A pharma batch is finished, but the record isn't. The batch lived as fragments — DCS trends, weigh-station logs, lab result PDFs from the QC LIMS, in-process check sheets typed into the MES, two deviations written up at 03:40 and 11:15, and a CIP cycle that ran an extra 14 minutes for reasons that are now sitting in someone's head. Stitching that into a single, signed, Part 11-defensible electronic batch record is the job that consumes most of QA's review week. EY's published analysis on EBR systems describes the prize directly: a successful review-by-exception implementation can compress what was a 150-page batch record review down to a roughly 3-page exception report. The eBR Drafting AI is what closes that gap. The LLM drafts narrative sections from the raw process data, lab results, manual entries, and deviations. A deterministic rule engine reconciles every signature, timestamp, and batch-genealogy link against 21 CFR Part 11 requirements. A tabular ML model flags numerical inconsistencies the LLM can miss. QA reviews the draft, signs the record. The AI never signs. It never closes the batch. It produces evidence; humans produce the release decision. Built on Llama 3.1 70B with mandatory rule-engine validation, runs on an on-site RTX PRO 6000 Blackwell appliance inside your validated environment, live in 6–12 weeks from PO. We're walking through the full architecture — drafting, rule checks, Part 11 audit trail — on a live webinar — register here.
Electronic Batch Record Drafting
& Auto-Reconciliation AI For Pharma Plants
An LLM drafts the narrative sections of your eBR from raw DCS data, LIMS results, manual entries and deviations. A rule engine reconciles every signature, timestamp and batch-genealogy link against 21 CFR Part 11. A tabular ML model flags numerical drift. QA reviewer reads, edits, and signs — the AI never does. Compresses batch record review from days to hours, with the audit trail intact. Owned outright, runs inside your validated zone.
The Batch Record Review Cycle Is Where Release Time Goes To Die
A pharma batch can sit waiting for a finalised, signed eBR longer than it took to manufacture. Industry coverage of EBR transformations describes review cycles routinely cut by 40–60% when documents are digitised and routed properly — and this is before any drafting AI touches the record. The drafting AI doesn't replace the review. It removes the manual reconstruction step that precedes the review. Talk to our pharma lead about your current batch release cycle time.
Manufacturing supervisor stitches DCS, LIMS and MES outputs into a draft. QA finds inconsistent timestamps, missing signatures, deviation references that don't match. Round trips between Production and QA run for days before the record can even enter formal review.
LLM produces the narrative draft from validated source data. Rule engine certifies every signature and timestamp. Tabular ML flags numerical anomalies. QA reviewer opens a draft that's already internally consistent — they edit, query, and sign. The 21 CFR Part 11 chain is intact end-to-end.
An AI that signs an eBR step or releases a batch is operating outside Part 11. The drafter has no signature capability — none. It produces a draft for human review. QA signs. QP releases. Always.
What Goes In, What Comes Out — A Drawn Schematic
A schematic of the source-to-draft pipeline. The drafter pulls from systems you already operate — DCS, LIMS, MES, QMS, scale and weigh-station logs, manual entry forms, deviation logs. It produces a single draft eBR with every claim traceable to a source row. The diagram shows the ingestion, the three reasoning models, and the rule-engine gate that blocks any draft missing a required signature, timestamp or genealogy link.
Generates narrative sections — process summary, in-process control commentary, deviation impact descriptions — from retrieved source rows. Cited claims only.
Verifies every signature, timestamp sequence, and batch-genealogy link against Part 11 § 11.10 and § 11.50. Not a model — it's hard rules. The blocker on bad drafts.
Gradient-boosted model trained on historical batches. Flags numerical drift the LLM can miss — yields out of trend, weights inconsistent with charge log, hold times unusual for product.
Every claim cites a source row. Rule-engine pass/fail visible per section. Anomaly flags shown next to the relevant data. Goes to QA reviewer queue. AI never signs.
A Section Of A Draft eBR — What The Reviewer Sees
Below is an illustrative excerpt of the kind of draft section the AI produces. Three things to notice: every numeric claim carries the source citation, the rule engine has stamped the signature/timestamp section either passed or flagged, and the tabular ML has annotated the in-process control row with an anomaly flag the reviewer can drill into. This is representative of one section — a real draft has 30 to 60 such sections per batch.
Granulation of Product 44 commenced at 02:14 on 18-Apr-2026 in Granulator GR-03. Total granulation time was 22 minutes, within the validated range of 18–25 minutes. End-point moisture by NIR was 2.1% (target 1.8–2.4%). The batch transferred to fluid-bed dryer FB-02 at 02:38. No alarms were raised during the granulation step.
| Parameter | Spec | Result | Source | Flag |
|---|---|---|---|---|
| Granulation time | 18–25 min | 22 min | DCS GR-03 | Within |
| End-point moisture | 1.8–2.4% | 2.1% | LIMS Result-99214 | Within |
| Granulation liquid charged | 22.0 ± 0.4 kg | 22.6 kg | Scale SC-12 / Charge log | ML flag |
| Outlet air temp | 30–38 °C | 34 °C | DCS GR-03 | Within |
ML flag detail: Granulation liquid charge of 22.6 kg sits 0.2 kg above the upper validated range. Tabular model assigns this an anomaly score of 0.71 (threshold 0.50). Reviewer should confirm whether a deviation has been raised against this charge or whether the validated range should be re-examined.
