Shorter
Batch release cycle time
DNAI™ root cause in minutes vs hours of manual investigation across LIMS, ERP, and paper records.
“Why isn't Batch XYZ releasing?” — answered in seconds, with that answer linked to the exact evidence of what isn't working and our best explanation for why and how to fix it.
Datanoetic's auditable, grounded intelligence is built for the standards you operate under. Every root cause is cited from your audit-logged records. Every recommended action awaits QA approval. Every process improvement is traceable from trigger to deployment.
The improvement, KPI by KPI.
DNAI™ resolves today's batch incident; DNOVA™ removes the cause for good.
Fewer
Batch rejection / hold rate
DNOVA™ eliminates chronic equipment-driven deviations — not just resolves individual batch holds.
Direction of improvement shown where no figure is given; we model the numbers on your value stream in the first 30 days. Results depend on operational baseline, data connectivity, and deployment scope. All data connectors are audit-logged and tenant-isolated; the platform is designed for deployment in GxP-regulated environments (21 CFR Part 11 / GDP / GxP).
Where pharma supply chains lose value. And how Datanoetic closes each gap.
Gap 01
QC hold root causes are buried across LIMS, ERP, and manual records — hours to investigate a single batch.
The challengeWhen Batch XYZ goes on hold, the investigation requires separate logins to LIMS, ERP, CMMS, and paper batch records. Cross-referencing them to identify whether Equipment E-06's calibration drift caused the yield suppression — and that it happened 94 minutes before the first failing test — takes hours.
Every hour is a batch release delay and a regulatory risk.
How Datanoetic closes it
DNAI™ KPI Guard monitors every published VSM node continuously. When Batch XYZ's yield drops below threshold at QC Release, an Insight Card surfaces on that specific node — citing Equipment E-06's calibration flag from CMMS, the 3 prior correlated yield events from LIMS, and the recommended action — awaiting QA supervisor approval.
Root cause in 312ms, not hours.
Gap 02
LIMS, CMMS, ERP, and environmental monitoring are siloed. No single view connects the batch lifecycle.
The challengeA regulated manufacturing operation generates data across four to six systems — each knowing part of the batch story. LIMS knows QC results. CMMS knows equipment calibration state. ERP knows the production schedule. Environmental monitoring knows excursion events.
None of them know how Equipment E-06's calibration drift connects to Batch XYZ's yield suppression — until the investigation starts.
Four systems · one batch lifecycle
LIMS CMMS ERP env_monitoring
QC Release · Batch XYZ · live
One live view — every feed on the node that owns it.
Audit-logged, read-only connections — your systems stay in place.
How Datanoetic closes it
Datapro-V™ maps your end-to-end regulated operation as a live VSM — every batch step, every KPI anchored to the step that owns it, every equipment entity and data source assigned to the steps they affect. Audit-logged, read-only connectors to LIMS, CMMS, ERP, and env_monitoring feed a single tenant-isolated layer.
Source audit trails are preserved, not replaced; the cross-system view is assembled from what already exists.
Gap 03
Generic AI gives general answers. It doesn't know your batches, your equipment, or your compliance requirements.
The challengeGeneral-purpose AI assistants can describe GxP best practices. They cannot tell you that Batch XYZ is failing at QC Release because Equipment E-06's calibration flag was raised in CMMS 94 minutes before the first failing test result, that this correlates with 3 prior yield events on Line 2 in the last 7 days, and that the QA supervisor needs to approve escalation to maintenance and re-routing of Batch ABC to Line 3 — with every step audit-logged.
Same question, two answers
Generic assistant General industry knowledge
Yield holds are usually caused by temperature variance, operator error or recent maintenance. It would be worth checking your batch records and equipment logs.
DNAI
Equipment E-06 calibration drift — the flag was raised in CMMS 94 minutes before the first failing test.
Grounded in your operation — every answer cited to source records.
How Datanoetic closes it
DNAI™ reasons from your Knowledge Graph — every batch, every equipment entity, every LIMS assay, every compliance threshold — not from general training data. KPI Guard generates hypotheses grounded in your process graph, cites the specific audit-logged records it used, and proposes a QA-approvable action.
Every step is traceable: source record → hypothesis → recommendation → approval → deployment.
How the workflow changes.
Hours across LIMS, ERP, CMMS and paper records
Batch XYZ goes on hold. The QA lead opens LIMS, cross-references ERP, pulls CMMS calibration records, checks paper batch records.
Three hours later: Equipment E-06's calibration drift identified as root cause, manual action proposed, manually documented.
DNAI™ surfaces the E-06 calibration flag in 312ms — citing LIMS, CMMS, and ERP simultaneously.
The QA supervisor confirms the recommended action: escalate E-06, re-route Batch ABC to Line 3. Decision is faster, fully audit-logged, traceable from record to approval.
Seconds, cited and audit-logged
The same equipment hold cannot recur.
After eight weeks of DNAI™ incident data, DNOVA™ identifies: Equipment E-06 calibration events correlate with batch yield suppression at 91% confidence across 7 incidents.
DNOVA™ proposes three changes — an automated pre-batch calibration check for E-06 before Line 2 assignments, a dynamic line routing rule (if E-06 calibration age exceeds 48 hours, auto-assign batch to Line 3), and a maintenance interval reduction from 14-day to 10-day for the E-06 class. Awaits process owner and QA sign-off.
On approval, rules are codified in Datapro-V™ and executed automatically for all future batches. E-06-related batch holds eliminated. DNOVA™ loop restarts on remaining drivers.
Automation design codifies each validated action into a standing rule — every rule gated by human sign-off, never autonomous.
Explore the platform30 minutes. One scenario. Your batch data.
We'll walk your team through the Batch XYZ QC hold scenario on a sample VSM that mirrors your GxP-regulated operation — and one compliance KPI you wish you could explain in real time.