Customer Results

Real Workflows.
Measurable Outcomes.

Customer deployments across food, industrial, and discrete manufacturing. Each engagement began with a specific operational problem and a clear success metric.

4 Industries
Border Transit • MRO • Pharma • Production
< 4 wk
Avg. Time to First Value
Plug-In
Works on Your Existing Data
Natural Language
Query Interface

Jump to a case study

Discrete Mfg Cross-Border
Border Transit Visibility
Mexico–US lane intelligence & predictable production
Industrial MRO
Proactive MRO Intelligence
Invisible risk to 20% revenue recovered
Pharma Supply Planning
Drug Substance Planning
What-if scenarios & 30% fewer stock-outs
Production Maintenance
Machine Uptime Tracking
Automated uptime analytics that run themselves
Discrete Manufacturing Cross-Border USMCA / Mexico–US Lanes

From Border Blindspots to Predictable
Production on Mexico–US Lanes

A manufacturer pulling components across the border tracked inbound shipments with carrier phone calls and a shared spreadsheet. Customs holds and bridge backups were invisible until parts simply didn’t show — by then a line was already starved or someone was booking emergency air freight to cover the gap.

< 4 wk
To full deployment
Zero
Border-driven line stops post-deploy

Before

  • Inbound cross-border parts tracked by carrier phone calls and a hand-updated spreadsheet
  • No visibility into customs clearance status or bridge dwell time — delays surfaced only when parts failed to arrive
  • The SAP/Oracle production schedule had no link to actual transit status; planners assumed on-time until proven otherwise
  • Reactive expediting (hot-shots, air freight) to cover gaps, blowing the inbound freight budget

What changed

  • Live border-transit feeds — carrier EDI/GPS, customs-broker clearance status, and bridge wait times — auto-sync into the production schedule every shift
  • Every inbound shipment is mapped to the production orders that consume it, so at-risk orders are flagged before the line is starved
  • Planners get days of warning — "this order will miss its start because its parts are still in customs at Laredo" — instead of finding out the morning of
  • Expedite decisions made early and selectively, not in panic

What the team actually asks it

"Which production orders this week are at risk because their parts are still south of the border?"
⚡ 3 orders at risk — Line 2 wire harness (cleared customs, ETA 36h, starts in 28h → 8h short), Line 4 bracket set (held at Nuevo Laredo, broker docs pending), Line 6 fasteners (in transit, on time). Recommend pulling Line 2 forward one shift.
"What’s stuck in customs right now, and how long has it been there?"
📦 2 shipments held: PO-4471 at Otay Mesa (19h, awaiting broker release), PO-4488 at Laredo (6h, inspection). Both feed lines scheduled within 48h. Auto-alert sent to planning.
"If the World Trade Bridge backs up 6 hours today, which lines are affected tomorrow?"
🔍 4 inbound loads cross that bridge in the next 24h. A 6h delay pushes Line 2 and Line 5 below safety stock by Thursday AM. Suggested: re-route 1 load via Pharr, expedite 1.
"How much did we spend on emergency expedites last month vs. forecast?"
📊 Emergency freight: $64K actual vs. $20K planned — 3.2× over. 78% tied to 5 cross-border shipments flagged late. All 5 would now trigger an early at-risk alert.
Zero
Border-Driven Line Stoppages
since go-live
~60%
Reduction in Emergency Freight
fewer panic expedites
Days
Earlier Warning on At-Risk Parts
was the morning-of
1 Source
Customs • Carrier • Bridge
joined to the schedule
Schedule a Demo
Industrial Manufacturing Multi-Plant MRO & Spare Parts

From Invisible Risk to
Proactive MRO Intelligence

A manufacturer tracked spare parts across three disconnected data sources—inventory, pricing, and consumption. Planners manually cross-referenced them to answer basic questions: strict stock levels and risk.

4 wk
To full deployment
Zero
Unplanned line stops post-deploy

Before

  • Data fragmented across three disconnected data sources (inventory, pricing, consumption)
  • Critical "at-risk" parts only identified after a line stopped
  • Unreliable valuations due to $0 cost errors in the inventory system
  • Hours spent manually cross-referencing sources to check stock levels

What changed

  • Data sources merge automatically every shift—zero manual work
  • Proactive alerts for critical parts falling below safe stock levels
  • Automatic price correction using last-purchase data for accurate valuations
  • Instant answers for floor managers via plain English queries

What the team actually asks it

"Which critical parts at our main plant will run out before the next delivery arrives?"
⚡ Found 4 at-risk critical parts — Bearing SKF-type (8 days remaining, 14-day lead time), Seal Kit A (3 days remaining, 12-day lead time)… Auto-reorder recommended for 3 of 4.
"What's our total critical parts inventory value across both plants this week?"
📊 Critical inventory: Plant 1: $512K, Plant 2: $335K. Total: $847K. 6 parts using price fallback (inventory showed $0). Non-critical: $241K.
"Show me parts with more than 180 days of stock — possible overstock?"
🔍 18 non-critical parts exceed 180-day runway. Top by value: Gasket Set ($18K, 340 days), Filter Kit ($12K, 290 days)… Recommend review for excess disposal.
"What percentage of critical parts are in stock vs. out of stock right now?"
✅ Critical in-stock: 94.2% | Out-of-stock: 5.8% (7 parts). Prior week: 91.3%. Trend improving. 3 of 7 have active POs already in transit.
94%+
Critical Parts In-Stock Rate
vs. reactive discovery only before
Zero
Unplanned Line Stoppages
due to parts shortage since go-live
3 Sources
Auto-Joined in Real Time
inventory • pricing • consumption
Seconds
To Answer Any Parts Query
was hours of manual work
Pharma Manufacturing Multi-CMO Supply Planning

Drug Substance Planning
That Answers “What If”

Four CMO sites, four separate spreadsheets. Because data was siloed, modeling a single demand change required days of manual recalculation across every product and site.

