AIEight AI features that think for you
PlatformSee the full QC suite
IndustriesBuilt for food manufacturing
PricingTransparent, modular plans
10×faster traceability
100%audit-ready compliance
60%less paperwork
<30sdeviation alerts

Replace spreadsheets and paper logs with
one connected quality platform.

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◆ AI · Built into every shift

Your factory
has a nervous system now.

Every machine, every QC check, every shift handover — flowing through one model that's been trained on your plant. It sees patterns before your team does. It surfaces what matters before you ask.

Read the technical brief
NERVOUS_SYSTEM.LIVESTREAMING
PRODUCTION
QC
SUPPLY
MAINT.
PEOPLE
AUDIT
METRICS
ALERTS
◆ LIVE · LAST 60 SECONDS ON YOUR PLANT2,847 events processed
14:23:01INGESTED142 QC checks
14:23:02INFERSPC drift · Bakery-2
14:23:04RANKhumidity 87% · op-shift 64%
14:23:06TRAINoperator confirmed root cause
14:23:09ALERTMixer-01 vibration trending
14:23:12CITE4 lots · 2 SPC charts
14:23:15PREDICTshift yield → 96.4%
14:23:18INFERshelf-life model · Lot L-22841
14:23:21TRACELot L-22810 · 4-min trace
14:23:24ANSWERwhy is yield off? · cited
14:23:01INGESTED142 QC checks
14:23:02INFERSPC drift · Bakery-2
14:23:04RANKhumidity 87% · op-shift 64%
14:23:06TRAINoperator confirmed root cause
14:23:09ALERTMixer-01 vibration trending
14:23:12CITE4 lots · 2 SPC charts
14:23:15PREDICTshift yield → 96.4%
14:23:18INFERshelf-life model · Lot L-22841
14:23:21TRACELot L-22810 · 4-min trace
14:23:24ANSWERwhy is yield off? · cited
14:23:01INGESTED142 QC checks
14:23:02INFERSPC drift · Bakery-2
14:23:04RANKhumidity 87% · op-shift 64%
14:23:06TRAINoperator confirmed root cause
14:23:09ALERTMixer-01 vibration trending
14:23:12CITE4 lots · 2 SPC charts
14:23:15PREDICTshift yield → 96.4%
14:23:18INFERshelf-life model · Lot L-22841
14:23:21TRACELot L-22810 · 4-min trace
14:23:24ANSWERwhy is yield off? · cited
14:23:01INGESTED142 QC checks
14:23:02INFERSPC drift · Bakery-2
14:23:04RANKhumidity 87% · op-shift 64%
14:23:06TRAINoperator confirmed root cause
14:23:09ALERTMixer-01 vibration trending
14:23:12CITE4 lots · 2 SPC charts
14:23:15PREDICTshift yield → 96.4%
14:23:18INFERshelf-life model · Lot L-22841
14:23:21TRACELot L-22810 · 4-min trace
14:23:24ANSWERwhy is yield off? · cited
8 Capabilities · One Model

Every feature shows its work.

Eight things our AI does on your plant — grounded in real data, never a black box.

01Anomaly detectionCatches drift hours before tolerance breach.
02Root cause rankingTop causes ranked by confidence, every one cited.
03Yield predictionEnd-of-shift forecast from the first 90 minutes.
04Recall traceLot to finished pallet in under five seconds.
05Schedule optimizationRe-sequences WOs around capacity and changeovers.
06Predictive maintenanceSchedules service before machines break.
07Audit pack generationAuditor asks. AI assembles. Every record cited.
08Plain-English answersAsk in your words. Get cited answers from your data.
Two we're proud of

A closer look.

The two capabilities that change a plant manager's day the most.

08 · PLAIN-ENGLISH ANSWERS

Ask in your words.
Get cited answers.

No SQL. No dashboards to memorize. Type the question the way you'd ask a shift lead — the model pulls from your QC, OEE, supplier, and operator data and answers in the same language, with every claim linked to the record it came from.

  • → Speaks your plant's terminology, not generic jargon
  • → Every number cites the WO, lot, or chart behind it
  • → Answers in seconds — even comparative ones across shifts
ask.quabook · plant manager
How does this Tuesday compare to last Tuesday?
Throughput is up +4.2% versus last Tuesday — driven mostly by Bakery-2, which had no breakdowns this week. QC reject rate is flat (0.6% both weeks).
423 WOs4 OEE charts2 shift reportsview sources →
Why was Bakery-2 so clean
audit-pack-CCP2-BRC.pdfSIGNED · 14 OPS
CLAUSE 4.11.6 · CCP-2 VERIFICATION
Six months of evidence. Zero gaps.
PAGES
384
VERIFICATIONS
7,402
GAPS
0
VERIFICATION DENSITY · 6 MONTHS
JANJUN
generated in 3.1s→ ready to share
07 · AUDIT PACK GENERATION

Auditor asks.
AI assembles.

Type the clause, the date range, the scope. The model pulls every verification, every signature, every corrective action — and assembles a defensible audit pack in seconds. The work that used to take a week of binders happens before the auditor's coffee gets cold.

  • → BRC, SQF, FSSC, customer-specific schemes
  • → Every signature traced to the operator and timestamp
  • → Gaps flagged before they become findings
The Gap

What changes when AI is in the loop.

✗ Before — manual plant
✗Friday afternoon: notice yield is off. Pull six spreadsheets to investigate.
✗Saturday: trace the lot back through paper logs. Three hours, half a binder.
✗Monday: present root cause to QA director. Best guess. Confidence: low.
✗Wednesday: run the same product. Same problem. Different shift.
✗“We’ll add it to the meeting agenda.”
A I → I N L O O P
→ After — QuaBook AI
→14:02 · AI flags the drift the moment SPC slips one sigma.
→14:02 · Lot trace, supplier history, and operator log appear in one panel.
→14:03 · Top-3 root causes ranked by confidence (87%, 64%, 41%).
→14:04 · Suggested correction sent to operator kiosk. Confirmed.
→“Fixed before the next shift started.”
How it works

Trained on your plant. Not the internet.

Every QuaBook deployment ships with a model that learns from your machines, recipes, suppliers, and shift patterns. It doesn't hallucinate generic factory advice — it answers from your data, citing the lot numbers, work orders, and SPC charts it pulled from.

  • 1Continuous training. Every QC check, every lot, every alert feeds back. Accuracy compounds shift after shift.
  • 2Cited answers, always. Every recommendation links back to the WO, lot, or chart it came from. You audit the AI like you'd audit an operator.
  • 3Your data stays your data. Tenant-isolated training. Nothing leaves your plant's instance — not for training, not for inference.
model.quabook.your-plantv 4.2 · TRAINED ON YOU
14:23:01[TRAIN]ingested 142 QC checks · WOs WO-04391..04412
14:23:02[INFER]SPC drift on Bakery-2 weight · σ=1.34 · alerted
14:23:04[INFER]root cause ranked: humidity (87%) · op-shift (64%) · raw-lot (41%)
14:23:06[TRAIN]operator confirmed: humidity. confidence updated.
14:23:09[ALERT]Mixer-01 vibration trending → maint. WO-MNT-0421
14:23:12[CITE]answer cited 4 lots, 2 SPC charts, 1 supplier audit
PLANT EVENTS · TODAY
2,847
ROOT-CAUSE ACCURACY
87.4%
DECISIONS CITED
100%
◆ Get the brief

Your plant is generating signal.
It's time to listen to it.

Tell us about your facility and we'll show you what AI can do with your production data — before you sign anything.

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