Briefing
Twenty minutes. First, enough of the technology that we all use the same words. Then the numbers for a data management practice of this size.
Part one — the technology
Given a sequence of text, it predicts what comes next. Everything else is built on top of that.
The loop runs once per token. What people call "reasoning" is the model spending more tokens thinking before it answers.
Part one — the technology
A chunk of text, roughly four characters. Models read, price and limit work in tokens, not words.
How much text it can hold at once. Everything it knows about your task lives there, and is gone on the next call.
The trained parameters. Open-weight models can be downloaded and run on your own hardware.
How adventurously it picks. Low for data work and code. Higher for drafting and ideas.
Part one — the technology
The right-hand column is the specification for what an agent has to solve.
Part one — the technology
A chat window gives the model words.
An agent gives it hands, memory, and a job.
Part one — the technology
You send a request. Hermes gives the model tools, reads the real results back, and repeats until the work is actually done.
It never guesses what a script returned. It runs the script and reads the result.
Part one — the technology
01
Files, spreadsheets, databases, scripts, web, email. How the model touches anything real.
02
Durable notes loaded every session, so what you explained once does not need explaining again.
03
Your procedures on disk, loaded only when the task matches. Your SOPs, not the model's defaults.
04
Scheduled and background work, so reconciliation, listings and checks run overnight and are waiting in the morning.
Part one — the technology
A skill is a written procedure with a trigger. Hermes loads it only when the work calls for it.
Your value is twenty years of knowing how a sponsor wants their data. A skill is that knowledge written down once, applied consistently, and no longer trapped in one person's head or lost when they are on leave.
Part one — the technology
Protea is a privacy and memory layer, used as the example of the right architecture for regulated work. It sits between a client and any model backend, so you get the frontier model and keep the data.
Named entities — people, sites, sponsors, compounds — are replaced with stable placeholders before the request leaves your hardware, and restored in the reply. The model never sees a real patient name or a blinded molecule, so you can run frontier models without an exception.
A single SQLite file per user behind an OpenAI-compatible API. An embedded local model means it runs fully offline once the model weights are pulled. No third party ever holds your corpus.
Structured recall across conversations and months — spiral-indexed by relevance and recency — rather than starting from nothing every session. Fifty years of knowing how a sponsor wants data stops living in one person's head.
Conductor routing passes each task to the model best suited for it; a confidence cascade escalates only when needed: local model for the routine, frontier model for the hard call. Consensus fans a delicate question to several models and synthesises the answer. Teams decompose a brief, rate deliverables, and review every result. An Open WebUI front-end gives clean per-user separation, with identity forwarded through the API.
The pitch to a sponsor: "we run AI-assisted data management and your data never leaves our environment." Very few small vendors can say that sentence truthfully.
docs/README.md; redaction pipeline docs/redaction-algorithm-research.md (local: D:\Projects\Protea\docs)Part one — the technology
Asked for something it cannot know, a model produces a fluent, plausible, wrong answer rather than nothing.
An invented variable name. A confident number with no source. A mapping that looks right and is not.
Read before answering, run before claiming, show the source. Grounding in real output — and a person signing off. Never autonomy over a regulated deliverable.
Part one — the technology
The evidence is not "experts get faster" — it is that expertise is what lets AI be used on the hard stuff at all.
The model cannot produce the question that only twenty years of knowing sponsors produces. The value is upstream of the answer.
AI is fluent and confidently wrong. Inside its capability boundary it gains ~40%; pushed past it, performance drops 19 points. Only experience says where that boundary is.
The model suggests; the domain expert decides. A 19-point penalty exists precisely because it is not obvious to a generalist which tasks AI can do and which it cannot.
A senior hand encodes their own judgement as a skill. Their expertise stops being a bottleneck and becomes repeatable.
AI compresses the skill gap on routine tasks — a beginner with AI reaches near-expert output on well-defined work. But a data management practice does not bill for routine. It bills for the messy, consequential, one-off judgement where a confident wrong answer costs a study.
The real gain for an expert is not doing the same task faster. It is having a tireless assistant who drafts and cross-checks, while the expert spends the saved hours on the calls, the protocols, and the relationships no model can hold.
Part two — the numbers
The question is not whether AI is impressive.
It is what an hour of your team's time is worth, and how many of those hours you currently sell.
Part two — the numbers
A worked example at the scale you described: eight people, about 1,700 working hours each — roughly 13,600 hours of capacity a year. Adjust the staff count and the numbers scale.
9,520 hrs
70% utilisation. This is the revenue.
4,080 hrs
Status reports, proposals, reconciliation chasing, formatting, re-keying, QC of your own paperwork, answering the same sponsor question a fourth time.
That second box is the target. Not the people — the 4,080 hours of work nobody pays for.
Part two — the numbers
$28–71
Published benchmark range. Medidata's analysis puts the average at $53.87.
~96,980
Which is roughly $5.2m of query handling in a single large study.
