Deal sourcing becomes noisy when a nuanced investment thesis is reduced to a few database filters. The result may be large, but it is difficult to tell which companies truly fit, which were already considered, and why any row deserves attention.

Strawberry can use the firm's thesis, portfolio, pass history, CRM, and accepted examples alongside current web and logged-in research sources. It shows the evidence behind each candidate and begins with a varied first set that the investor can correct before the search expands.

Translate the thesis into observable signals

Stage, geography, check size, business model, ownership, and sector may be hard boundaries. Team, product, traction, timing, or a change in the market may be useful signals. Some questions cannot be answered from public evidence at all. Strawberry keeps those categories distinct instead of hiding them inside one fit score.

Want to try it?

Ask your Strawberry companion: “Use my thesis, portfolio, pass history, and available context to find a varied first set of investment opportunities. Explain why each may fit, show the sources, and check for pipeline duplicates.”

Skill

Source companies against my thesis

Find a thesis-matched first set with evidence, fit rationale, and material unknowns.

Search beyond standard categories

Relevant companies often describe themselves differently from the category an investor has in mind. Strawberry can search by problem, customer, technology change, business model, adjacent market, and known lookalike as well as standard labels. It can follow company sites, accelerator cohorts, investor portfolios, launch platforms, founder writing, hiring, funding records, and databases available in the visible browser.

The source of a deal can matter. Each candidate retains its provenance, and the list is checked against the portfolio, active pipeline, pass history, and existing records before it is presented as new.

Review a varied first set

What to reviewWhat it prevents
Why the company may fitAn unexplained ranking or opaque score
The evidence and its dateStale or repeated claims presented as current facts
What remains unknownPublic proxies being mistaken for diligence
Duplicates and pass historyReintroducing companies the firm already knows

A manageable first set lets the investor correct the interpretation, sources, exclusions, and useful fields before the search consumes more time or credits. The goal is a better funnel, not an arbitrary row count.

Hand each company to the next owner

Sourcing stops at the reviewable candidate set. Research deepens one company; an introduction uses relationship evidence; CRM changes require their own approved scope. Keeping those boundaries explicit makes the funnel easier to trust and improve.

Skill

Research an investment opportunity

Research a selected company before deciding whether deeper work is warranted.

Skill

Find and prepare a useful introduction

Find and verify a credible network path, then prepare the right introduction or ask.

Skill

Keep investment records current

Apply separately reviewed company or opportunity updates to the investment CRM.

Once the firm accepts the thesis translation and source mix, save them as a firm skill. A Routine can later look for new companies, deduplicate them against the current pipeline, and prepare only the additions worth reviewing.

How to map a startup ecosystem with AIBuild the market and investor view that can sharpen a sourcing thesis. How to review an investment pipeline with AIConnect new opportunities with the firm's active pipeline and current priorities.
Official Strawberry skill
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name
source-investment-opportunities
description
Find and qualify a reviewable set of startups or private companies against an investor's thesis. Use when a venture investor wants new deal flow, a thesis-matched company list, lookalikes, emerging companies, or a repeatable sourcing workflow; keep market mapping, deep diligence, outreach, and CRM writes separate.

Source Investment Opportunities

Turn the investor's real thesis into a sourced set of companies worth reviewing. The first result should help the user correct the search, not bury them in an unexplained list.

1. Understand what belongs in the funnel

Clarify the sourcing goal and the decision the list should support. Learn the relevant stage, geography, check size, sector, business model, ownership, traction, timing, and exclusions. Use the firm's thesis, portfolio, passed opportunities, prior searches, CRM, and accepted examples when available; do not ask the user to restate what those sources already establish.

Translate the thesis into observable signals and identify which criteria are hard requirements, useful indicators, or questions that cannot be answered from public evidence. Do not turn a nuanced thesis into a single opaque fit score.

2. Search beyond the obvious list

Use the sources that fit the search: current company sites, startup and funding databases available in the logged-in browser, accelerator cohorts, investor portfolios, launch platforms, hiring, founder writing, industry communities, research, news, and internal network or pipeline context. Follow links through the visible browser so the user can inspect the path and take over when useful.

Search with plain language, adjacent categories, problem statements, technology changes, customer types, and known lookalikes as well as standard industry labels. Preserve where each company came from; source provenance can matter as much as a company field.

3. Verify, deduplicate, and qualify

Check that each company exists, is active enough for the agreed purpose, fits the stated scope, and is not already in the portfolio, pipeline, pass history, or supplied list. Resolve likely duplicate names and entities before presenting them.

For each candidate, gather only the evidence needed for preliminary review. This may include what the company does, founding and location, stage and funding, business model, customer or traction signals, team, recent changes, thesis fit, possible conflicts, and material unknowns. Label inference and stale or weak evidence clearly.

4. Calibrate a varied first set

Present a manageable, varied sample before expanding a subjective or costly search. Include why each company may fit, which evidence supports that view, and what remains unknown. Invite the user to correct the thesis interpretation, sources, exclusions, fields, and ranking logic.

Expand only the accepted approach. A list is successful when it improves deal discovery and judgment, not when it reaches an arbitrary row count.

5. Deliver and hand off cleanly

Return a reviewable list or table with source links, dates, fit rationale, uncertainties, and deduplication status. Keep sourcing distinct from contacting a founder, requesting an introduction, adding records, or moving pipeline stages.

Use strawberry/venture-capital/research-an-investment-opportunity to deepen a selected company. Use strawberry/operations/make-a-warm-introduction when the next step is a credible network path. Use strawberry/sales/keep-crm-updated for separately approved record changes.

After the user accepts the method, offer to save the thesis translation, sources, exclusions, fields, and review behavior as a custom or team skill. A Routine may run on an agreed cadence, deduplicate against the current pipeline, and prepare only new or materially changed candidates for review. It should stop when the thesis changes, source access fails, or the search begins returning mostly ambiguous matches.