Case Studies
Proof in Production.
We believe the most persuasive thing we can share is what actually happened — with real numbers, real challenges, and real engineering decisions. These are a selection of the engagements we're most proud of.
BFSI · AI Engineering · Fraud Detection
Real-Time ML Fraud Detection for a Tier-1 Indian Bank
The Challenge
A leading private sector bank was losing ₹18 crore monthly to payment fraud. Their legacy rules-based system generated 40% false positives, creating customer friction and overwhelming their operations team with manual reviews. Any replacement had to handle 50,000 transactions per second with sub-50ms latency — and be migrated to without downtime.
A leading private sector bank was losing ₹18 crore monthly to payment fraud. Their legacy rules-based system generated 40% false positives, creating customer friction and overwhelming their operations team with manual reviews. Any replacement had to handle 50,000 transactions per second with sub-50ms latency — and be migrated to without downtime.
Our Approach
We designed and deployed a three-tier ML ensemble combining gradient boosting for known fraud patterns, a graph neural network for relationship-based detection, and a real-time anomaly detector for novel attack vectors. The system was deployed behind a shadow mode validation framework before going live, eliminating migration risk entirely.
We designed and deployed a three-tier ML ensemble combining gradient boosting for known fraud patterns, a graph neural network for relationship-based detection, and a real-time anomaly detector for novel attack vectors. The system was deployed behind a shadow mode validation framework before going live, eliminating migration risk entirely.
92%
Fraud reduction
38ms
P99 latency
73%
False positive drop
₹18Cr
Monthly loss recovered
Healthcare · Data Platform · Life Sciences
Clinical Intelligence Platform for a Global Pharma Group
The Challenge
A top-10 global pharmaceutical company had 14 separate clinical data systems — each with different schemas, access controls, and data quality standards. Clinical trial matching took 8–10 weeks manually. Researchers were spending 60% of their time on data preparation rather than research.
A top-10 global pharmaceutical company had 14 separate clinical data systems — each with different schemas, access controls, and data quality standards. Clinical trial matching took 8–10 weeks manually. Researchers were spending 60% of their time on data preparation rather than research.
Our Approach
We built a FHIR R4-compliant clinical data lakehouse, unifying all 14 source systems with a metadata-driven ingestion framework. An AI-assisted trial matching engine was layered on top, using NLP to extract patient eligibility criteria from unstructured clinical notes and match against trial protocols in real time.
We built a FHIR R4-compliant clinical data lakehouse, unifying all 14 source systems with a metadata-driven ingestion framework. An AI-assisted trial matching engine was layered on top, using NLP to extract patient eligibility criteria from unstructured clinical notes and match against trial protocols in real time.
3×
Trial match speed
67%
Faster time-to-insight
14
Systems unified
$2.4M
Annual research savings
Manufacturing · ML Engineering · IoT
Predictive Maintenance at Scale for a Global Engineering OEM
The Challenge
A Tier-1 automotive components manufacturer was losing $4.2M annually to unplanned CNC machine downtime across 3 factories. Reactive maintenance was creating production bottlenecks, quality escapes, and missed OEM delivery commitments. Their existing IoT infrastructure was generating petabytes of sensor data with no analytical layer to extract signal from it.
A Tier-1 automotive components manufacturer was losing $4.2M annually to unplanned CNC machine downtime across 3 factories. Reactive maintenance was creating production bottlenecks, quality escapes, and missed OEM delivery commitments. Their existing IoT infrastructure was generating petabytes of sensor data with no analytical layer to extract signal from it.
Our Approach
We deployed an edge-to-cloud ML architecture: lightweight inference models running on Jetson edge nodes for real-time vibration and thermal signature analysis, feeding into a cloud-based fleet intelligence platform. Models were trained on 36 months of historical failure data and validated against actual failure modes per machine family.
We deployed an edge-to-cloud ML architecture: lightweight inference models running on Jetson edge nodes for real-time vibration and thermal signature analysis, feeding into a cloud-based fleet intelligence platform. Models were trained on 36 months of historical failure data and validated against actual failure modes per machine family.
$4.2M
Downtime saved/yr
89%
Model accuracy
72hr
Advance warning
800
Machines monitored
Government · GovTech · Digital Services
Unified Citizen Services Platform for a State Government
The Challenge
Citizens in one of India's largest states were navigating 47 separate department portals to access government services — each with different authentication, UI patterns, and service standards. Digital adoption was at 12%. The existing infrastructure was unable to handle peak load events such as application windows for welfare schemes.
Citizens in one of India's largest states were navigating 47 separate department portals to access government services — each with different authentication, UI patterns, and service standards. Digital adoption was at 12%. The existing infrastructure was unable to handle peak load events such as application windows for welfare schemes.