~$1.3m
Avoided cost, on one study. Cleaner data at entry beats faster query resolution.
You do not have to capture that whole number. You have to be the vendor who can credibly claim a share of it and price accordingly.
Part two — the numbers
Revenue, USD. Modelled on 8 billable staff at ~$1.0m in 2026, headcount growing 4% a year in all three cases.
Part two — the numbers
EBITDA, USD. Waiting is not neutral: from 2028 the client-side expectation of AI-era pricing arrives whether or not you have the capability.
Part two — the numbers
| Path | Utilisation | Revenue | EBITDA | Margin | Cumulative EBITDA 2026–31 |
|---|---|---|---|---|---|
| Start now (2026) | 82% | $1.62m | $481k | 29.7% | $2.14m |
| Wait until 2029 | 80% | $1.52m | $383k | 25.2% | $1.34m |
| Do not adopt | 70% | $1.16m | $57k | 4.9% | $0.80m |
$802k
Cumulative EBITDA foregone by starting in 2029 instead of 2026. The capability is still reachable later. That money is not.
~$28k
First-year programme: tooling, hardware, and the time to write your procedures down properly. Roughly 2% of revenue thereafter.
Part two — the numbers
Every scenario on the previous slides has the team growing, not shrinking. The gains come from selling more of the hours you already have, not from having fewer people.
The dull half of the job. Re-keying, chasing, formatting, first-draft documentation, the fourth version of the same listing.
Judgement on a protocol. The sponsor relationship. Who signs off. Knowing which query actually matters and which is noise.
The scope one data manager can carry. More studies each, more senior work each, and a reason to pay them more.
A practice this size does not win by employing fewer people. It wins by being able to take on the study it would previously have turned down.
Part three — where to point it
Draft the eCRF spec, edit checks and validation rules straight from the protocol, for a human to correct rather than compose. The blank-page hours are the expensive ones.
Rank the day's queries by whether they will actually change an analysis. Draft the text. The data manager approves or rewrites.
External labs, SAE against the safety database, vendor files. Overnight, with an exceptions list on the desk each morning.
First-pass SDTM mapping and define documentation. Tedious, rule-based, and reviewable — the ideal shape for this.
MedDRA and WHODrug first-pass suggestions with the reasoning shown, and a person confirming every term.
DMP drafts, status reports, meeting minutes, sponsor updates. Pure non-billable time today.
Bid responses and costing drafts from your own past work. More bids answered, from the same people.
A new hire asks the system how you do things and gets your actual procedure, not a guess. Weeks off ramp-up.
Part three — where to point it
The interesting part is not doing the same work cheaper. It is that some services only become viable once the marginal hour is cheap.
Not a monthly cleaning pass — a standing service watching every incoming record. Recurring monthly fee instead of project fees.
Inherited messy databases from failed vendors. Currently priced out of reach because it is all manual. High-margin, and it wins the next study too.
Old studies into modern standards for regulatory submission or reuse. A large backlog exists and almost nobody wants the work.
Biotechs and academic units the big CROs will not quote for. A cost base this low makes small studies profitable.
Sponsors nervous about AI and confidentiality. Local models mean you can offer the benefit without the data leaving. Protea is exactly this.
Trials on the continent, with local regulatory knowledge, local time zone and a cost base that still undercuts Europe. A hard-currency price point.
Part three — where to point it
Pick the single most repetitive non-billable task. Time it honestly for two weeks first, so the before number is real.
Write that procedure down properly as a skill. This is the actual work, and it has value even if the AI part fails.
Run it alongside the human for a month. Compare outputs. Keep the human sign-off permanently.
Measure the hours recovered. If they are real, take on one more study with the same team. That is the whole thesis.
8 billable staff, 1,700 hours each, 70% utilisation and a $105 blended hourly rate — giving ~$1.0m in 2026. Headcount grows 4% a year in every path. Utilisation rises to 82% at full adoption, phased over roughly four years. Programme cost ~$28k in year one, then ~2% of revenue.
From 2028, non-adopters face ~3.5% annual real rate erosion as sponsors price in AI-era expectations. This is the single most debatable assumption and the main driver of the "do not adopt" line.
Sources: contract CRA rates $85–110/hr and CRA salaries $95–115k (market reporting, 2025–26). Query cost $28–71, average $53.87 (Medidata PICAS/CROCAS analysis); ~96,980 queries per average Phase III (industry reporting). Adoption effect anchored on Tufts CSDD/DIA (Getz, Applied Clinical Trials, Jun 2025): adopters reported ~18% average cycle-time reduction; only 11% of companies fully implemented. Not a vendor 35–45% projection.
Close
The technology is ordinary. It predicts text, and a framework gives it tools and memory.
The opportunity is not ordinary. Four thousand unpaid hours a year, in a business that bills by the hour.
Nothing here requires betting the company. It requires writing down how you work, and starting.