CASE STUDY

SensorsCall’s Camera-Free Senior-Care Platform, Engineered By thinkbridge

thinkbridge helped SensorsCall build a privacy-first, AI-powered senior care platform—detecting falls, anomalies, and critical sounds with 0 cameras, 2% false positives, and real-time insights across thousands of homes.
25%
More Continuous Coverage
30%
False-Alarm Reduction
100%
Homes Keep Existing Detectors

The Challenge

  • Zero-camera mandate: achieve accurate fall and wellness detection with only ambient sensors—even in bathrooms, where most incidents occur
  • One size doesn’t fit all: seniors follow very different daily rhythms; a fixed threshold model would either miss events or over-alert
  • Make the old new again: leverage existing smoke alarms and other household sounds to create actionable, app-based alerts
  • HIPAA-grade security & scale: process millions of daily events while protecting sensitive health signals

The Solution

  • Cloud-Native IoT Backbone: Serverless ingestion and Kafka-based streams ensure <1 s end-to-end latency across thousands of homes.
  • Behaviour-Adaptive Anomaly Detection: Unsupervised ML models (Isolation Forest + LSTM auto-encoders) train on each resident’s first two days of data, then refresh nightly to stay aligned with evolving routines. This self-learning loop keeps false positives under 2% while catching subtle deviations such as prolonged bathroom stays or skipped meals.
  • Sound Classification Suite: A lightweight CNN, distilled from AudioSet, identifies ANSI-standard smoke-alarm patterns, CO detectors, glass breakage, and even specific voice cues. Retrofit kits simply clip a mic near existing alarms—no rewiring needed.  
  • Explainable Insight Layer: Rule-engine overlays translate ML outputs into plain English insights (e.g., “Mom has spent 35 % longer than usual in the restroom this morning”) inside caregiver apps.
  • Privacy-First Architecture: All sensor and audio features are encrypted in transit & at rest, with on-device pre-processing so raw sound never leaves the home.
Triangle svg

Result

  • Monitoring reliability: +25% uptime. More continuous coverage—even during ISP blips
  • False-alarm reduction: -30% alerts. Personalized models suppress noise, letting caregivers focus on true risks.
  • Anomaly detection accuracy: <2% false positives Self-tuning thresholds maintain precision as routines change
  • Smoke-alarm retrofit coverage: 100% of homes keep existing detectors. No costly hardware swap; alerts now arrive on mobile in <3 s
  • Insight adoption: 90% of caregivers engage weekly. Plain-language explanations build trust and drive action

Conclusion

By fusing edge sensors with a self-learning AI engine and sound-classification toolkit, thinkbridge transformed SensorsCall from a data logger into an intelligent, proactive care companion. The camera-free solution protects dignity, preserves privacy, and scales effortlessly, proving that thoughtful technology can keep families connected—and seniors safer—without intruding on everyday life.

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SensorsCall’s Camera-Free Senior-Care Platform, Engineered By thinkbridge

thinkbridge helped SensorsCall build a privacy-first, AI-powered senior care platform—detecting falls, anomalies, and critical sounds with 0 cameras, 2% false positives, and real-time insights across thousands of homes.

Read More
25%
More Continuous Coverage
30%
False-Alarm Reduction
100%
Homes Keep Existing Detectors

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Read More
40%
Reduction In Average Quote Time
60%
Decrease In Onboarding Time
100%
Elimination Of Direct Database Access

How to have a Tech-Forward Business

That will actually increase your bottom line
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How to have a Tech-Forward Business

That will actually increase your bottom line
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CASE STUDY

SensorsCall’s Camera-Free Senior-Care Platform, Engineered By thinkbridge

thinkbridge helped SensorsCall build a privacy-first, AI-powered senior care platform—detecting falls, anomalies, and critical sounds with 0 cameras, 2% false positives, and real-time insights across thousands of homes.

25%
More Continuous Coverage
30%
False-Alarm Reduction
100%
Homes Keep Existing Detectors

The Challenge

  • Zero-camera mandate: achieve accurate fall and wellness detection with only ambient sensors—even in bathrooms, where most incidents occur
  • One size doesn’t fit all: seniors follow very different daily rhythms; a fixed threshold model would either miss events or over-alert
  • Make the old new again: leverage existing smoke alarms and other household sounds to create actionable, app-based alerts
  • HIPAA-grade security & scale: process millions of daily events while protecting sensitive health signals

MEET THE TEAM

img

Anand Krishnan

Managing Partner & CEO

sai-img

Sai Ganesh

Managing Partner & Chief Scientist

img
Shamik Mitra

Managing Partner & Chief Delivery Officer

Andy K-img
Andy Komandur

Vice President-Growth
APAC & GCC

img
Andrew Zarkadas

Vice President - Growth Americas

MEET THE TEAM

img

Anand Krishnan

Managing Partner & CEO

sai-img

Sai Ganesh

Managing Partner & Chief Scientist

img

Shamik Mitra

Managing Partner & Chief Delivery Officer

Andy K-img

Andy Komandur

Vice President-Growth
APAC & GCC

img
Andrew Zarkadas

Vice President - Growth Americas

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The Solution

  • Cloud-Native IoT Backbone: Serverless ingestion and Kafka-based streams ensure <1 s end-to-end latency across thousands of homes.
  • Behaviour-Adaptive Anomaly Detection: Unsupervised ML models (Isolation Forest + LSTM auto-encoders) train on each resident’s first two days of data, then refresh nightly to stay aligned with evolving routines. This self-learning loop keeps false positives under 2% while catching subtle deviations such as prolonged bathroom stays or skipped meals.
  • Sound Classification Suite: A lightweight CNN, distilled from AudioSet, identifies ANSI-standard smoke-alarm patterns, CO detectors, glass breakage, and even specific voice cues. Retrofit kits simply clip a mic near existing alarms—no rewiring needed.  
  • Explainable Insight Layer: Rule-engine overlays translate ML outputs into plain English insights (e.g., “Mom has spent 35 % longer than usual in the restroom this morning”) inside caregiver apps.
  • Privacy-First Architecture: All sensor and audio features are encrypted in transit & at rest, with on-device pre-processing so raw sound never leaves the home.

