Enquirer Consulting Group

Reachable Buyer Map

Prepared for Shyam Khatau · Morphomix · August 2026
Your use cases are written by vertical, which is the honest way to sell this work and also the reason the reach problem is harder than it looks. Six use cases means six different buyers, sitting in six different kinds of company, and none of them read the same first sentence. This map is the market side of that: who signs inside each named vertical, and roughly how many US companies are in it. It describes the market rather than your business, and there is nothing to buy at the end of it.
Pharma, biotech and life sciences
The vertical your material leads with, and the one where an outside partner is already a normal way to buy engineering. Long approval paths, but the work is funded from program budgets rather than from a central technology line, so there are several doors into the same company.
Who signs: chief digital or data officer, VP of R and D informatics, head of clinical operations, head of medical affairs, and the executive who owns proposals and bids.
5,500 to 6,500
US pharmaceutical, biotech and life science employers; roughly 1,300 of them carry 250 or more people
Contract research, sites and specialty labs
The clearest single fit on this page, because response time on bids is a revenue number here rather than an efficiency number. Also the most consolidated group, which cuts both ways: fewer names to reach and a shorter path to a reference that travels.
Who signs: chief operating officer, VP of clinical operations, head of proposals and bid management, chief information officer.
2,400 to 3,000
US contract research, clinical site and testing laboratory employers
Health systems, provider groups and payers
The largest concentration of disconnected data in the country, and the segment where harmonization work is understood without being explained. Slow to buy and durable once landed, with a buying committee rather than a buyer.
Who signs: chief information officer, chief digital officer, chief analytics officer, chief medical information officer, VP of revenue cycle.
6,000 to 6,600
US health care and provider employers at 250 people or more
Investment firms, private capital and asset managers
The outlier on this page. Deal scoring and automated diligence are a competitive advantage rather than a cost saving here, so the decision is fast and the sponsor is senior. They also break the usual way of building a list, which is covered below.
Who signs: managing partner, chief operating officer, head of platform or value creation, head of research, chief technology officer where one exists.
3,500 to 4,200
US private equity, venture and investment management firms with payroll on file
Manufacturers with inspection and quality workloads
Where the labeling and computer vision work sells itself once someone has walked a line. Rarely reached by consultancies that arrive through the technology conversation, because the person who owns the problem sits in operations, not in the technology function.
Who signs: VP of operations, plant or site director, head of quality, director of digital manufacturing, continuous improvement lead.
5,800 to 6,500
US manufacturing employers at 250 people or more
Higher education
Small by count and unusual in structure: the buyer with the budget and the buyer with the problem are often two different offices, and the fiscal calendar decides the timing more than the need does. Worth working as a named list rather than a segment.
Who signs: provost, chief information officer, VP of enrollment management, dean of student success, head of institutional research.
1,700 to 2,100
US degree-granting institutions with meaningful enrollment and payroll

Where the openings are

1
Investment firms break every list filter there is. A firm running billions can be forty people on payroll, so any list built with a headcount floor deletes the entire segment before anyone reads a name. That is not a small mistake at the edges, it removes one of your strongest-fit verticals from the market you can see. Reaching them takes a different identification route from the one that works for health systems.
2
Six use cases do not share a buyer, and they cannot share a first email. Automated bid response speaks to the person who owns proposals. Data harmonization speaks to the person who owns systems. Predictive forecasting speaks to the person who owns the number. Same company, three seats, three problems. One general message is understood by none of them, and that is a targeting problem rather than a copy problem.
3
The venture brands and the parent will knock on the same door. Trialomix and Synaptomix reach broadly the same named people in life sciences and health care as the parent does. Which name arrives first, and what the second one says when it follows, is a sequencing decision that has to be made once at the market level rather than campaign by campaign. Left unmade, the same person hears from the group three times and remembers none of it.
Built from public federal registry data covering US employers that file a benefit plan, current to the 2024 filing year. Counts are banded deliberately. Sector codes are self-reported, so a company that does two things appears under one of them. Owner-only and very small firms are not published in this data, which is why the investment segment reads smaller here than the market actually is.
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