Agent-assisted ADaM,
audit intact
BuildADaMs derives CDISC ADaM datasets from frozen SDTM with propose-only AI agents, human approval at every gate, deterministic fallbacks, and matching R and SAS output.
AI you cannot audit is AI you cannot submit
Analysis derivations are slow because every variable is double-programmed by hand, and generic AI copilots are opaque, so regulators do not trust them. When the SAP changes or someone asks how a value was derived months later, the answer is guesswork pieced together from scripts and memory.
× Double programming is slow
Every analysis variable is built twice by hand to satisfy QC, and the second build is pure cost with no new science in it.
× Black-box AI fails review
A suggestion you cannot trace or reproduce has no place in a regulated derivation, so most copilots never make it past the reviewer.
× Derivations drift from the spec
When the SAP changes, keeping code, spec, and QC in step is manual and error-prone, and the drift surfaces only at inspection.
Propose-only agents suggest, humans approve, the record proves it
Four propose-only agents work alongside a gated workflow. They read specs and bounded metadata, never patient rows, every proposal is hashed and audited, and a human applies or rejects each one before it touches a dataset.
Agents suggest derivations, QC logic, and code. They never write to a dataset or skip a gate, the human decision is always the one that lands.
Every agent has a fallback heuristic, so the workflow finishes even with no model available and a study is never blocked on AI.
Each proposal is hashed and linked to the user action that applied or rejected it, so the record shows exactly what AI suggested and who decided.
A gated path from spec to sign-off
- Each dataset moves through spec ingestion, derivation, codegen review, QC double-programming, execution, output review, and sign-off.
- A human approves each gate, and the workflow cannot skip a state or advance without an audit event.
Why it mattersNothing reaches analysis without review, QC, and sign-off, and the path is the same every study, so the state of every dataset is provable.
Four agents that suggest, never decide
- spec_ingestion parses the SAP, derivation proposes analysis variables, qc proposes double-programming logic, and codegen_assist drafts the code.
- Every proposal is hashed and audited, and a human applies or edits it before anything is written.
Why it mattersYou get the speed of AI with a human decision and an audit record on every suggestion, so review is fast and the trail is intact.
Many SDTM domains into one analysis dataset
- Derive an analysis dataset from several frozen SDTM domains at once, with the lineage of every ADaM made explicit.
- ADSL draws on DM, EX, and DS, and ADLB draws on DM, VS, and LB, all visible in one tree.
Why it mattersThe inputs to each dataset are visible and reproducible, not buried in a program, so a reviewer can trace any value back to its source.
One spec, matching R and SAS
- The derivation spec compiles to Admiral-based R and to SAS that follow the same logic line for line.
- Run the R in the platform on a pinned image, or take the SAS to your own validated environment.
Why it mattersYou are not locked in. Reviewers read the generated code, and it runs the same on your platform as on ours.
QC double-programming, reconciled by hash
- The QC agent suggests an independent second build of the dataset.
- The platform content-hashes the production build and the QC build, and confirms they match before sign-off is allowed.
Why it mattersDouble programming converges to a provable match, not a manual eyeball compare, so QC sign-off rests on evidence rather than judgment.
Agents see metadata, never patient rows
- Agents receive specs and bounded column profiles, never subject-level data.
- Patient-level rows stay inside the platform and never cross to the model.
Why it mattersYou get assistance without exposing subject data, which keeps privacy and compliance intact while the agents still do useful work.
Metadata only
Subject-level analysis data never leaves the platform, the model sees only specs and bounded metadata.
Every proposal and sign-off, on the record
- Apply, reject, and sign-off events are append-only, each carrying the actor, server time, and a content hash.
- The ledger links every agent proposal to the human action that accepted or rejected it.
Why it mattersAn inspector can see exactly which AI suggestion was accepted, by whom, and when, with nothing reconstructed under deadline.
| When | Actor | Event | Hash |
|---|---|---|---|
| 09:14:02 | j.okafor | ADSL sign-off | 7c1a…e9 |
| 09:02:51 | system | QC build matched | b40f…2a |
| 08:47:19 | m.reyes | derivation applied | a3f1…9c |
| 08:31:08 | derivation agent | proposal hashed | 5d8e…11 |
Built for derivations regulators can trust
| Capability | Manual programming | Generic AI copilot | SAS-only stack | BuildADaMs |
|---|---|---|---|---|
| One spec, matching R and SAS output | ||||
| Propose-only AI with a hashed audit trail | ||||
| Deterministic fallback, no hard model dependency | ||||
| QC double-programming reconciled by hash | ||||
| Metadata-only AI, no patient rows to the model | ||||
| Append-only Part 11 audit trail |
One derivation engine, six points of view
Biostatistician
Trust that every analysis variable traces back to a frozen SDTM source and a reviewed spec, with the derivation visible for any value.
Lead statistical programmer
Author the derivation spec once, see every dataset's state on one board, and approve gates with the agent proposals and evidence attached.
QC programmer
Take the QC agent's independent build, confirm the hash match in one place, and sign off knowing the compare is provable.
Medical monitor
Read the analysis-ready ADaM with confidence that flags like treatment-emergent followed a reviewed rule, not an undocumented script.
Submission lead
Ship analysis datasets with the generated R and SAS and a clean trail of which AI suggestion was accepted and by whom.
QA / auditor
A read-only, tamper-evident trail links every agent proposal to a human decision, ready for inspection on demand.
Regulatory rigor, by construction
The controls auditors look for are inherent in the system, not bolted on after the fact.
21 CFR Part 11
A gated workflow with human sign-off at every gate, server-time stamping, and access control on every dataset.
AI audit trail
Every agent proposal is hashed and linked to the user action that applied or rejected it, so AI involvement is fully traceable.
Metadata-only AI access
Agents never see patient rows, they receive only specs and bounded metadata, so subject data stays inside the platform.
Tenant isolation
Row-level security separates every tenant's data and audit at the database, verified through the request path.
Derive one of your analysis datasets in a 30-day pilot
See BuildADaMs ingest your SAP, propose derivations, generate R and SAS, and reconcile QC by hash in days. Humans approve every step, and the code is yours to keep.
- We load one of your studies into a private workspace
- Your team runs spec to sign-off on an analysis dataset
- You keep the generated R and SAS
- A readout on QC time saved against your current build
info@the-bdkm.com · BDKM LLC · Back to Life Sciences