Automated Due Diligence: Fast Insights With Expert-Led Refinement

This article is written for acquirers of Lower-Middle-Market (LMM) software companies—including Private Equity buyers, holding companies, and strategic acquirers.

TL;DR

• Let AI build v1 of the Competitive Landscape: automation assembles a defensible fact-based Capabilities × Competitors grid in minutes, not days.
• Let People Add Judgment: the deal team's subtle tweaks to Harvey Ball settings help to capture nuances that adjust for the investor's unique/proprietary investment thesis and sensitivities.
• Get to Conviction Faster: end up with fewer blind spots (AI helped you scan for competitors and capabilities you may have otherwised overlooked), clearer priorities (you helped refine the Harvey Balls), and a professionally presented grid that stands up in partner and Investment Committee discussions.

Why pair automation with judgment in PE diligence

Automation gives you breadth, speed, and consistency—pulling signals from messy websites and normalizing them into a comparable view across competitors and features. Human judgment adds the context machines miss: segment focus, implementation depth, edge-case coverage, and what actually matters for your investment thesis. Together, you get a defensible starting point plus explainable refinements that reflect deal-specific nuance, not just marketing copy. The result is fewer hours spent chasing basics and more time on the calls that change outcomes.

The limits of binary checklists

Most private equity buyers (e.g. of vertical B2B SaaS) are investing in relatively mature categories, where many competitors can credibly claim “feature parity” because they’ve built enough to check the box.

When you’re forced to go on the record with Does/Doesn’t in a checkmark-style Capabilities x Competitors table, you lose the subtle signal that makes all the difference. Did they ship the bare minimum (a 25% Harvey Ball), or is it an industry‑leading capability (a 100% Harvey Ball)? That's where all of the instant, powerful, fact-based scan by automation / AI, lets you leverage your time at the critical moment -- enabling you to make those judgement calls with all of the supporting evidence readily available for your perusal.

This is why pairing automation with judgment works so well: AI can instantly scan and normalize evidence about competitor capabilities, and analysts can then apply nuanced gradations assessing the strength of competitors' capabilities.

Graded signals capture strength without over-claiming

Binary checkmarks flatten reality; graded indicators (like Harvey Balls) show strength without pretending to be more precise than the evidence allows.

• 0%: Not present or contradicted by evidence
• 25%: Bare-minimum or checkbox-level support
• 50%: Solid but partial coverage; important gaps remain
• 75%: Robust implementation with a few caveats
• 100%: Industry-leading depth and completeness

This language lets teams reflect uncertainty and quality without over-claiming—and you can always click through to the underlying sources when you need to drill down.

A pragmatic workflow from scan to conviction

Start wide: let automation compile a list of reference material for each Capabilities × Competitors pair. This is the baseline readily available (but painful to gather manually) from public signals (docs, product pages, changelogs, trust centers):

Then converge: analysts review the evidence, add graded refinements where the machine’s “present/not present” misses the nuance, and capture those judgments in a way that the whole team can see.

Machines draft the grid

The machine pass scans known sources, and assembles a defensible baseline quickly so you’re not starting from a blank page, keeping a link back to each source so you can verify claims in context. This first draft is intentionally conservative: it aims for breadth and consistency, with enough fidelity for analysts to apply judgment where it matters most.

Analysts refine with Harvey Balls

Analysts apply 0/25/50/75/100 Harvey Balls to express strength, maturity, and recency—turning ambiguous “yes, but…” and “it depends” into a shared, graded signal. These refinements sit on top of the machine baseline, so automated updates don’t overwrite human judgment; they simply add new context the team can review. Because each adjustment ties back to a source, it’s easy to justify the call in partner meetings and update it later as facts change. This workflow respects the time of deal teams: the machine drafts, humans decide.

Keep the loop tight with immediate feedback

• Instant visual updates help the team converge quickly on the small set of real disagreements.
• A shared, link-backed record makes it clear why a call was made and what would change it.
• Lightweight gradations reduce rework by preventing false “parity” that later unravels in diligence. Tight feedback keeps momentum high and makes the grid a living artifact—not a one-off slide that goes stale.

What this enables for deal teams

The combination of automation plus human refinement yields faster speed-to-insight, fewer blind spots, and an explainable record of why you trust each call. Teams align sooner, partner conversations focus on what matters, and IC materials reflect graded reality rather than binary claims. If you want a quick walkthrough using your current targets, reach out at sales@suitecompete.com, or skim our feature overview to see how the grid and Harvey Balls fit together.