The factory of
the future — built on
the factory you run today
We don't replace your plant. We map every stage of your production flow, then layer AI on top of your existing ERP, MES and WMS — turning manual control points into measurable advantage, gate to dispatch.
Inward gate
Supplier delivery to goods receipt note
Every part that enters your plant passes this gate. Document mismatches, manual QC sampling and slow GRN posting create the first bottleneck — and the cheapest place to buy quality before defects travel deeper into the line.
- Paper delivery challans transcribed into ERP by hand
- PO–invoice–GRN matching done overnight in Excel
- Spot-check QC catches escapes after put-away
AI sits on the gate camera feed, ASN data and your ERP receiving APIs — no core config change required.
Reads challans and invoices at the gate, matches lines to open POs and flags mismatches before unload.
–60% gate hold timeCamera-based counting and packaging integrity checks on pallets as they roll off the truck.
2–4× sample coverageBuilds a provisional goods-receipt in ERP for clerk confirmation — cut typing, keep control.
–45% GRN labourLive defect, delay and short-ship rates push into purchasing for vendor ranking.
Weekly vendor heat-mapWarehousing
Put-away, storage discipline and retrieval
Inventory accuracy underwrites every downstream plan. Slotting errors, shadow stock and slow cycle counts quietly inflate working capital and starve the line.
- Locations updated late or not at all after moves
- Cycle counts freeze an aisle for half a shift
- Hot SKUs buried behind slow movers
Computer vision and slotting models read WMS events and RFID / camera feeds without replacing the WMS.
Cameras or handheld scans reconcile bin labels against WMS — silent cycle counts every shift.
Inventory accuracy → 99%+Recommends bin moves from demand velocity so hot parts live near the pick face.
–25% pick travelSuggests the next open location by class, weight and FIFO rules in real time.
–30% put-away timeFlags locations with no movement vs book, and pushes a directed recount.
Working-capital liftKitting
Bill of materials to staged line kits
Wrong, incomplete or late kits are the quiet line-stoppers. Manual kit checks and paper BOMs leave assembly waiting — and quality hunting missing fasteners after the fact.
- Paper pick lists mixed with tribal knowledge of substitutes
- Missing parts discovered only at station 3, not at kit build
- No photo proof of kit completeness at handoff
Vision kit verification and demand-synced sequencing sit on MES work orders and WMS pick tasks.
Checks every cavity and fastener against the eBOM photo set before the cart leaves kitting.
Incomplete kits → near zeroOrders kit builds to match the live assembly sequence — not yesterday’s schedule print.
–40% feeder rework walksSurfaces open engineering changes on the kit screen the moment MES loads the WO.
Zero stale BOM pullsBinds kit carton ID to the unit VIN / serial at line feed for full traceability.
Audit-ready in secondsAssembly
Station work, torque integrity and line balance
This is where value is created — and where small process drifts become scrap, rework and warranty. Operators need guidance that adapts; engineers need signals before scrap piles up.
- Work instructions on paper or static screens lag ECNs
- Torque guns write data few people ever look at
- Bottleneck station identified by gut, not by cycle-time histogram
Station AI coaches, torque anomaly detection and vision Poka-Yoke layer onto MES and tool controllers via APIs.
Context-aware work instructions that change with WO, option codes and live ECN status.
–50% first-week errorsLearns normal torque curves per fastener and flags drift before the joint is sealed.
Escape rate ↓Confirms part presence, orientation and label match before the station releases.
Wrong-part stops → 0Surfaces stations that exceed takt from real cycle clocks — not from a whiteboard estimate.
OEE +5–12 ptsPre-delivery inspection
End-of-line quality buy-off
PDI is your last hard gate before the customer. Manual checklists miss visual defects under fatigue; rework loops eat capacity that should ship.
- Subjective visual standards vary by shift and inspector
- Rework reasons coded late or as “misc”
- Customer claims look nothing like the PDI defect taxonomy
Multi-angle vision models and structured defect taxonomies write straight into QMS / MES quality events.
Trained on your defect library — scratches, gaps, missing labels, wrong stickers.
