrangements and other negotiated provisions – and lawyers can then ask natural-language questions about the firm’s historical transactions.
For example, a lawyer might ask how the firm handled a particular indemnification provision across its last 10 deals for a specific client, or what position a partner typically took on an escrow limitation.
Information from Insights can also help guide current deals. If a lawyer is preparing an issues list, for example, the system may identify how the firm previously resolved a similar issue for the same client and use that history to inform its recommendation.
The platform can also preserve decisions made during a transaction. This could help lawyers reconstruct the history of a transaction, including why a particular negotiating position was taken, even after members of the deal team have moved on.
Security and Pricing
Usman, the CTO, emphasized that security and confidentiality were priorities in developing the platform.
He said useABI has zero-data-retention agreements with its AI model providers, maintains customer data within the United States, and separates one firm’s data from another through tenant isolation and encryption.
He also said that customers can establish document-retention policies and delete uploaded material, and that company personnel cannot ordinarily access client documents without specific authorization.
The company sells the platform to law firms by subscription, priced by team rather than per user or by usage. The company has standard pricing tiers but will negotiate arrangements based on the firm’s size and intended deployment.
Although their immediate focus is M&A, the founders have broader ambitions. They plan to expand into other corporate practices, including finance, capital markets and venture capital, and they said they are developing features for sell-side auction processes and post-closing transaction management.
They also described plans for integrations with Microsoft 365 and Teams, along with voice and email interfaces that would let lawyers assign work to useABI much as they would to a human associate.
Beyond the Chatbox
useABI’s founders acknowledge that they are entering a legal AI market already populated by established companies offering contract analysis, diligence review, drafting and similar tools for M&A lawyers.
Ijaodola’s view is that the first wave of legal AI companies set themselves apart by offering secure access to models that law firms could not otherwise use, and that as the developers of the frontier models themselves move into legal, that advantage is gone.
“There is now a collapse of who can do what,” Ijaodola said. “We are all chatboxes.”
They argue that their product goes beyond existing tools in its ability to take on much of the first-pass work traditionally done by an M&A associate, freeing lawyers to focus on negotiation and legal judgment.
I am no M&A lawyer, but several aspects of useABI stood out during the demonstration. These include its organization around entire transactions rather than isolated tasks; its ability to connect diligence, issues lists and markups; its ability to be customized to a firm’s negotiating preferences; and its ability to use a firm’s historical deal data to inform current work.
For Ijaodola, it all comes down to one idea born of his own experience practicing M&A law. Lawyers need something more than another tool they must continually direct. With useABI, he believes he is providing them with an AI colleague that already knows what to do.