Our Approach
We designed and delivered a unified GovCloud platform on a sovereign cloud infrastructure — consolidating all 47 services under a single authenticated citizen identity. An AI-powered virtual assistant handled 60% of citizen queries without human escalation. The platform was built for 10 million concurrent users with a 99.99% availability SLA.
We designed and delivered a unified GovCloud platform on a sovereign cloud infrastructure — consolidating all 47 services under a single authenticated citizen identity. An AI-powered virtual assistant handled 60% of citizen queries without human escalation. The platform was built for 10 million concurrent users with a 99.99% availability SLA.
12→68%
Digital adoption
47→1
Portals unified
60%
AI query resolution
99.99%
Uptime SLA
Retail · AI Engineering · Personalisation
AI Personalisation Engine for a D2C Fashion Retailer
The Challenge
A fast-growing D2C fashion brand with 8 million active customers was seeing 2.1% conversion rates — well below industry benchmarks. Their product recommendation system was rule-based, showing the same "popular items" logic to every user segment. Cart abandonment was 74%.
A fast-growing D2C fashion brand with 8 million active customers was seeing 2.1% conversion rates — well below industry benchmarks. Their product recommendation system was rule-based, showing the same "popular items" logic to every user segment. Cart abandonment was 74%.
Our Approach
We built a multi-armed bandit recommendation system incorporating real-time behavioural signals, visual similarity embeddings for catalogue matching, and contextual bandits for dynamic pricing and promotional targeting. The system was A/B tested across 500,000 users before full rollout.
We built a multi-armed bandit recommendation system incorporating real-time behavioural signals, visual similarity embeddings for catalogue matching, and contextual bandits for dynamic pricing and promotional targeting. The system was A/B tested across 500,000 users before full rollout.
+41%
Conversion uplift
-28%
Cart abandonment
+19%
Avg order value
$3.1M
Added annual revenue
Telecom · Data Engineering · Churn Analytics
Churn Intelligence Platform for a Regional Telecom Operator
The Challenge
A regional telecom operator with 22 million subscribers was experiencing 4.2% monthly churn — double the industry average. Their retention team was working from 30-day-old batch reports, making proactive intervention impossible. High-value customer identification was based on revenue alone, missing behavioural precursors to churn.
A regional telecom operator with 22 million subscribers was experiencing 4.2% monthly churn — double the industry average. Their retention team was working from 30-day-old batch reports, making proactive intervention impossible. High-value customer identification was based on revenue alone, missing behavioural precursors to churn.
Our Approach
We built a real-time churn intelligence platform on Apache Kafka and Flink, processing 2 billion daily events into a subscriber health score updated every 4 hours. An XGBoost churn propensity model — trained on 18 months of behavioural, network quality, and service interaction data — feeds a next-best-action engine for the retention team.
We built a real-time churn intelligence platform on Apache Kafka and Flink, processing 2 billion daily events into a subscriber health score updated every 4 hours. An XGBoost churn propensity model — trained on 18 months of behavioural, network quality, and service interaction data — feeds a next-best-action engine for the retention team.
-38%
Churn rate reduction
4hr
Insight latency
$8.4M
Revenue retained
2B
Daily events processed
Client Testimonials
What Our Clients Say
We measure our success by the words of the people we work with — not the awards on our wall.
"Being Systems didn't just deliver a fraud detection system — they fundamentally changed how we think about AI in our risk operations. Their team understood our regulatory constraints from day one, embedded themselves in our product and risk squads, and delivered something that actually moved the needle on losses. This is what a genuine engineering partner looks like."
"The clinical data platform Being Systems built for us became the foundation for our entire AI drug discovery programme. What impressed me most was their ability to navigate the complexity of 14 legacy systems while keeping the clinical team's workflow central to every design decision. The quality of their data engineering is genuinely best-in-class."
"We'd worked with several large SIs on our modernisation programme before engaging Being Systems. The difference was immediate and stark. They came with opinions, challenged our assumptions, and brought a level of technical depth that we hadn't experienced before. Three months in, we expanded the engagement to cover our entire data and AI stack."
"Building a citizen services platform that serves tens of millions of people requires more than technical skill — it requires the ability to work within government constraints while never losing sight of the citizen experience. Being Systems demonstrated both, and their security architecture was approved by our CERT team faster than any vendor we've worked with."
"The personalisation engine Being Systems built for us paid back its full cost in under 90 days. But what I value more than the ROI is the way they worked — transparent about trade-offs, honest about timelines, and obsessively focused on the metric we agreed mattered. They feel less like a vendor and more like the engineering team I always wished we had."
"We evaluated five firms for our cloud transformation. Being Systems won not because they were cheapest — they weren't — but because they were the only team that could demonstrate they'd actually solved this problem before, in a regulated environment, at our scale. Eighteen months in, we are on track and on budget. That almost never happens."