How TJM Labs grows

Each AI agent ('digital worker') costs ~$45K/year, slightly less than one pharmacy technician's salary, while doing the work of 2-3 technicians (~$150K+/yr with benefits and payroll). Pitch: hire a digital worker that works 24/7, train once, retrain for more tasks. Explicitly rejected per-seat pricing and per-task pricing (tasks are ambiguous; 'close to tokens'). Pharmacies buy multiple agents (average 2.5-3; a big one can buy 100), so revenue grows with each agent added.

Over the Nov 2024 holiday break, a customer he'd built trust with over many years had won a large contract and needed to hire ~200 pharmacy technicians in 4 weeks. He offered to put in AI agents so they could hire fewer people ('instead of hiring 200, maybe you have to hire only 100') and wrote the first code for them. Pharmacy has no test sandboxes, so access to a live production system came only from years of trust. Served only this customer for ~9 months before selling to other pharmacies (Feb 2025); capabilities expanded as AI models improved.

Treats AI as 'true labor replacement', a 'system of action' rather than a system of record. The agents replace technician data entry on incoming e-prescriptions, faxes and paper scripts, and the pharmacist double-checks. Pitch to mom-and-pop pharmacies: average margin goes from ~2% to ~8%; to enterprise (e.g. grocery-chain pharmacies run as loss leaders): no longer a loss leader. He left CaryRx because this view was too early for a mature company.

Raised the $10M Series A (Dec 2025) mainly to buy EncoreRx and the $15M A1 (Mar 2026) mainly to buy Pharmasol: two pharmacy teams doing adjacent work that could overlap. Internally called it 'The Manhattan Project': put the handful of people who can do this in one room. Acquired companies had under ~$2-3M combined revenue.

Raised $10M A, $15M A1 and a $75M Series B from Elephant in ~6 months while at 40% EBITDA, without an investment deck. Always includes some secondary liquidity in a round (size depends on the market). Goal: a 'fortress balance sheet' so they never have to raise again, plus money for M&A and to win the AI-agent category before the transition ends (~a year).

Looks at public companies' revenue multiples vs growth (Figma ~10-15x at ~40% growth; growing 10x should be no less than 15x). With Elephant, did napkin math on a path to $100M ARR: 450 pharmacies today of ~16,000, going to 1,000 then 3,000, and checked the valuation holds if they execute and the plan is realistic. Warns that too high a valuation comes back to bite.

Customer 1 came from a long relationship; customer 2 was a friend of customer 1 who saw the results; customer 3 came after a customer showed the agents to a seatmate on a plane, who called the founder's cell on landing. That third deal convinced him to build a sales team, but referrals alone carried them to ~30 pharmacies.

Initially stayed quiet about funding, modeled on Veeva; switched to announcing rounds (press release for the $10M A, $15M A1 and $75M B) because candidates worry a startup will go away, and a big raise reassures them.

The team runs outbound through LinkedIn; no further detail given.