Days → Seconds
Scenario modeling time
4 CMOs
Unified DS/DP/FPP view

Before

  • Siloed data: each planner monitored their own site, blocking a unified view
  • Demand changes required days of manual recalculation
  • Blind batch additions caused overstock or shortages
  • Inventory projections were weeks stale by review time

What changed

  • Unified live view of inventory and batches across all CMOs
  • Instant scenario modeling for demand changes and impact analysis
  • Immediate answers to complex multi-year inventory questions
  • Smart caching provides instant answers to recurrring questions

Scenarios the planning team now runs in seconds

Scenario A — Demand Surge
"If sales increase 20% starting Q2, when does our primary product inventory drop below 3 months on hand at each site?"
⚡ MOH breaches 3.0 at Site A in Aug and Site B in Oct under this scenario. Recommend adding 2 DS batches at Site A in Q1 to buffer. Want me to model that addition?
Scenario B — Batch Addition
"Add 4 DP batches at our secondary CMO in 2027. Show ending inventory impact vs. baseline."
📊 Adding 4 DP batches at Site B increases ending inventory ~18% vs. baseline by Dec 2027. MOH improves from 4.2 → 5.1 months. Overstock risk low. Side-by-side chart ready.
Seconds
Scenario Modeling Time
was 2–3 days per scenario
4 CMOs
Unified in One View
DS • DP • FPP pipeline
Live
Batch & Inventory Tracking
across all products and sites
Real-Time
MOH & Ending Inventory
was always weeks stale
Process Manufacturing OEE / Uptime Maintenance Planning

Machine Uptime Tracking
That Runs Itself

Every shift, someone at this process manufacturer was manually comparing two sources — the maintenance master plan and the actual execution log — then calculating production impact by hand for each machine. By the time the numbers were ready, the shift was already over.

Auto
Uptime calc from maintenance codes
Per-Line
OEE visibility every shift

Before

  • Two separate data sources — a Master Plan and an Execution Log — maintained by different teams, reconciled manually each shift
  • Maintenance codes each carried different hour deductions. Engineers calculated production impact manually per machine, per shift
  • When execution deviated from the master plan, actual uptime was only known the following day after a supervisor reviewed both sources
  • No machine-level OEE tracking — production impact reported as a plant average, masking which lines had chronic downtime patterns

What changed

  • The execution log and master plan merge automatically each shift. When actual execution differs from what was planned, the actual always wins
  • Each maintenance code (weekly service, tool swap, monthly shutdown, major stoppage) has a defined hour deduction. Uptime % is calculated per machine, per shift, automatically
  • Shift supervisors see uptime per line in real time — not a plant-wide average. Machines with patterns of chronic downtime show up immediately instead of hiding in the aggregate
  • Questions like "Which lines had more than 2 hours of unplanned stoppages this week?" go from a 30-minute lookup to an instant answer

How the uptime engine works

Step 1
Ingest Both Sources
Execution log + Master plan loaded each shift automatically
Step 2
Reconcile
Execution overrides master plan. Standard shifts = 0 deduction. Heavy maintenance = hours deducted per code.
Step 3
Calculate Uptime %
Uptime = (24 − maintenance hours) ÷ 24. Per machine, per shift, per day.
Step 4
Surface on Dashboard
Machine-level OEE live for every shift supervisor. Alerts fire when lines drop below threshold.
Standard Shift
Production / No Impact
0 hrs deducted
Weekly Service
Scheduled Maintenance
−1.5 hrs
Monthly Shutdown
Planned Overhaul
−3.17 hrs
Major Stoppage
Bi-Monthly / Quarterly
−19.66 hrs
Real-Time
Uptime per Machine
was known only next-day
Auto
Plan vs. Execution Reconcile
zero manual cross-referencing
Line-Level
OEE Visibility
not just plant averages
Catalog
Driven Rules
update hours, not code

More Case Studies in Progress

Deployments completing now — detailed write-ups publishing Q2 2026

Coming Q4 2026

Automotive Tier 1 — JIT Orchestration

Coordinating just-in-time parts delivery with Tier 1/2 suppliers based on live production pace. Deployed across multiple plants.

Coming Q4 2026

CPG — Multi-Vendor Spend Intelligence

Unifying fragmented packaging vendor data, surfacing invisible spend, and automating reorder across 10+ suppliers.

Coming Q4 2026

Food & Bev — FSMA Compliance Autopilot

Automating FSMA 204 traceability, supplier cert management, and audit trail generation across multiple SKU lines.

Your plant could be next.

Every engagement is scoped around a specific workflow, with documented outcomes — numbers, before state, and final result. No vague claims.

Start a Pilot →