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AI pharmacy agents take repetitive prescription work off staff and sell as digital workers at $45K a year.
$1M (Dec 2024) → about $15M (Dec 2025) → $25M at recording · 40% EBITDA
“I don't care about being a system of record. I want to be the system of action.” TJM sells labor capacity to pharmacies, not another seat in their software stack.
Fewer bars = easier, cheaper, or faster for an AI-assisted solo builder. Editorial judgments based on the case details.
Jonathan Adly spent 15 years working in pharmacy before moving from product work into engineering and founding GalenAI, a bootstrapped healthcare software company later acquired by CaryHealth. After that exit and a stint at CaryHealth, he returned to founding with a labor-replacement thesis: AI should perform operational work, not just sit beside a system of record. TJM Labs began coding in November 2024 after a pharmacy customer won a contract that would have required hiring about 200 technicians in four weeks. The agents first handled prescription data entry, then expanded into intake, refills and other tasks. The company says one agent can replace two to three technicians and charges about $45,000 per digital worker per year. Adly says TJM grew from $1 million ARR in December 2024 to about $15 million in December 2025 and $25 million at recording, serving about 450 pharmacies and processing roughly half a million prescriptions daily. He also reports 40% EBITDA. The result is a high-trust, regulated vertical AI company with unusually fast reported growth and a demanding implementation surface.
TJM’s first pharmacy customer came through a relationship Adly had built over years in the sector. That buyer had won a large contract and faced hiring about 200 technicians in four weeks, so the pitch answered an urgent operating constraint instead of asking for an experimental AI budget.
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Join the Unicorn Club — $5/moCancel anytime, in one clickEvery claim traces back to a listed source — open any of them and check the number yourself. The exact words are quoted below, with a timestamp that jumps to the second each figure is said.
“So $1 million to about $15 million to $25 million.”
0:30 · hear it in the video“Revenue was about as close to $25 million ARR, right?”
12:22 · hear it in the videoData credibility: Jonathan Adly reports the ARR growth, 40% EBITDA, 450 pharmacy customers, $45,000 annual price per digital worker and about half a million prescriptions processed daily. These are founder-reported interview claims, not independently audited figures. The stated average of 2.5 agents per pharmacy and unit price imply a rough amount that does not reconcile cleanly with the stated $25 million ARR; treat the unit averages as approximate and segment-skewed. He also says the two acquisitions contributed less than 5% of ARR.
The first paid use case was attached to a specific labor crunch. In November 2024, a pharmacy that had just secured a large contract needed to staff quickly; TJM proposed agents that could reduce the human hiring requirement rather than waiting for a complete software rollout.
TJM’s growth loop combines operational savings with trust transfer. The agents take repetitive prescription entry off pharmacy staff, and the founder says the service can improve a small pharmacy’s margin by moving work from a thin-margin human operation to lower-cost automated capacity.
Pharmacy automation has an unusually long trust hurdle: the workflow touches regulated prescriptions, production systems and medication that can harm patients if handled incorrectly.
TJM Labs is an AI labor layer for pharmacy operations. Its agents handle work that would otherwise be performed by pharmacy technicians or assistants, including typing electronic, faxed or paper prescriptions into a pharmacy’s system of record and supporting refills. The pharmacist remains part of the dispensing workflow, checking the prescription before medication reaches the patient. The commercial promise is coverage and capacity: an agent works continuously and can be retrained for additional tasks, while the pharmacy buys digital workers rather than seats or a fee for each ambiguous task. The company positions this as a system of action alongside existing pharmacy software. For a smaller builder, the replicable product shape is an agent that completes one structured task inside an incumbent system, with a visible queue, confidence signals and a human correction path. Do not begin by promising a general pharmacy employee; earn scope through reliable task completion and documented exception handling.
TJM prices at about $45,000 per digital worker per year. Adly says that fee is deliberately a little below the salary of one pharmacy technician; his comparison for three human workers is about $150,000 before benefits and payroll costs. The buyer can therefore evaluate capacity against a familiar labor budget, while TJM avoids per-seat and per-task pricing. The founder reports about 450 pharmacy customers, an average of roughly 2.5 agents per pharmacy, $25 million ARR and 40% EBITDA. Those operating averages are approximate and do not reconcile cleanly when multiplied into company revenue, so treat the directly stated ARR as the reported total and the unit figures as directional. The interview also describes a first customer worth more than $1 million annually and later contracts at varying sizes. For a challenger, annual capacity pricing can work when one unit replaces a budgeted role, but define scope, volume bands, support and retraining terms so workload does not expand without compensation.
Our read: a 40% EBITDA margin in a regulated enterprise workflow is a strong signal, but it is not a template for a small copycat. TJM still has to fund integrations, model and infrastructure costs, pharmacy implementation, support, safety review and continuous workflow changes. The company raised $100 million across roughly six months and acquired two teams, which gives it resources to move quickly and build category coverage. A smaller entrant should keep the first product narrow and make onboarding repeatable before adding custom workflows. Track cost per completed prescription, human review minutes, exception rate, uptime and support hours by customer. The strongest margin protection will come from reusable connectors and predictable task boundaries, not from removing oversight where errors carry patient risk. Keep enterprise pricing tied to labor capacity and operational reliability, while reserving custom integration work for separately scoped contracts.
Copy the product thesis, the buyer language and the sequence of proof. TJM charges for digital capacity, compares it with a real staffing line item, and starts where a pharmacy faces a specific labor problem. It also uses domain expertise to earn access to a live workflow before expanding the agent’s job. A small team can apply the same pattern in a less regulated niche: identify repetitive work with a clear labor budget, deploy beside the incumbent system, keep a human approval step, and price against the capacity released. Change the ambition and sales motion. TJM’s scale depends on pharmacy experience, production trust, a strong technical team, acquisitions and substantial financing. A solo founder should not start by building a broad autonomous pharmacy platform or selling to national chains. Pick one operator segment and one task, validate the correction burden with paid pilots, and expand only when implementation can be repeated without founder heroics.
Our read: the central risk is safety and trust, not whether a language model can type a prescription. A subtle data-entry error, mishandled refill or failed handoff can damage a patient and end an enterprise relationship. Integrations can also break when pharmacy systems change, and pharmacy operators have little room for downtime. The economics need continued scrutiny: Adly says average independent-pharmacy margins are about 2%, so a buyer needs visible savings quickly, while service costs can climb if each location needs custom support. The business has also raised heavily and is pursuing an aggressive category-winning strategy, which creates execution pressure even with reported profitability. A challenger must not treat 40% EBITDA as guaranteed or extrapolate the $25 million ARR profile to a new product. Demonstrate safe completion, define human accountability, and prove net labor savings after onboarding and support before selling a wider replacement story.
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