Before & After. By Industry.
See exactly
See exactly
what changes
when AI goes live.
Pick your industry. See the before. See the after. Then decide.
Before AI
⏱ QC inspector checks at roll-end — 120m wasted
Night shift runs with zero quality monitoring
Rejection 7–9% — buyer claims every quarter
Every answer needs a phone call
After NeuAI
Defect flagged at exact metre in real-time
Every shift AI-supervised, every anomaly logged
Rejection drops to 2–4%
Live data on WhatsApp in 2 seconds
Showing: Before
Industry: Textile Manufacturing
Textile Manufacturer
FabricGuard — Live
01
Catching defects after they've already become losses.
Before
⏱ QC at roll-end — defect already 120m in
Night shift: zero quality monitoring
Rejection 7–9%. Buyer claims every quarter.
No root cause visibility by loom
After FabricGuard
Defect flagged at exact metre — real-time
Every shift AI-supervised, every alert logged
Rejection drops to 2–4%
Auto QC certificates — buyer-ready
−62%
Rejection
120m/min
Speed
<50ms
Latency
97.4%
Confidence
Aquaculture Farmer
Matsya Saathi — Pilot
02
By the time you see it in the pond, the harvest is already at risk.
Before
Pond checked twice daily — blind in between
DO crash at 3 AM — fish dying by 6 AM
Disease: call agent, wait 2 days, too late
Feed by habit — FCR high, cost unchecked
After Matsya Saathi
DO/pH/temp monitored every 15 min
Crash predicted 6–10hrs early via WhatsApp
Disease risk scored daily before symptoms
Feed by pond health — FCR drops 12–15%
7–10d
Early Warning
−15%
Feed Cost
24/7
Monitoring
5
Languages
Indian SMB
ManasBot — Pilot
03
You're the ERP. Everything runs through you. That doesn't scale.
Before
Call manager for stock — wait 20 min
Supplier follow-ups forgotten every day
Floor staff can't query Tally — you translate
Sunday report compiled — decisions Monday
After ManasBot
WhatsApp query → live Tally data in 2 sec
Supplier chases automated and tracked
Any language — any staff — zero calls to you
Daily brief at 8AM — exceptions highlighted
<2s
Response
5+
Languages
0
App Installs
24/7
Always On
Semiconductor Distributor
StockSense AI — Pilot
04
Dead stock doesn't announce itself — or explain why. By the time you notice, it's worthless.
Before
Inventory reviewed monthly — EOL already hit
Velocity lag — always 45 days behind
No visibility into why a part is dying
Panic liquidation — whatever price you get
After StockSense AI
60-day risk signal before velocity drops
Root-cause tag per SKU — EOL, design-out, demand drop
Buyer-match suggestions surfaced automatically
Liquidation price-band — margin floor protected
60d
Early Signal
₹1.2Cr
Modelled Saving
SKU-level
Granularity
Farmer / FPO
Krishi Sahitya — Pilot
05
A corporate agri-business has analysts. You have a middleman and hope.
Before
Mandi price from middleman — his number
Pest spotted: call agent, wait 2 days, crop hit
PM-Kisan: know the name, not the process
Generic advisory — not your soil or season
After Krishi Sahitya
Live APMC mandi price — your crop, today
Photo → pest ID in 30 sec in your language
Scheme eligibility — step by step, your language
Advisory specific to your crop, soil, region
30s
Pest ID
10+
Languages
Live
Mandi Prices
0
Middlemen
Which one is your problem?
Tell us your industry. We'll walk you through exactly what changes — and what it takes to get there.
