Cruce Saunders
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Synteq

Jul–Aug 2026Remote

Tip: The engine I built won't rank a company as a top prospect without a dated source.

Cruce SaundersLevel 9
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The lead engine Cruce built for Synteq: a list of researched companies, each marked with a tier and a pass, beside one open record with a recommended opening line, a dated reason to reach out now and two risk flags.

Synteq

Project lead (internship)

Jul–Aug 2026Remote

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  • Back to the world
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Synteq

A two-week internship at Synteq Digital, a company that rents out powerful computers for AI.

As project lead, I built its first engine for finding and reaching new customers.

I delivered the engine and a playbook for running it to Synteq's leadership in August 2026.

I pitched my way in from San Francisco

Alpha, my school, set up the internship with Synteq Digital as a pitch for a spot. The pitch slots landed in the middle of my Wharton program in San Francisco, so I moved mine into my lunch break and pitched on July 13. I got the internship and started on July 27.

“I'm coming to learn a lot more than I'm coming to teach, and I'm good with that.”

The last line of my pitch

Synteq waited for customers to find it

Synteq Digital builds computing infrastructure, from bitcoin-mining hardware and data-center hosting to GPU cloud computing: racks of powerful chips that companies rent to train and run AI. Its cloud business relied on customers finding Synteq on their own. No one was reaching out to them first, and that was the gap my manager, Ben Haupt, Synteq's growth strategy lead, and I set out to close.

How the engine finds a customer

I built a tool that does the first and slowest part of a salesperson's job. It starts from a written profile of Synteq's best-fit customer: company size, the job titles that buy computing, US only. It pulls matching companies and contacts from a people-search database, drops the ones Synteq already knows and anyone who doesn't make buying decisions, then hands each remaining company to two AI agents in turn.

The first agent researches the company on the live web and has to return a source with a link and a date. The second has no web access. It scores the company against the profile and may only cite what the first agent found, so it can't make up a reason. No dated source means no top-tier ranking. Companies that pass are enriched with contact details and saved, and for the strongest fits the engine drafts a first email that waits for a person to approve it.

How the lead engine qualifies a company

TargetFindScreenResearchVerdictSaveReach outRules in codeThe model proposes; the code checks each AI step.Best-fitprofileCompany size,buyer titles, USonlyPeople searchPulls matchingcompanies andcontactsScreenDrops companiesSynteq knows andpeople who don'tmake buyingdecisionsResearch agentReads the liveweb about onecompanyVerdict agentScores thecompany againstthe profileLead databaseEnriched withcontacts, savedwith its evidenceFirst emailDrafted for thestrongest fits;it waits for aperson to approveiteachcompanysourcespasstop fitsMust return a source with a link and a dateNo web access; may onlycite the researchNo dated source, no top-tier rankingTargetFindScreenResearchVerdictSaveReach outRules in codeThe model proposes; the code checks each AI step.Best-fit profileCompany size, buyer titles, US onlyPeople searchPulls matching companies and contactsScreenDrops companies Synteq knows and people whodon't make buying decisionsResearch agentReads the live web about one companyVerdict agentScores the company against the profileLead databaseEnriched with contacts, saved with itsevidenceFirst emailDrafted for the strongest fits; it waits for aperson to approve iteach companysourcespasstop fitsMust return a source with a link and a dateNo web access; may only cite the researchNo dated source, no top-tier ranking
Two agents in turn: one finds the evidence, the other can only judge what it found.Request pathRules in code
  • Best-fit profile: Company size, buyer titles, US only
  • People search: Pulls matching companies and contacts
  • Screen: Drops companies Synteq knows and people who don't make buying decisions
  • Research agent: Reads the live web about one company
  • Verdict agent: Scores the company against the profile
  • Lead database: Enriched with contacts, saved with its evidence
  • First email: Drafted for the strongest fits; it waits for a person to approve it
  • Rules in code: The model proposes; the code checks each AI step.
  • Rules in code at Research agent: Must return a source with a link and a date
  • Rules in code at Verdict agent: No web access; may only cite the research
  • Rules in code at Lead database: No dated source, no top-tier ranking
  • Best-fit profile to People search
  • People search to Screen
  • Screen to Research agent: each company
  • Research agent to Verdict agent: sources
  • Verdict agent to Lead database: pass
  • Lead database to First email: top fits

Every lead comes with its reasons

Each company in the engine gets a record a salesperson can act on: a recommended opening line, why now is the right moment to reach out, with the date of the evidence, and the risks to plan around. A person can grade every verdict, agree or disagree, so the team can measure how often the engine is right. I wrote a playbook so Synteq's team could keep running it after I left.

One lead record in the engine for a public software company: a recommended opener, a dated why-now note about the company's cloud costs, two risk flags, and buttons to agree or disagree with the machine's verdict.

I presented it to leadership

On August 11, 2026, I presented the finished engine from Alpha in Austin to Synteq's leadership and my head of school: the database of researched, scored companies, first emails ready for approval, and the playbook to keep it running. I built the deck around the engine itself, five stages from finding a company to the dated evidence behind its score.

People

  • BH

    Ben Haupt

    Growth Strategy Lead, Synteq DigitalMy manager on the internship

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Led to

  • Level 10The WorkshopOct 2025–now

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