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Yes: an AI agent can help find investors who appear to play tennis or padel by combining contact data, public-profile research, visual analysis of public Instagram photos, LinkedIn connection mapping, and human-reviewed introduction drafts. Thanh Pham of Asian Efficiency described this four-stage workflow for Paddle Society, a padel club in Austin. He reported about $30–40 in LLM credits for a 100-contact batch; the source does not establish independent accuracy, investor-conversion results, or a privacy and legal review.
How can an AI agent find investors who actually play tennis or padel?
It looks for several kinds of evidence rather than expecting a single profile or database field to reveal the prospect’s interests. In Pham’s account, structured contact information starts the search; text research finds explicit mentions of racket sports; visual analysis can find apparent play in public photos; and LinkedIn connections help prepare a warm-introduction request.
The workflow was developed for Paddle Society, an Austin padel club seeking investors. Its premise is useful beyond that setting: a prospect may fit a qualification criterion that is visible in public material but absent from conventional contact records. That is a reason to investigate, not proof that the person is an investor, a committed player, or interested in the business.
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There are two distinct questions in this pipeline. First, does the contact appear to have a relevant racket-sport interest? Second, is there a plausible mutual connection who could introduce the contact? Combining the answers can help a team prioritize outreach, but neither answer alone establishes willingness to invest or guarantees a response.
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Pham’s description is a reported workflow, not a validated lead-scoring model. It does not provide a precision or recall study, a false-positive analysis, or an investor-conversion rate. Treat a flagged person as a lead for human verification rather than as a confirmed investor prospect.
What are the four stages of the pipeline?
The system moves sequentially from contact discovery to evidence gathering, image review, and introduction drafting. Each stage produces information the next stage can use; the described process does not rely on one agent or one data source doing everything.
1. Pull a working list of contacts
Pham starts with Apollo to find C-level executives in target industries, then cross-references LinkedIn to build a working contact list. This is the structured-data stage: it identifies people who fit the professional and industry criteria before the system investigates their interests.
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2. Search public text for sports signals
An agent checks publicly available digital material, including LinkedIn bios, Twitter, personal websites, interviews, and press coverage. It flags mentions of tennis, padel, squash, or other racket sports.
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This stage can capture explicit self-description or written reporting that image analysis will not find. It can also miss a genuine interest when a person never writes about it. A mention may be old, quoted from someone else, or unrelated to the contact’s own activity, so the signal should retain enough context for a reviewer to judge what the text actually says.
3. Use visual reasoning on public Instagram photos
The agent scans public Instagram photos and uses visual reasoning to flag a person who appears to hold a tennis racket or play on a tennis or padel court. As Pham put it in the Asian Efficiency article of August 19, 2026, “The agent scans their Instagram photos. It’s doing visual reasoning: if this person appears in a photo holding a tennis racket, or appears to be playing on a tennis or padel court, they get flagged as a qualified lead.”
This is the pipeline’s distinctive signal: a photo may suggest an interest even when a professional bio says nothing about sport. But an image can be ambiguous. A person might be posing with equipment, appearing in someone else’s photo, or standing on a court without playing. The account does not report how the system identifies the person across images, handles uncertainty, or checks a visual flag against additional evidence. Do not treat visual classification as confirmation without review.
4. Map LinkedIn connections and draft an introduction
A separate agent uses a virtual machine to navigate the author’s LinkedIn account, find mutual connections, and draft a text or email requesting a warm introduction. The author reviews and personalizes the draft before sending it.
This changes the output from a research list into a possible next action, while leaving the actual request under human control. The described workflow does not say that the agent sends the message automatically. A reviewer should confirm that the proposed mutual connection is appropriate, that the draft accurately represents the relationship and reason for contact, and that any claims about the prospect are supported.
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Can Instagram photos reveal interests that LinkedIn does not?
They can add a visual clue that is missing from a professional profile, but they cannot reliably establish a person’s interests on their own. The workflow uses Instagram to supplement—not replace—text-based research and structured contact data.
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The evidence described is limited to publicly available material. Pham’s account does not establish that private accounts or private photos are accessed, nor does it explain image retention, consent procedures, or a legal assessment. Teams should not infer permission to access restricted content from a description of public-photo analysis.
How do Apollo, LinkedIn, Instagram, and a vision model fit together?
