Sector Thesis Builder
Turns a sector hunch into a written investment thesis with the tailwinds, risks and what has to be true to win.
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Your analyst spends 20 hours a week in databases. This Claude team runs entire deal origination in 20 minutes: a full origination department built as 7 teams and 35 Claude agents, covering thesis to pipeline.
Figures as cited in the original post, attributed to Blackstone, EQT, Lazard and JPMorgan. ProXreach is not affiliated with any of them.
Every agent reads the same Context Document, so your firm, your buy box and your current thesis are never re-explained.
Name it “Origination Engine”. One project per firm, not per thesis.
Paste it into the Project instructions. Update the Current Buy Box block as the thesis evolves.
Paste any prompt below with the inputs it asks for. Or upload the full .txt and just type the trigger phrase.
FIRM NAME: [e.g. Northbridge Capital Partners] FIRM TYPE: [e.g. lower middle market private equity, 8 investment professionals] FUND SIZE / CHECK SIZE: [e.g. $400M fund, $15M to $60M equity checks] SECTOR THESIS FOCUS: [e.g. vertical software, specialty industrials, healthcare services] GEOGRAPHY: [e.g. North America] DEAL TYPES PURSUED: [e.g. platform buyouts, add-ons, select growth equity] OWNERSHIP PREFERENCE: [e.g. control, majority preferred, minority considered for the right asset] EXISTING PORTFOLIO COMPANIES (for add-on fit): [list] KNOWN INTERMEDIARY RELATIONSHIPS: [list of bankers/brokers already covered] RETURN REQUIREMENTS: [e.g. target 3x MOIC, 25%+ gross IRR] PUBLIC POSITIONING ALREADY APPROVED: [any firm language cleared for external use] COMPLIANCE NOTES: [e.g. what counts as material non-public information, approval rules for outbound content] HOUSE STYLE: [e.g. plain English, data-led, no superlatives, no em dashes] CURRENT BUY BOX (update as the thesis evolves) SECTOR / SUB-SEGMENT: [specific] SIZE RANGE: [revenue / EBITDA band] HARD DISQUALIFIERS: [list] PRIORITY SIGNALS: [list, e.g. founder succession, sponsor hold period 4+ years]
Blackstone's AI flagged disruption risk and killed two public-company deals.
Define where you are hunting before you hunt: sectors, size, check and deal type, published as one buy box everyone sources against.
Turns a sector hunch into a written investment thesis with the tailwinds, risks and what has to be true to win.
Converts a thesis into a precise buy box: size, geography, check size, deal type and ownership structure.
Maps which deal structures fit the thesis: platform, add-on, growth equity, recap, or take-private, and what changes in sourcing for each.
Packages the thesis and buy box into a one-page publication for the team, intermediaries and the firm's own site.
EQT maps companies across its own 300+ sector taxonomy.
Map every owner, sponsor, lender and LP inside the thesis, ranked by fit, so nobody in the space is a surprise later.
Builds the full list of companies inside the thesis and sub-segments them so the team can prioritize systematically.
Tracks which companies in the universe are sponsor-owned, by which fund, since when, and estimates hold period.
Maps the lenders and known debt facilities behind companies in the universe and flags approaching maturities.
Maps the LP and co-investor relationships connected to targets or their sponsors, useful for both intel and capital formation.
Builds a structured, reusable sector taxonomy so every future mapping exercise in this thesis uses the same categories.
Turns the mapped universe into a presentation-ready market map grid by sub-segment and size.
EQT's Motherbrain fully sourced 9 investments, including AnyDesk and Netlify.
Track the signals that predict a deal before anyone sends a teaser: succession talk, founder age, leadership exits, debt maturities and hiring freezes.
Flags owners showing succession signals: age, lack of next-generation involvement, recent estate or legal activity.
Scans for CFO, CEO and founder departures across the target universe, since a leadership change often precedes a sale process.
Cross-references the lender and maturity map against the thesis to surface the targets where financing pressure is most likely to trigger a conversation.
Monitors hiring freezes, layoffs and leadership hiring sprees across the target list as early operating signals.
Combines every tracked signal into one proprietary deal-readiness score per company, so the team knows who to call this week.
Lazard now queries its entire CRM through its own internal LLM.
Track every banker and broker relationship, what they send and when, so you're the first call, not the fifth.
Builds and maintains the master list of bankers, brokers and advisors the firm covers, with relationship strength and sector focus.
Analyzes what each intermediary has sent historically to predict what they are likely to send next and whether it fits the buy box.
Reads an inbound teaser and instantly routes it against the buy box: fast pass, fast interest, or needs more information.
Builds a personalized, scheduled outreach cadence to keep every covered intermediary warm without becoming noise.
Prepares talking points and counters for an active conversation with an intermediary about a specific live opportunity.
EQT feeds 140,000 employee connections into its sourcing engine for warm paths.
Write DMs, emails and multi-year nurture touches that reference the owner's actual business, not a template with their name dropped in.
Finds the most credible warm introduction path to a target owner through the firm's own network.
Writes a first LinkedIn or email message to an owner that proves you understand their business, not a template.
Builds a multi-touch, multi-year nurture sequence for owners who are not ready now but fit the thesis long term.
Drafts the reply to an owner's response, whatever it is: interested, not now, flattered but no, or skeptical.
Asks the firm's existing portfolio company owners and executives for referrals to other owners who might fit the thesis.
JPMorgan's AI builds a full pitch deck in about 30 seconds.
Write the thesis posts, deal breakdowns, owner newsletters and webinar invites that make owners find you before you find them.
Turns the sector thesis into a LinkedIn post that signals exactly what you are looking for to the owners and intermediaries reading it.
Writes a public breakdown of a recent deal (the firm's own, or a market deal) that demonstrates expertise and attracts similar owners.
Writes a periodic newsletter aimed at owners in the thesis sector, building authority and staying top of mind before they are ready to sell.
Writes the invitation sequence for a webinar or owner-focused event: announcement, reminder and follow-up.
Plans a month of inbound content across thesis posts, deal breakdowns, newsletter issues and events so output stays consistent.
Blackstone's AI builds a first model "while I'm still on the phone."
Triage every teaser and CIM, score fit fast, prep the first call and log every touch so nothing falls through.
Reads a CIM and produces a fast go, no-go or dig-deeper read against the buy box, in minutes, not days.
Builds a quick, directional financial model from CIM-level data to support a go, no-go decision on the first call.
Builds the prep brief and question list for the first call with an owner or management team.
Logs every touch, from first teaser to decision, into a consistent pipeline record so nothing is lost and reporting is always current.
Turns the pipeline log into a weekly report for the partners: volume, sources, stage movement and what needs a decision.
Each team hands its output to the next, so the owner on today's call list is there because the thesis, the map and the signals all agree.
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