Escape ↓ 40–70%Routes the unit to the right bay with the exact station history attached.
–25% rework cycleMaps customer claims back to PDI images and station data for closed-loop CAPA.
Hours → minutesTightens or eases check depth from live defect rates — not a static AQL card.
Capacity back to linePackaging
Pack, label, palletise and ship-ready
Wrong labels, incomplete packs and damaged pallets are expensive after the gate. Packaging is often under-instrumented — yet every carton is a customer moment.
- Customer-specific labels printed from local macros
- Carton contents checked by eye against a packing list
- Stretch-wrap / corner-board discipline is tribal
Label vision, pack verification and pallet integrity checks sit between WMS pack confirm and the TMS ASN.
Reads every printed label against the shipment order before the carton closes.
Mis-label → ~0Confirms SKU count / barcode presence against the packing list in the station cell.
Short-pack claims ↓Auto-archives wrap / corner / label photos with the shipment ID for claims defence.
Claim handle time ↓Writes confirmed pack contents back so the ASN the customer sees matches the dock.
Dock disputes ↓Logistics & dispatch
Yard, load, track and prove delivery
From dock to customer doorstep: yard chaos, late carriers and opaque POD chains burn service levels. AI closes the plant loop without replacing your TMS.
- Trucks called in by phone; dock doors assigned on a whiteboard
- OTIF measured after the month closes
- POD images scattered across carrier portals
Yard vision, ETA models and POD aggregation layer onto the TMS and carrier EDI / API feeds.
ANPR + appointment logic assigns doors and predicts gate wait from live yard occupancy.
Dwell –30–50%Blends TMS plan with GPS / traffic and pushes proactive delay notices to CS.
OTIF visibility livePulls carrier PODs and damage photos into one plant-owned case file.
Dispute cycle ↓Suggests carton / pallet stacking and route grouping to raise cube utilisation.
+8–15% cube useBook a factory audit
A 2–3 day on-site assessment of your plant, gate to dispatch. We map every stage, shortlist the highest-ROI AI applications, and model the business case on your own data — before any code is written.
AI should make your existing systems smarter — not make them obsolete.
Every AI application reads from and writes to your current systems via secure APIs. No rip-and-replace. No production interruption. Reversible in one configuration change.
Non-disruptive integration
We work on top of your existing ERP, MES and WMS — no system replacement, no data-migration risk, no downtime during deployment.
Transparent ROI at every gate
A clear business case before each phase. You approve scope and budget stage by stage — no open-ended commitment.
Your team owns the outcome
Every tool ships with documentation, a trained internal champion, and a 90-day hypercare SLA.
Factory AI, answered plainly
The questions plant heads, quality directors and CFOs ask us before booking an audit.
2–3 days on-site, walking all seven stages from inward gate to dispatch. You receive a quantified bottleneck map per stage, a shortlist of 5–7 AI use cases ranked by modelled payback, and a finance-ready ROI report by week 5 — before any deployment commitment. Book a factory audit.
No. Every application connects through vendor-supported APIs on top of your existing systems — no system replacement, no core configuration changes, no downtime during deployment. The layer is reversible in one configuration change, and your plant operates identically with it switched off. .
Most plants start with a single Quick Win application — live in production within 6–8 weeks of proof-of-concept start. Quick Wins typically pay back in 3–7 months on reclaimed labour hours and avoided escapes alone, modelled conservatively on your own volumes and validated by your finance team before work begins.
It depends on your constraint, but the most common answer is the inward gate: it has the smallest hardware footprint, the clearest KPI (gate-to-GRN cycle time), and it is the cheapest place in the plant to buy quality — a defect caught at receiving costs 10–20× less than the same defect caught at final inspection. .
Neither, in the sense usually feared. The AI removes document handling and manual matching, not people; deployed plants process 30–50% more deliveries with the same team. Every deployment ships with a trained internal champion, full documentation and a 90-day hypercare period, so your team owns the system after handover.
See your factory's transformation map
A structured, non-disruptive pathway from discovery to live AI applications running in your plant — first application live by week 12.