LLM For Narrative, Rules For Compliance, Tabular ML For Drift
An LLM alone is the wrong tool for an eBR. It writes well and hallucinates. Rules alone are the wrong tool — they validate but cannot draft. Tabular ML alone misses everything that isn't a number. Together, each does what it's good at, and the gap between them is filled by the others. We walk through the full stack on the live webinar.
The LLM is constrained to retrieval-only generation. It drafts process summaries, in-process control commentary, deviation impact narratives — only from retrieved rows of your DCS, LIMS, MES, and QMS data. Every numeric claim is bound to a source. Free-form base-model knowledge is excluded for any regulated section. If retrieval is empty, the section is left blank for human input — never invented.
Not a model. Hard, auditable rules — every required signature present, every timestamp in correct sequence, every batch-genealogy link resolved, every electronic signature carrying user ID, action, and date-time as Part 11 § 11.50 requires. If a rule fails, the section is flagged. The draft cannot exit the system without either a clean pass or an explicit reviewer override with reason.
Gradient-boosted model trained on your historical batches for each product. Looks at yields, charge weights, hold times, in-process test results — flags rows that drift from the product's historical pattern even when they fall inside spec. Anomaly score is shown to the reviewer, not used to block release. Catches the drift the LLM and the rules both miss.
What Part 11 Requires & How The Drafter Maps To Each Requirement
21 CFR Part 11 § 11.10 lists the controls electronic record systems must implement. The drafter is not a system of record — your validated MES/eBR system is. The drafter is a content generator that operates on top of, and writes back to, that system of record. The mapping below shows where the drafter contributes and where the underlying validated system carries the obligation.
| Part 11 control | What it requires | How the drafter contributes |
|---|---|---|
| § 11.10(a) System validation | Validated to ensure accuracy, reliability, and consistent intended performance | Drafter ships with URS, FS, IQ, OQ, PQ test scripts. Citation accuracy and rule-engine pass-rate measured during validation. |
| § 11.10(e) Audit trail | Secure, computer-generated, time-stamped record of operator actions | Every drafter action — section generated, source rows retrieved, rule check, reviewer edit — written to immutable audit trail. |
| § 11.10(g) Authority checks | Only authorised individuals can use the system or sign records | Drafter has read access only to source systems and write access only to draft scope. Sign-off authority lives with QA in the eBR system. |
| § 11.50 Signature manifestations | Signed records carry signer name, date-time, and meaning of signature | Rule engine verifies each signature on the draft has all three components before allowing reviewer routing. |
| § 11.70 Signature/record linking | Signatures cannot be excised, copied, or transferred to falsify a record | Drafter never signs. Signatures remain bound to the eBR system's signature engine — drafter touches narrative content only. |
| § 11.30 Open systems | Authenticity, integrity, confidentiality from creation to receipt | Drafter runs on-prem, air-gapped from the public internet. Source data and drafts never leave your validated zone. |
What this means in practice: the drafter is a Part 11-aware tool, not a Part 11 system of record. Your validated eBR / MES system retains the records and the signatures. The drafter speeds up how those records get filled in — and gives the reviewer a head start on review-by-exception.
5 Steps From Batch End To Signed Record
The five-step workflow below is what a typical batch goes through with the drafter in place. The reviewer's actual decision time stays where it should — on edge cases, deviation impact, and final sign-off. The mechanical reconstruction work that used to fill the day is what the drafter absorbs.
MES emits batch-end event. Drafter pulls the time-bounded slice of DCS, LIMS, scale logs, manual entries, and deviation references for the batch.
LLM drafts narrative sections from retrieved rows. Tabular ML scores numerical anomalies. Rule engine sweeps signatures, timestamps, genealogy links. Section status set: pass / flagged / blocked.
QA reviewer sees a draft eBR with green sections, exception flags, ML anomaly notes, and clear pointers to source rows. Edits inline. Queries production where needed. The audit trail captures every edit.
QA signs the eBR in your validated MES / eBR system, not in the drafter. Signature carries name, date-time, and meaning per § 11.50. The drafter has no sign capability.
Qualified Person reviews the signed eBR alongside the deviation register and CoA, makes the release call. Drafter has no role here — it's already done its job upstream.
From PO To Live Drafter, In Three Phases
A pharma site is not a greenfield. There is an MES, a LIMS, a QMS, a historian, and a validation team. Deployment is staged so each phase produces a validation deliverable — not just running software. Live in 6–12 weeks from PO, with global dispatch on the appliance and field engineers on the floor for cabling, integration, and corpus calibration.
