The study build that took 68 days now takes 12. Here is the business case.
Your assumptions
Per-phase build — traditional vs. your configuration
What the AI costs to run — per study build
| Agent | Input tok | Output tok | Cost |
|---|---|---|---|
| Parser | 1.01M | 69K | $6.75 |
| DMP Writer | 206K | 60K | $2.53 |
| eCRF Builder | 275K | 81K | $3.40 |
| eCRF Builder | 218K | 61K | $2.61 |
| SDTM Mapper | 451K | 97K | $4.67 |
| ADaM Generator | 405K | 101K | $4.55 |
| Query Candidate | 252K | 85K | $3.39 |
| Define-XML Generator | 164K | 45K | $1.94 |
| Total / study — Claude Opus 5 | 2.98M | 598K | $29.84 |
The business case — status quo vs. TrialNexus (annual)
Business case summary — paste into the deck
For a 18 eCRF trial at 88% automation with standard oversight, TrialNexus compresses study build from 68 to 12 calendar days (82% faster) and from 82 to 31 person-days (62% less effort), moving the quality index from 69 to 75. Cost per study falls from $62,320 to $23,760 — of which agent inference, priced at current frontier-model list rates, is just $29.84. Across 8 studies a year against a $124,000 platform investment, that is $184,477 in annual savings, a 149% ROI, a 8.1-month payback, and 1.8 FTEs of specialist capacity redeployed from mechanical build to clinical science. Model covers 8 build modules.
- Cycle time compresses from the automated draft speed plus pipeline overlap; more oversight adds a little review-queue time.
- Effort & cost fall as agents absorb the mechanical share; the residual is human review, which the HITL lever scales. Complexity (eCRF forms) scales both.
- Amendments add rework on top of the 68-day base build, on both sides of the comparison — manual rebuilds cost far more than agent rebuilds, so the baseline grows faster than your configuration.
- Quality is a first-pass-yield index: automation removes transcription defects, oversight catches the rest — but automation with weak oversight lets model error through, which is why the index can drop below traditional.
- Token cost is per-agent input/output volume, scaled by form count and automation level, priced at current list rates for the frontier reasoning models available today (Claude Opus 5 $5/$25, Claude Fable 5 $10/$50, GPT-5.6 Sol $5/$30 per 1M).
- Platform investment defaults to an illustrative $60k base plus $8k per study; edit the studies-per-year and rate levers to fit your portfolio.
CDISC study-build ROI — not the whole clinical-ops stack
This explorer costs the window between a final protocol and a validated go-live database. It is the DMP, eCRF, edit checks, SDTM mapping, ADaM, automated-review setup, and Define-XML — the work a scarce specialist rebuilds for every study and every amendment.
It does not cost Signal weekly narratives, custom Agent Studio work, or the years of data-management operations after first patient in. Those have different unit economics. Use the levers to match your protocol; export the briefing; then get a scoped price on a 30-minute call.
Study build is slow because it is done by hand, one artifact at a time
Between a final protocol and a validated, go-live database, a clinical trial passes through eight build activities. Seven produce the database itself; the eighth builds the automated data review that will run against it from first patient in. Each is done by a scarce specialist, each waits on the one before it, and each is re-done from scratch every study and every amendment.
That number is not a technology limit. It is the sum of human queue time across a fixed sequence: read the protocol, write the Data Management Plan, design the CRFs, build and program the EDC edit checks, annotate the SDTM mapping, derive the analysis datasets, build the automated review that reads incoming data and raises queries, then assemble the Define-XML submission package. Every one of those is a document a person produces, another person reviews, and a third person reconciles.
Clinical Data Management is, structurally, a factory that rebuilds the same artifacts by hand for every trial.
Each of those artifacts now has an agent — with a human on the signature
Signal runs for the life of the trial, not just the build. Ask any question about your data in plain language — enrollment trends, query rates, safety signals, protocol deviations — and Signal returns a deep, sourced answer instantly. No dashboards. No ETL. No programmer.
The compression is all eight phases moving at once, not one agent. SDTM annotation that took a specialist week and a half now takes under three days; ADaM derivations that required programmer cycles are drafted in hours; Define-XML generates automatically from the reviewed SDTM source. The phases overlap rather than queue, and library reuse means the agent rarely starts blank.
Quality is the counterintuitive part. Drop oversight to light-touch in the explorer and the quality index falls below the traditional baseline — automation without review ships errors faster, not slower.
What procurement asks about the study-build model.
Your estimate is built. Put a number in front of procurement.
A 30-minute call turns your model into a scoped price for your specific protocol, phase, and portfolio. No deck required — the numbers speak for themselves.
Model calibrated to Tufts CSDD and ACRP build-time surveys. Outcomes depend on protocol complexity, therapeutic area, library maturity, and organizational readiness. All production artifacts require human review and a 21 CFR Part 11 e-signature.