Reading the AI Buyer Thesis: How to Interpret SellSide DB's AI Scores

acquisition_attractiveness (0–10)

A composite score that weighs financial structure (recurring revenue, margin, growth), defensibility, operational complexity, and buyer-side leverage. 7+ is the top quartile of the dataset. Below 4 usually indicates structural problems (high churn, customer concentration, founder dependency).

Use this as the first-pass filter when sorting hundreds of listings. It doesn't replace diligence - it tells you which listings deserve diligence.

bootstrap_score (0–10)

Measures how realistic the business is for a bootstrapped operator to run post-acquisition. A high bootstrap score means lean cost structure, low headcount needs, and operations a single person can manage. Low means the business needs a team or capital infusion to continue.

Use this alongside attractiveness - a 9-attractiveness business with a 3-bootstrap-score might still not be right if you don't have the team to run it.

risk_signals (array)

Specific risks the AI identified in the listing. Common signals: high customer concentration, single-platform distribution risk, founder dependency, recent churn uptick, AI cost exposure, regulatory exposure.

Each signal maps to a diligence question. If 'founder dependency' is flagged, your interview process needs to surface what only the founder does - and whether that can be transferred.

opportunity_signals (array)

Upside levers a new owner could pull. Common signals: untapped distribution channel, pricing power, adjacent product expansion, international expansion, marketing maturity gap.

These are the parts of the buyer thesis where you'd create value post-close. Match them against your skills - if the opportunity is 'untapped paid acquisition' and you don't run paid ads, that opportunity is less real for you.

ai_buyer_thesis (text)

A 2–4 sentence narrative of why this business might be worth buying. Synthesizes the financial profile, defensibility, and upside lever into a single argument.

Treat this as the AI's opinion, not as fact. The thesis is useful for framing your own analysis but never a substitute for the underlying data.

ai_red_flags (text)

A short list of explicit concerns the AI surfaced - anomalies in the listing, structural weaknesses, or things that don't reconcile (e.g. 'reported high MRR but only 3 customers'). These are the things you must verify in diligence.

Practical weighting

A simple heuristic for weighting these fields in a first-pass review:

• Attractiveness 7+ AND bootstrap 6+ → worth a careful read
• Attractiveness 6–7 → skim; only deep-dive if signals match your buy box
• Red flags listed → verify each one in diligence; assume the AI found something real
• Opportunity signals → useful for framing post-close playbook, not for valuation

FAQ