Quality Analytics
Trending that does not wait for the annual review
Litewave turns the records you already execute into structured quality data, so yield, deviations, OOS rates, supplier performance and cycle times are live all year instead of reconstructed once at review time.
Most quality data is not missing, it is unreadable
It exists. In a binder, in a scanned PDF, in a free-text deviation description, in a LIMS export nobody joins back to the batch record.
So the annual product quality review becomes archaeology: weeks of manual extraction to produce a document describing a year you can no longer do anything about. Meanwhile the signals that mattered were visible in month three. A yield trending down inside the acceptable range. One supplier assay creeping toward the limit. A deviation category quietly doubling on one line.
Our API customer put it exactly: yield varied within acceptable ranges, but the lack of integrated data made it impossible to identify the underlying drivers. That is the real cost of unstructured records. Not non-compliance. Blindness.
How it works
- Executed batch and production records
- In-process and analytical results
- Deviations, OOS and OOT records, CAPA and change control
- Complaints, returns and stability data
- Environmental monitoring and supplier CoAs
- Equipment and downtime logs, LIMS, MES and ERP exports
- Normalizes everything into one quality data model keyed to batch, product, site, line, equipment, lot and time
- Keeps every field traceable to the page it came from
- Runs statistical trending against your alert and action limits
- Detects drift and rising frequency before a limit is breached
- Live dashboards by product, site and line
- Drafted APQR, PQR and management review packs, every figure linked to its record
- Alerts on drift and emerging trends
- A clean export your statisticians can actually use
The questions it answers
Not "here is a dashboard", but the specific questions a quality director is already asking.
Batch and process trends
- Yield, cycle time, right-first-time, rejection and rework rate by product, line, shift and campaign
- Control charts, capability indices and rule-based signal detection against your limits
- Assay, impurities, dissolution, moisture and microbiological results drifting toward specification
- Which variables track most closely with yield, ranked and testable, rather than an undifferentiated wall of correlations
Deviation trends
- Frequency and category by equipment, process step, product and shift
- Recurrence detection across events that were investigated separately
- Investigation aging and closure performance against your target
- CAPA effectiveness outcomes, including the ones that did not hold
Supplier trends
- Acceptance rate and out-of-specification frequency by supplier and material
- Result drift across lots from the same supplier
- Late or missing documentation, and repeated method changes
- Supplier scorecards built from the CoA review rather than maintained by hand
Laboratory and environment
- OOS and OOT trending including invalidation rates by laboratory, analyst, method and instrument
- Environmental monitoring excursion frequency by room, grade and sample location
- Utility monitoring against the manufacturing window
- Cross-site benchmarking where the same product runs in more than one place
What the regulations actually ask for
Requirement
21 CFR 211.180(e)
What it obliges
An annual evaluation of records for each drug product, to determine the need for changes to specifications or procedures.
Requirement
EU GMP Part I
What it obliges
A regular periodic Product Quality Review across batches, materials, deviations, changes and stability.
Requirement
ICH Q10
What it obliges
Monitoring of process performance and product quality as a standing element of the pharmaceutical quality system.
Requirement
21 CFR Part 117
What it obliges
Verification activities and reanalysis of the food safety plan at least every three years, or upon significant change.
Where the evidence comes from
The API deployment reports improved batch yield efficiency through performance driver identification. We have deliberately not attached a percentage to that: no yield improvement figure has been verified and published, and inventing one would be the easiest claim on this site to disprove.
Questions we get asked
Do we need to be fully digital first?
No. The structured data comes from the records you already execute, paper included. That is the point: analytics becomes a byproduct of the review rather than a separate program with its own budget.
Is this a Design of Experiments or digital twin tool?
No, and we are not trying to be one. Litewave reads the batches you have already executed, which makes it observational: it can show you that a parameter moves with yield, not that changing it causes the change. Establishing that is what a designed experiment is for, and simulating it is what a mechanistic twin is for. Both live in process development. Litewave works on the commercial record, so what it gives you is a trended, structured view of what your process has actually done, which is useful input if you later run a study and is the continued process verification layer either way.
Does this replace our BI tool?
No. Litewave produces the structured, traceable quality dataset most sites do not have. You can consume it in Litewave dashboards or export it into the BI and statistics tooling you already run.
How do we defend a number that came out of an AI system?
Every figure links to the executed record and page it came from. An inspector can drill from a trend line to a scanned batch page in the same session.
Can it trend across sites running different systems?
Yes, and that is the normal case. Litewave normalizes to a common model, so a product made at two sites on two different platforms can be compared without harmonizing the platforms.
Related
Built for validated environments
Your data stays where your auditors expect it
Deploy in your environment
Your cloud, your data center, on the plant floor, or fully air-gapped. The same agents run identically in all four, because the models are open-source and served inside your perimeter, so inference never leaves it.
Your data never trains our models
Documents are never sent to third-party AI providers, and they never train a model that any other customer touches. Where a model does adapt to your site, the handwriting engine learning your operators, that loop is closed inside your deployment and what it learns stays yours.
21 CFR Part 11
Electronic records, electronic signatures and complete audit trails on every action, built to ALCOA+ data integrity principles.
EU GMP Annex 11, and building for what is next
Meets the currently effective Annex 11 expectations for computerised systems. We are building against the draft revised Annex 11 and the draft Annex 22 on AI while both remain in consultation, so nothing here is claimed as compliance with a standard that is not yet in force.
Validated like the computerised system it is
A risk-based validation package written to GAMP 5 second edition and Annex 15 expectations: specifications and traceability matrix, IQ, OQ and PQ protocols with executed evidence, and a supplier assessment pack, so your quality unit can qualify Litewave under its own CSV or CSA procedure.
AI governance you can inspect
Context of use, model versions, performance monitoring and change control are documented against the EMA and FDA guiding principles of good AI practice in drug development, so the model sits inside your quality system rather than beside it.
Evidence on every flag
Every extraction, check and recommendation links back to its source page. Nothing asserted is unverifiable.
Human in the loop by design
Agents propose, qualified people approve. Litewave never dispositions a batch on its own.
See it run on one of your own records
Bring something real. We will show you what Litewave finds in it and what it cites as evidence.