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Vertical AGI — Reasoning Engine for Industrial Operations
LOGISTICSBlueDart Mumbai→Delhi · 5 shipments · 100% delayed · ₹28,600 at risk● LIVE
MANUFACTURINGM2-Lathe · 360 min downtime this week · 54% output attainment · escalating● LIVE
WAREHOUSESKU-105 Conveyor Belt · stock = 0 · lead time 21 days · stockout for 56 days● LIVE
DEVOPSpayment-service · 2 failed deploys in 6 days · P1 incident · MTTR 105 min● LIVE
MLOPSfraud-detector · accuracy 94% → 87% · data drift 0.34 · retraining overdue● LIVE
RETAILSKU-202 · stock = 0 · 28 units/week demand · ₹46,200 stockout loss this week● LIVE
SUPPLY CHAINImportComp China · Sensor-F6 · stock=2 · lead time 45d · 38% OTD · CRITICAL● LIVE
GST NOTICEASMT-10 · GSTIN 27ABCDE1234F1Z5 · Demand ₹8,40,000 · 4 issues · Reply due 7 days · Draft not started● LIVE

These pains are happening
in your operations right now.
OpsOracle AI finds them first.

Upload your logistics, warehouse, manufacturing, or DevOps report. AI reads every row, names the specific pain — carrier, machine, SKU, or service — and tells your team exactly what to do about it. In under 30 seconds.

14-day Pro trial · No credit card · Results in 30 seconds

< 30s
Analysis time
Named
Specific pain, not a score
₹→$
Financial impact quantified
Free
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Watch AI read real operations data

Pick an industry. Click run. See exactly what your team gets in 30 seconds.

Click "Run AI Analysis" above to see the output live — no account needed.

Real Pain → AI Solves It

Your team faces these every week.
OpsOracle names them and fixes them.

Actual AI output from real Operations data. Upload your report and get this analysis in under 30 seconds.

The Pain

BlueDart keeps missing Delhi deliveries. You find out 4 days late — after the customer calls.

Raw data signal

SH-1005 Mumbai→Delhi BlueDart PENDING +3d | SH-1007 PENDING +2d | SH-1009 PENDING +1d | Pattern: 5/5 delayed

OpsOracle AI Output

82% Risk — Act Today

BlueDart Mumbai→Delhi route: 100% delay rate across 5 shipments. ₹28,600 at risk. DTDC same corridor shows 0% delay.

[THIS WEEK] Action

Re-route SH-1005, SH-1007, SH-1009 to Delhivery before 5pm. Escalate SLA breach to BlueDart key account.

Expected impact: Prevent ₹28,600 cost-in-transit + 3 customer SLA breaches this week

The Pain

M2-Lathe broke down again. Third time this week. Production is bleeding 360 minutes.

Raw data signal

M2-Lathe Morning: 95min downtime | Afternoon: 110min | Night: 155min | Output: 415/900 planned (54%)

OpsOracle AI Output

88% Risk — Critical — Stop the Bleed

M2-Lathe downtime escalating shift-over-shift. Not random — mechanical degradation. ₹1,94,000 production value lost this week.

[THIS WEEK] Action

Maintenance engineer to inspect spindle bearings before next shift. Schedule 4-hour PM this weekend — do not wait.

Expected impact: Recover 485 lost units/week = ₹1,94,000 weekly production value

The Pain

Conveyor belt snapped. Production stopped 4 hours. The spare part? Out of stock.

Raw data signal

SKU-105 Conveyor Belt 2m: stock=0 | reorder_point=5 | lead_time=21d | last_restock=2026-04-15 (56 days ago)

OpsOracle AI Output

91% Risk — Stockout — Order Now

SKU-105 at zero stock for 56 days with 21-day lead time. You've been operating on luck. Every unplanned need = production stop.

[THIS WEEK] Action

Raise emergency PO today — request express delivery (2–3 days, accept 30% premium). Set reorder point to 8 units.

Expected impact: Prevent next ₹2,40,000 production stoppage

Analyze Your Operations Data Free →

14-day Pro trial · No credit card · Results in 30 seconds

How it works

From raw data to named fix in 30 seconds

01

Upload your report

CSV, Excel, or PDF from your ERP, WMS, or TMS. Any format — no template needed.

02

AGI reasons, names the pain

Not 'risk score 78'. It shows 5 reasoning steps: classifies the sub-vertical, identifies critical rows, detects patterns, calculates exposure, and names the fix — M2-Lathe, BlueDart Mumbai–Delhi, SKU-105.

03

Your team acts today

Three actions: This Week / This Month / Next Quarter. Each names an owner, a task, and the financial impact of fixing it.

AGI Reasoning Engine

Not a chatbot. A reasoning engine that shows its work.

Every analysis exposes the full chain of thought — which data it read, what pattern it found, why it's recommending this exact action.

Live reasoning chain — Manufacturing report⚡ Vertical AGI
Classified as manufacturing / discrete — CNC, lathe, press, shift keywords in 3+ columns. Sub-vertical: discrete manufacturing.
Isolated 3 critical rows: M2-Lathe Morning (95 min downtime), M2-Lathe Afternoon (110 min), M2-Lathe Night (155 min) — same machine, three consecutive shifts.
Pattern is escalating mechanical failure — not random. Downtime rising each shift: +15 min, then +45 min. Every other machine (M1, M3, M4, M5) shows zero breakdown pattern.
Financial exposure: 415 lost units × ₹468/unit standard cost = ₹1,94,220 at risk this week alone. Annualized at current trend: ₹1.01 Cr.
Root cause: escalating downtime on single machine across shifts matches spindle bearing wear lifecycle. Emergency maintenance before next shift prevents full breakdown and 72-hr repair stoppage.
→ Verdict:Maintenance engineer inspects spindle bearings before next shift. Schedule 4-hour PM this weekend — do not wait for full breakdown. Cost: ₹8,000 PM. Risk avoided: ₹1,94,220 this week.

5-step chain-of-thought

Every analysis shows the full reasoning — not just the answer

Evidence-first

Each finding cites the specific rows that drove the conclusion

Honest uncertainty

When data is sparse, the AI says so — no fake confidence

Stop finding out about
problems after they cost you.

Upload your first report now. The AGI reasoning engine reads every row, names the specific pain, shows exactly how it reached that conclusion, and gives your team a Monday Morning Action Plan — in 30 seconds.

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