RTX PRO 6000 Blackwell appliance ships pre-loaded, racks at your site. Field tech handles power, network, air-gap zoning. Your DCS, LIMS, MES/eBR, QMS data sources are mapped under read-only credentials. URS / FS drafted for your validation team. Recipe templates and product master catalogue indexed.
Drafter enabled for one product family in advisory-only mode (drafts produced but not routed to QA queue yet). IQ and OQ test scripts run against citation accuracy, rule-engine pass-rate, and tabular ML anomaly precision. Quality observes, edge cases logged.
PQ test scripts cover real reviewer workflows. Drafter routes drafts into the QA queue for production batches. Reviewer training (3 days, on-site). 24×7 remote monitoring active. Rollout to additional product families on a schedule the validation team controls.
Recipe revisions, new products, and updated SOPs flow into the corpus on a controlled cadence. Tabular ML retrained monthly on closed batches. Quarterly review with our pharma lead — citation accuracy, rule-engine pass-rate, anomaly precision, reviewer satisfaction. Optional after year one.
Hardware, Drafter Software, Integration, Validation Support — One PO
The eBR Drafting AI is delivered as a turnkey appliance: an on-site RTX PRO 6000 Blackwell server, pre-configured and ready to rack, the drafter stack pre-loaded with rule library and tabular ML scaffolding, and our pharma + AI engineering team on the floor for source mapping, validation evidence, and reviewer training. 6–12 weeks from PO to a live, validated drafter. Owned by you outright.
Pre-racked, burn-in tested, IEC 62443 zoned. Llama 3.1 70B + tabular ML stack loaded. Air-gapped from public internet. One-time CapEx, no recurring license. Global shipping included.
LLM drafter, rule-as-code engine, tabular ML scaffolding, audit-log writer, draft-to-MES API. Pre-loaded; calibrated to your eBR templates and recipe taxonomy during weeks 1–4.
Read-only connectors to Werum PAS-X, Körber MES, Veeva Vault, MasterControl, Documentum, OSIsoft PI / Aveva Historian, Honeywell DCS, LabWare LIMS, Empower CDS. Cabling and integration handled on-site.
URS, FS, IQ, OQ, PQ test scripts, traceability matrix, rule-library version log, citation accuracy reports, anomaly precision/recall, audit-trail samples. Drafted by us, reviewed by your validation team, owned by you. Aligned with GAMP 5 Second Edition.
3-day on-site rollout: QA reviewers (draft navigation, exception flags, ML anomaly review), supervisors (routing rules), validation team (rule library internals). Plus a 1-day workshop on drafter audit log review.
24×7 remote monitoring, monthly tabular ML retraining on closed batches, quarterly review with our pharma lead. Optional after year one. Drafter keeps running either way.
What Pharma QA & Manufacturing IT Ask First
No, by architecture. The drafter has read access to your source systems and write access only to its own draft scope. Electronic signatures live in your validated eBR/MES system, where authorised users sign with their own credentials per § 11.50. The drafter has no signing capability — it doesn't exist in the tool surface, so it can't be turned on by accident.
It stays inside your validated zone. The drafter runs on-prem on the appliance, indexes your source data into an on-site vector store, and writes drafts back into your MES/eBR system through a controlled API. No data leaves your perimeter. The model is not trained on your batches — it's used at inference only against retrieved chunks.
The deployment is structured to produce validation evidence as a phase deliverable. URS, FS, IQ, OQ, PQ are drafted by us, reviewed by your validation team, signed off by QA. The rule engine has its own test scripts measuring pass-rate; the LLM has its own measuring citation accuracy and fabrication rate; the tabular ML has its own measuring anomaly precision/recall. Aligned with GAMP 5 Second Edition and the GAMP AI guidance.
Three layers catch it. The rule engine blocks any draft missing a Part 11 control — those drafts never reach the reviewer. The tabular ML flags numerical drift the LLM may have written through. And the QA reviewer is the final, human gate — they edit before signing. Citation grounding means the reviewer can always click any number in the draft and see the source row it came from. The drafter is a productivity tool, not a release decision-maker.
The corpus refreshes on a controlled cadence — typically nightly for closed batches and on-event for recipe and SOP revisions. Old revisions stay retained but flagged; the drafter always cites the effective revision number active during the batch. Your QA controls when a new revision becomes citable in production.
Drafter keeps running. You own the appliance, the model weights, the indexed corpus, the rule library, the validation evidence, and the audit logs it has produced. Renew support and monthly recalibration annually, run it in-house with our handover docs, or mix both. No kill switch, no recurring license.
Bring A Closed Batch & Its Source Data. We'll Show You The Draft The AI Would Produce.
A working session, not a pitch: bring one closed batch — DCS slice, LIMS results, MES check sheets, deviation reports (sanitised is fine). Our pharma and AI team will run it through a sandbox drafter and walk you through the output side by side with your existing eBR — citation accuracy, rule-engine flags, ML anomalies, validation evidence pattern, and the Part 11 boundary in detail. No commitment.
