Transforming due diligence, from initial request to client-ready report

M&A due diligence has always been one of the most time-consuming disciplines in legal practice. It requires large teams to process enormous volumes of documents at pace, without missing anything that could affect the deal. And yet the tools most firms have used to manage this process have changed very little, email threads, spreadsheets, shared drives, and hours of manual document review.

The consequences are well documented. Research by Thomson Reuters found that 10–25% of due diligence time is typically written off by law firms, and with growing deal complexity the process now takes nearly twice as long as it did a decade ago.

This is the problem that HighQ‘s due diligence guided workflow (DDGW) was designed to solve. Not with generic document review AI, but with a purpose-built, legal expert-guided AI powered workflow that reflects how M&A lawyers actually work, from the moment a deal room is set up through to the delivery of a partner-ready due diligence report.

In this blog, we break down the four pillars of the due diligence guided workflow solution within HighQ and explains what each one delivers for your deal team and clients.

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Deal Room Command Center

Embedded prompts to risk profiles

Visualize, collaborate, and report

What sets DDGW apart

Deal Room Command Center

From fragmented admin to a single command center

Ask any M&A associate what the first few days of a due diligence instruction look like, and the answer is remarkably consistent: hunting for documents, chasing the seller for missing materials, cross-referencing request lists against what has actually been provided, and trying to keep the whole team aligned across email chains and shared spreadsheets. This is not legal work. It is administration, and it absorbs a disproportionate share of the time that clients are paying for or sometimes refuse to pay.

A workspace built on the platform firms already trust

DDGW deal rooms are created within HighQ, the platform firms already use to manage transactions and collaborate. HighQ gives deal teams a central, secure deal room connected to collaboration features, and DDGW provides fiduciary grade AI custom built for due diligence work.

Setup is fast. A fully configured deal room can be up and running in a single working session. The process involves creating the workspace in HighQ, linking it to the guided workflow, and either uploading your firm’s preferred document request checklist or selecting one from Practical Law’s ready-to-use templates. Team permissions are set once, and the deal is live.

The user interface organizes active matters as a tile-based dashboard, giving every team member immediate visibility of their current deals, the status of each, and any pending actions since they last logged in using HighQ’s native legal project management capabilities.

Automated document classification and completeness checking

Once documents begin to populate the deal room, DDGW’s AI automatically classifies them into practice area categories: Commercial Agreements, Corporate, Real Estate, and more. Surfacing the structure of the deal room without any manual sorting. It then performs the task that has traditionally consumed significant associate time at the start of every instruction: the completeness check. The system compares every document in the deal room against the request list, produces a structured report of missing documents, and critically identifies missing cross-references: documents that are referenced within existing materials but have not been provided. This is the kind of gap that is easy to overlook when reviewing hundreds of files manually, and that can result in incomplete risk analysis downstream.

Seller communication, automated

Having identified the gaps, DDGW drafts the document request email to the seller. The deal team reviews and approves the draft before anything is sent, lawyers remain in control of all external communications, but the time spent compiling the list, writing the email, and tracking responses is eliminated.

Embedded prompts to risk profiles

Why expert-guided questions change everything

Most legal AI tools ask users to write their own prompts. The output is only as good as the question asked, and that varies by experience, time pressure, and the user’s familiarity with the issues. The result is inconsistent analysis across the team. DDGW is built on a different principle.

Practical Law expertise, embedded from the start

DDGW comes pre-loaded with expert question sets for each of its six practice areas: Commercial, Corporate, Real Estate, Employment, Intellectual Property, and Finance, curated by Thomson Reuters’ AI editors: experienced legal practitioners with deep M&A expertise who continuously update the question sets to reflect changes in law and market practice.

These are not generic prompts. They are the questions a senior Practical Law editor would ask when reviewing a commercial agreement or analysing an employment contract in the context of an acquisition. The AI is guided not by what the user happens to ask, but by what expert legal practice says should be asked, every time, across every document, for every lawyer on the deal.

The system is transparent. Before analysis begins, users can view the complete set of active questions for their chosen practice area, modify them, select a subset, or add firm-specific questions that reflect proprietary methodologies. This enables you to apply your firms own unique IP, adding layered custom questions to the analysis. Senior lawyers retain full control; junior team members benefit from an expert starting point that guards against gaps.

How the end-to-end workflow runs

Once the question set is confirmed, DDGW runs parallel analysis across every document in the deal room, not sequential review, which is what makes the time saving significant on large instructions. The workflow runs in four stages:

• Stage 1 (Deal Room Hygiene): document classification, completeness check, gap report, and seller communication as described above.
• Stage 2 (Document Analysis): AI analyses all deal room documents against the expert Practical Law question set for the selected practice area.
• Stage 3 (Risk Reporting): individual document risk profiles are aggregated into practice area risk summaries, which are then consolidated into a first-pass due diligence report in standard Practical Law format.
• Stage 4 (Collaboration): Results can be published directly into a HighQ iSheet, added to client ready dashboards and individual document risk profiles are generated.

