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Automating M&A Due Diligence: A Practical Guide for Private Equity Firms
A practical guide to selecting a diligence process, defining agent boundaries, validating source-backed output, and keeping investment judgment with the deal team.
Hebbia vs. Custom Deal Intelligence: What PE Teams Should Evaluate
A current, evidence-based evaluation framework for private equity teams comparing Hebbia Matrix with a firm-specific deal intelligence implementation.
Rogo AI vs. Custom Deal Intelligence: What Finance Teams Should Evaluate
A current, evidence-based framework for comparing Rogo's finance AI platform with a firm-specific agentic implementation for deal delivery.
How to Pick the First AI Workflow Worth Automating in an Advisory Firm
A practical guide for advisory, diligence, compliance, and finance teams choosing the first AI workflow to automate without creating another stalled demo.
Automating M&A Due Diligence: A Practical Guide for Private Equity Firms
A practical guide to selecting a diligence process, defining agent boundaries, validating source-backed output, and keeping investment judgment with the deal team.
July 24, 2026
Hebbia vs. Custom Deal Intelligence: What PE Teams Should Evaluate
A current, evidence-based evaluation framework for private equity teams comparing Hebbia Matrix with a firm-specific deal intelligence implementation.
July 24, 2026
Rogo AI vs. Custom Deal Intelligence: What Finance Teams Should Evaluate
A current, evidence-based framework for comparing Rogo's finance AI platform with a firm-specific agentic implementation for deal delivery.
July 24, 2026
How to Pick the First AI Workflow Worth Automating in an Advisory Firm
A practical guide for advisory, diligence, compliance, and finance teams choosing the first AI workflow to automate without creating another stalled demo.
June 22, 2026
Diligence Red-Flag Extraction: The 30-Day AI Pilot Private Equity and Advisory Teams Can Start With
A practical pilot scope for PE and transaction advisory teams that need faster data-room review without hiding source evidence or human judgment.
June 22, 2026
Evidence Sufficiency Notes: The Narrow AI Workflow Cyber Compliance Teams Can Actually Use
A specific first workflow for SOC 2, ISO 27001, GRC, and cyber compliance teams: turn messy evidence files into reviewer-ready sufficiency notes with source references.
June 22, 2026
OpenAI's Deployment Company Proves the AI Market Is Moving From Models to Implementation
OpenAI's new Deployment Company validates a market shift Dotnitron sees every week: companies do not just need model access, they need AI systems deployed inside real workflows.
May 12, 2026
AI Agents vs AI Workflow Systems: What Teams Should Actually Ask For
The word agent is useful, but teams should evaluate the full workflow system: data access, tools, controls, review, validation, and adoption.
May 10, 2026
What Is a Forward-Deployed AI Engineer?
A practical explanation of the forward-deployed AI engineering role and why it matters for companies trying to move from AI experiments to production workflows.
May 8, 2026
ERP Operational Intelligence: Why Text-to-SQL Fails Without Business Context
Text-to-SQL is not enough for ERP and operating data. Teams need business definitions, table context, visible SQL, and validation before answers can be trusted.
May 2, 2026
AI Workpaper Automation for Advisory Teams: What to Automate First
A practical guide for cyber, compliance, risk, and diligence advisory teams deciding where AI workpaper automation should start, what reviewers can trust, and how to design the first workflow.
April 27, 2026
Why AI Pilots Fail: The Workflow Redesign Checklist Before You Build Agents
A practical operating checklist for teams that want AI pilots to become production workflows instead of impressive demos.
April 24, 2026
Evidence Review Automation: How to Reduce the Back-and-Forth in SOC 2 and ISO 27001 Engagements
A deeper guide to automating evidence review without weakening defensibility, reviewer control, or source traceability.
April 22, 2026
Policy-to-Control Gap Analysis with AI: A Workflow for Source-Backed Coverage Matrices
A step-by-step workflow for turning client policies into control coverage matrices with Covered, Partially Covered, and Missing findings tied to source paragraphs.
April 21, 2026
Due Diligence Document Review with AI: From Data Room to Memo-Ready Findings
How AI can help diligence teams move from large data rooms to memo-ready findings without losing source visibility, human judgment, or review control.
April 20, 2026
AI Evidence Review Automation: How to Make SOC 2 and ISO 27001 Work Defensible
How compliance and audit-support teams can automate evidence review without losing source traceability, reviewer control, or audit defensibility.
April 18, 2026
AI Support for ToD and ToE: What Advisory Reviewers Should Expect
How AI can draft Test of Design and Test of Effectiveness notes while keeping source evidence, exceptions, and senior reviewer judgment in the loop.
April 18, 2026
Build vs Buy for Advisory AI: Fieldguide, DataSnipper, Vanta, Drata, and the Custom Workflow Gap
A practical comparison of audit platforms, Excel-native audit automation, compliance platforms, model providers, and custom AI workflow builds for advisory firms.
April 15, 2026
How to Run a 30-Day Advisory AI Workflow Pilot Without Creating Another Demo
A practical 30-day pilot structure for advisory teams that want measurable workpaper automation, not a slide deck or generic AI demo.
April 9, 2026
Private Deployment for Advisory AI: What Client-Approved Workflows Need Before Launch
Security, isolation, audit trails, permissions, and review controls advisory teams should define before using AI on client evidence and workpapers.
April 6, 2026
Build vs Buy vs Implement: The AI Workflow Automation Decision for Professional Services Firms
A decision framework for firms choosing between SaaS platforms, internal builds, and custom implementation partners for AI workflow automation.
April 4, 2026
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Useful AI starts with a clear understanding of the work.
These articles focus on the choices that make AI useful in real organizations: reliable sources, human review, secure data, thoughtful product design, and results your team can measure.
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