Outcomes

Ten ways AI showed up on the balance sheet.

Every engagement below is priced on the outcome — savings, revenue, or SLA. Numbers are anonymised composites from live deployments.

Pharma Retail

Medicine Plus

Saved $1.6M / year

The problem: 400+ telecallers handling refill reminders, insurance verifications and order tracking across 180 stores.
What we built: Voice-AI agent stack in Hindi, Tamil and English, integrated with their POS and inventory.
The outcome: Replaced 78% of repetitive calling work, redeployed staff into clinical advisory roles.

Healthcare

$2.4M revenue unlocked

The problem: Radiology backlog of 6,000 scans/month; specialist reports delayed by 48 hours.
What we built: AI triage co-pilot for radiologists with auto-drafted reports, reviewed and signed by doctors.
The outcome: Turnaround dropped from 48h to 4h; throughput up 3x without new hires.

Manufacturing

$3.2M operational savings

The problem: Order processing across distributors took 3 days, with manual reconciliation slowing dispatch and inflating working capital.
What we built: AI order-intake and inventory orchestration agent integrated with their ERP and distributor channels.
The outcome: Order cycle compressed from 3 days to 2 minutes; AOV up 59% and inventory turnover up 42%.

Finance & Wealth

2x advisor productivity

The problem: Wealth advisors spending 60% of time on portfolio reporting and compliance notes.
What we built: Custom LLM trained on SEBI guidelines, generating client-ready portfolio briefs.
The outcome: Advisor capacity doubled; NPS jumped from 42 to 71 in a single quarter.

FMCG

$7.2M working capital freed

The problem: Demand planning across 14 SKUs and 9 states leading to 22% stock-outs and 8% wastage.
What we built: Forecasting + dynamic replenishment agent fed by distributor and retail-audit data.
The outcome: Stock-outs down to 6%, wastage to 2.1%, working capital released across DCs.

Pharma R&D

60% faster discovery

The problem: Manual literature review for new molecules consuming 9 months per program.
What we built: Research-AI workspace surfacing patents, trials and adverse-event data on demand.
The outcome: Discovery cycle compressed from 9 months to 11 weeks for 3 active programs.

Logistics

Saved $4.1M / year

The problem: 12,000 driver onboarding calls/month, plus dispute resolution and POD chasing.
What we built: Multilingual voice + WhatsApp agent for onboarding, POD collection and complaints.
The outcome: Ops team shrunk by 35% while monthly trips grew 28%.

IT SaaS

74% ticket deflection

The problem: Support and onboarding load scaled faster than headcount, with repetitive tickets eating into product engineering time.
What we built: AI support copilot + onboarding agent integrated with their SaaS platform, handling tier-1 queries and guided activation.
The outcome: Tier-1 ticket deflection at 74%; activation rate up 38% and engineering hours reclaimed for product work.

Education

3.2x ROI in year one

The problem: Mass-personalised tutoring across 4.2 lakh learners — humanly impossible.
What we built: Adaptive tutor + assessment agent in 7 Indian languages.
The outcome: Learning outcomes up 34%; cost per learner down 61%.

Agritech

17x reach at 1/4th cost

The problem: Crop-advisory call centre couldn't scale beyond 40,000 farmers.
What we built: Voice-AI agronomist on basic phones, trained on regional crop data.
The outcome: Served 6.8L farmers in 8 months; yields up 18% in pilot districts.