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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No credit card required · Setup in under a week

◆ 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.

Send us a sample of your data. We'll train a model on it and show you what it finds — before you sign anything.