Screen 18 acquisition targets in 20 minutes each.
For each target, the deal team inputs publicly available data and Inspector produces a standardised 8-dimension health score. It surfaces the top four targets for deeper diligence, replacing two weeks of analyst work per target with a 20-minute process.
Run Inspector- 1
Describe the task
Inspector applies the same eight-dimension screen to every acquisition target, so the shortlist is comparable instead of coloured by which analyst looked at which company. You feed it the public data on a target; it scores each dimension against your roll-up thesis, cites the evidence, and tells you whether the company is worth two weeks of deep diligence or a pass.
The sharper your prompt, the more decision-ready the score. Tell Inspector what the roll-up is consolidating for, which dimensions are deal-breakers vs nice-to-haves, and what "acquirable" means in your model, and it will hold that thesis constant across every target in the batch.
Screen these acquisition targets for our roll-up using the same 8-dimension health score for each. Data is in targets/ (one folder per company: site scrape, filings, reviews). Score each on: revenue quality, margin profile, customer concentration, management depth, systems maturity, integration difficulty, market position, and legal/regulatory risk. For every dimension: - Score 0-100 with the evidence it rests on - Flag any deal-breaker against our thesis in roll-up-thesis.pdf Treat customer concentration and integration difficulty as hard gates. Rank all targets, surface the top 4 for deep diligence, and give a one-line pass rationale for the rest.
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Give Inspector context
In an Inspector workspace, drop one folder per target holding whatever public data you have, site scrape, filings, review-site exports, press. Inspector scores each company in isolation but against the same rubric, so the resulting ranks are directly comparable across the batch.
Connect your roll-up thesis so the screen scores for fit, not just quality. A company can be healthy and still be a pass if it fails the thesis, wrong geography, un-integrable systems, and Inspector will surface that rather than rewarding a high standalone score.
Required contextA folder of public data per target, financials or filings, site content, reviewsYour roll-up thesis, what you are consolidating and what makes a target acquirableOptional contextNamed deal-breaker dimensions, so Inspector treats them as hard gates rather than weighted inputsA prior target you already scored, so the new batch is calibrated to your barAny teaser or CIM you have, for richer evidence than public data alonetargets/ (one folder each)roll-up-thesis.pdfprior-scorecard.xlsx - 3
What Inspector creates
Inspector scores every target on the same eight dimensions, cites the evidence behind each score, applies your hard gates, and ranks the batch. You get a top-four shortlist ready for deep diligence, a one-line pass rationale for the rest, and a note on any target too thin on public data to score confidently.
From Inspector: Screened 18 targets on 8 dimensions each against roll-up-thesis.pdf.
20 minPer target, vs 2 weeks18Targets screened4Advanced to deep diligenceTop 4 shortlist4 targetsTarget Composite Standout Watch Harbor Facilities Group 84 Recurring contracts, low concentration Aging ERP will need replacing Meridian Coatings Co. 81 Best-in-batch margin profile Owner-dependent management Delta Grounds Services 79 Clean systems, easy integration Thin market position regionally Passed on a hard gate5 targetsTarget Composite Gate failed Ridgeway Mechanical 76 Top client is 58% of revenue, fails concentration gate Ashcroft Utility Ltd. 72 Bespoke systems, integration difficulty scored 20 "Two targets, Crestline Environmental and Vantage Site Works, had too little public data to score customer concentration confidently; I ranked them provisionally and flagged the gap. Want me to build the diligence checklist for the top 4, or request the missing data on those two?"
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Follow-up prompts
Build the deep-diligence checklist
Inspector can turn each shortlisted target’s watch-items into the specific diligence questions the deal team should chase first.
For the top 4 targets, build a prioritised deep-diligence checklist. Lead each with the watch-item Inspector flagged and the exact data request that would resolve it.
Draft the investment committee one-pager
Route the shortlist into the format the IC actually reviews, one page per target with the score, thesis fit, and open risks.
Draft an IC one-pager for each of the top 4: composite score, dimension breakdown, thesis fit, standout strengths, and the top two risks with proposed mitigants.
Re-run as new targets come in
Save the screen as a runbook so every new target hits the same eight-dimension bar and slots into the ranked pipeline automatically.
Save this as a runbook called "target-screen", then run it on any new folder dropped into targets/ and tell me where each new target ranks against the current shortlist.
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Tips and troubleshooting
Hard gates keep the screen honest
A weighted average lets a great margin profile paper over a fatal customer concentration. Naming your true deal-breakers as gates makes Inspector pass a target outright when it fails one, no matter how strong the rest of the score.
Feed one prior target to calibrate the bar
Scores are more useful when they are anchored. Give Inspector a company you already know well and how you would rate it, and the batch comes back calibrated to your standard rather than an abstract 0-100.
Thin public data gets flagged, not guessed
When a target has too little public information to score a dimension confidently, Inspector ranks it provisionally and tells you exactly which data is missing, so you never mistake a data gap for a low score.
Ready to try it yourself?
Drop a folder per target and your roll-up thesis, and get a comparable, eight-dimension score for every company, with the top four surfaced for diligence in twenty minutes each.
Run Inspector