They serve different roles in the described architecture: contact discovery, professional and network context, public visual signals, and synthesis or drafting. The point is not that these specific services are the only way to build the workflow, but that each task needs a suitable source and a clear handoff.
| Component | Role in the reported workflow | What it contributes | What it does not establish |
|---|---|---|---|
| Apollo | Finds C-level executives in target industries | Structured contact discovery | Whether a person plays racket sports or wants to invest |
| Cross-references the contact list and maps mutual connections | Professional context and possible introduction paths | That a connection will make an introduction or endorse the opportunity | |
| Public Instagram photos | Provides images for visual analysis | Possible activity-related clues not stated in text | Confirmed identity, regular participation, or investment interest |
| Vision-capable AI | Interprets images for apparent racket or court activity | A visual flag for a person to assess | Measured accuracy; no validation results are reported |
| LLM agent and virtual machine | Supports research, LinkedIn navigation, and draft preparation | A chained workflow and a proposed introduction message | Permission to automate account activity or send without review |
In an implementation, the important handoff is a reviewable record, not merely a yes-or-no label. Preserve the source and context for each signal, mark uncertain interpretations as uncertain, and distinguish what the system observed from what a human inferred. The source does not specify a technical schema or software configuration, so exact field names, APIs, model choices, and browser setup cannot be attributed to this example.
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What does the pipeline cost for 100 contacts?
Pham reported roughly $30–40 in LLM credits for a batch of 100 contacts, covering identification, research, and generation of introduction requests. This is the estimate in the Asian Efficiency article by Thanh Pham in 2026, not an independently audited price or a guaranteed rate for another implementation.
The reported figure is specifically for LLM credits. The account does not establish whether it includes staff time or other possible subscription costs, so it should not be read as the full operating cost. Contact data, platform access, infrastructure, and human review may involve costs or requirements not quantified in the article.
For planning, keep the cost basis in separate buckets: model usage, any data or platform subscriptions, browser or virtual-machine infrastructure, and the time people spend validating leads and reviewing drafts. Only the first category has a stated estimate in this account. A team should measure its own spend and review time on a small, representative batch before forecasting a larger campaign.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a team assess whether this workflow is working?
The source describes an architecture, not a measured benchmark, so a team adopting it needs to validate both the lead signals and the outreach process for its own use. A low model-credit estimate alone says nothing about whether the resulting contacts are relevant or whether introductions succeed.
- Signal coverage: Check whether the workflow captures structured, textual, visual, and network evidence rather than relying on a single source.
- Evidence quality: Review whether each flag is supported by relevant context and whether uncertainty or mismatched identity is caught.
- Automation depth: Make clear which stages only gather or organize information and which involve authenticated browser actions or drafting.
- Human review: Specify who approves qualification decisions and outreach before anything is sent.
- Cost basis: Track credits, subscriptions, infrastructure, and labor separately instead of treating model usage as total cost.
- Outcomes: Measure the team’s own qualification and response results; no independent performance figures are supplied for this workflow.
These checks help separate a technically functioning chain from a useful prospecting process. If a visual flag or connection map is unreliable, improving that stage may matter more than adding further automation.
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Can the same agent architecture work for sales prospecting?
Yes, the architecture can be adapted when a sales team needs to find prospects using a defining characteristic that is not a CRM field, then identify a possible mutual connection and prepare a warm-introduction draft. Pham says a founder wanted to adapt the approach for that kind of prospecting.
The reusable idea is task separation: use structured data to define an initial audience, search relevant public sources for an additional signal, use visual analysis only when the signal is genuinely visual, map appropriate connections, and have a person review the proposed outreach. The particular sources and qualification evidence should fit the sales use case; the example does not establish that every characteristic can be inferred accurately from photographs.
Before deployment, separately review the rules and permissions that apply to the data and authenticated accounts being used. The article does not describe a consent procedure, a terms-of-service review for Instagram or LinkedIn, or a legal/privacy assessment. Those questions are unresolved by the workflow description and should not be treated as cleared simply because content is publicly visible.
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Frequently Asked Questions
Does the agent analyze private Instagram accounts?
The reported description refers to public Instagram photos and does not establish access to private accounts or restricted images. It also does not document a permission model for any account access.
Does a tennis-court photo prove that someone plays tennis?
No. A photo may be ambiguous or show a person posing rather than playing, and the account provides no false-positive analysis. A visual flag is best treated as a prompt for human verification, not a confirmed fact.
Does the system automatically send LinkedIn introduction requests?
The described process drafts a text or email for the author to review and personalize before sending. It does not report automatic sending.
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