The risks that get missed, and how DDGW surfaces them

To understand what expert-guided analysis delivers in practice, consider three examples of the risks DDGW is designed to surface:

• Change of control provisions: A junior associate may not spot that 15 supplier contracts all require third-party consent before completion. DDGW flags every affected contract instantly, giving the deal team a consolidated view of the exposure.
• Missing cross-references: The completeness check flags documents referenced within existing materials but never provided, a category of gap that manual review routinely misses under time pressure.
• Restrictive employment covenants: Under the Employment practice area, DDGW flags non-compete and non-solicitation provisions that may be unenforceable, unusually broad, or that could affect key personnel retention post-acquisition, issues with direct implications for deal structure.

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Visualize, collaborate, and report

From analysis to action: outputs the whole team can use

Most AI tools leave a gap between raw output and a usable deliverable. DDGW closes it, producing structured, source-linked, team-ready outputs that sit directly inside the deal team’s existing environment.

HighQ iSheets: the deal team’s working view

When the DDGW analysis is complete, the results are published automatically into HighQ iSheets, a configurable, interactive data table within the HighQ platform. This is the primary working output for the deal team.

The iSheets view gives every team member immediate access to the full question-and-answer dataset, organized by document, by question, or by practice area. Each answer is source-cited: lawyers can click through directly to the relevant clause in the original deal room document, which means they are not reading summaries and taking them on faith, they can verify findings, probe deeper, and apply their own judgement to the AI’s output.

Team members can filter the view, add notes to individual findings, flag issues for escalation, and update status as the review progresses. Everyone works in the same live data set, which means no version control problems, no reconciling separate reviewers’ documents, and no information lost between the analysis and the report.

Individual risk profiles and the first-pass report

Alongside the iSheets Q&A output, DDGW generates an individual risk profile for each document processed, a structured risk summary identifying key issues and risk ratings that lawyers can expand, review, and annotate within the interface.

These individual profiles feed into the first-pass due diligence report: a consolidated, practice-area report generated in standard Practical Law format. This is a lawyer-ready first draft, not raw extracted data, but a structured, narrative report that draws on all the individual document analysis and risk profiling completed during the workflow. It is the kind of output that previously required a senior associate to manually reconcile all junior team members’ work into a single coherent document. DDGW produces it automatically.

The report can be downloaded for offline work, refined by the deal team, or, via HighQ’s native Contract Express integration, used to populate a firm-branded report template, giving firms control over how the final deliverable looks without any manual reformatting.

Collaboration that keeps pace with the deal

M&A due diligence does not stand still. Documents continue to arrive from the seller throughout the process. Practice area leads need to review and sign off their sections. Partners need visibility of progress. DDGW is built to support this reality.

When new documents are added to the deal room, the system detects them automatically. Analysis can be re-run, and a new version of the report is generated, incorporating the updated materials. Outputs are never stale. The deal team’s HighQ workspace includes task allocation and notification functionality, so practice area leads receive alerts when their sections are ready for review, and the deal manager has real-time visibility of where each workstream stands.

Because everything operates inside HighQ, the platform the team is already using, collaboration is native rather than bolted on. There is no context-switching between a document review tool and a deal management platform. Analysis, annotation, escalation, and reporting all happen in the same place.

What sets DDGW apart

Purpose built for due diligence, not just document review

Law firms have more AI options than ever. The question is not whether AI can help with due diligence, but which solution is actually built for how M&A due diligence works. Three things set DDGW apart.

1. Expert-built, risk-first AI

Every analysis is guided by Practical Law expert question sets, maintained by Thomson Reuters’ AI editors with decades of M&A experience. A first-year associate and a senior associate start from the same expert baseline, solving one of the hardest problems in large-team due diligence: consistency.

2. End-to-end, one platform

DDGW orchestrates the full journey: deal room setup, document hygiene, expert-guided analysis, risk profiling, and structured reporting, without a platform switch. Competitor tools are point solutions: they process documents but do not manage deal rooms, run completeness checks, or push outputs natively into a deal management environment. DDGW is built inside HighQ and connects through Contract Express to the firm’s own report templates. No competitor can replicate this.

3. Deal room completeness check

DDGW is the only solution that performs a formal completeness check before analysis begins, comparing the deal room against the request list, flagging missing documents and missing cross-references, and drafting the seller follow-up. Analysis that starts from an incomplete deal room produces incomplete risk analysis. Harvey, Legora, and Kira do not offer this capability.

Validated by firms in production

DDGW is not a pilot or a proof of concept. It is in active production use with law firms, with further firms in active trials. A leading international law firm tested DDGW against a US deal room in a UK configuration and described the results as impressive, an important data point demonstrating that the Practical Law question sets perform robustly across jurisdictions and deal structures.

Lawyers remain the expert. DDGW helps to make them more effective.

DDGW doesn’t change what lawyers do — it changes what they have time to do. The mechanical work of due diligence has always been necessary, but it has never been where lawyers deliver their most distinctive value. That value is in the analysis: recognizing that 15 change of control clauses across a target’s supplier base could affect deal timing, or that a key executive’s non-compete is unlikely to survive challenge. DDGW handles the mechanical so lawyers have more time for the strategic, with Practical Law expertise embedded at every step, inside the platform firms already trust.

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