Result

Result

  • Monitoring reliability: +25% uptime. More continuous coverage—even during ISP blips
  • False-alarm reduction: -30% alerts. Personalized models suppress noise, letting caregivers focus on true risks.
  • Anomaly detection accuracy: <2% false positives Self-tuning thresholds maintain precision as routines change
  • Smoke-alarm retrofit coverage: 100% of homes keep existing detectors. No costly hardware swap; alerts now arrive on mobile in <3 s
  • Insight adoption: 90% of caregivers engage weekly. Plain-language explanations build trust and drive action

Case Studies

Browse our case studies ​

Transforming A Recruitment Process Outsourcing Firm With A Tech-First Platform

RPO firm transforms with thinkbridge, achieving 1.6x EBITDA, 6x valuation growth, and 2x revenue—without adding headcount.

Read More
6x
Valuation Multiple Improvemen
1.6x
Improvement In EBITDA
2x Revenue
Without Adding Headcount

Transforming A Leading Non-Emergency Medical Transportation Provider Into A Scalable, AI-Driven Operation

Dash Solutions transformed into a fintech leader—serving 10x more clients, launching products 8x faster, and tripling growth—thanks to a scalable, API-first platform built with thinkbridge.

Read More
20%
Improvement Of On-time Trips
25%
Drop In Member Grievenaces
30%
Cut In Call Wait Times

Client Success Story: Dash Solutions Leverages Technology to Evolve Into A Fintech Powerhouse

Dash Solutions used thinkbridge to 10x client capacity, 3x growth, and launch products 8x faster—transforming into a true fintech powerhouse.

Read More
3x
Growth Without Increasing Headcount
10x
Increase In Client Capacity
8x
Faster Product Launches

Consero Transforms Into A Finance-As-A-Service Firm With A Tech-First Approach Through The SIMPL Platform

thinkbridge helped Consero launch the SIMPL platform—driving 40% revenue growth, 30% EBITDA gain, and scaling FaaS operations without added overhead.

Read More
40%
Revenue Increase Without Overhead Growth
30%
EBITDA Improvement
2x
Accounts Overseen

Rebuilt To Scale, Delivered On Schedule: How thinkbridge Transformed Timerack’s Platform Into A Growth Engine

thinkbridge rebuilt Timerack’s legacy platform into a scalable, AI-ready solution—launched in 4 months, boosting leads, retention, and performance.

Read More
40%
Drop In Support Tickets
27%
Increase In Qualified Leads Post-Event
30–50%
Reduction In Future Development Cycles

From Zero To Launch In 60 Days: How thinkbridge Helped A Payment Platform Ship A High Impact Mobile Payments Platform In Record Time

Delivered a full-featured mobile payments app for a payment platform in just 8 weeks—50% faster than planned—using Flutter, internal accelerators, and agile delivery to meet tight budget, time, and quality targets.

Read More
50%
Faster Than The Planned
100%
Core Functionality Shipped
Maintained Budget Discipline

How thinkbridge Transformed A Leading Optical Retailers Operations Across Asia with Strategy-Led Digital Execution

thinkbridge helped the company move beyond spreadsheets to scalable, real-time retail operations—unlocking the full ROI of their ERP investment with Power BI, Power Apps, and a structured IT delivery model, all within six months.

Read More
48–72 hours
Approval Turnaround Time
3x
IT Enhancement Delivery Ad-hoc
70%
Less Excel Dependency

How A Next-Gen FinTech Platform And thinkbridge Built A Full-Featured Neo-Banking App In 90 Days

In just 90 days, thinkbridge helped a next-gen fintech platform launch a full-featured neo-banking app for underserved Asia-Pacific regions—cutting development time by 50%, slashing costs, and setting the foundation for long-term fintech growth.

Read More
45–50%
Savings
60%
Reduced Future Maintenance Burden
67%
Faster Than Industry Avg.

Orchestrating End-To-End Digital Transformation For A Retail Apparel Giant

thinkbridge partnered with a major apparel brand to cut manual data entry by 75% and reduce cycle times by 35%, delivering a clear, AI-ready transformation roadmap in just 6 weeks.

Read More
75%
Manual Data Entry Eliminated
35%
Reduction In Cycle Time
18%
Accuracy Improvement

A Digital Transformation Blueprint For A Global Pearl & Jewellery Powerhouse

thinkbridge helped a global luxury retailer cut manual work by 60% with a digital blueprint mapping 100+ processes and 35+ AI opportunities.

Read More
40–60%
Reduction In Manual Work
20–25%
Improvement In Inventory
100%
Executive Alignment Achieved

SensorsCall’s Camera-Free Senior-Care Platform, Engineered By thinkbridge

thinkbridge helped SensorsCall build a privacy-first, AI-powered senior care platform—detecting falls, anomalies, and critical sounds with 0 cameras, 2% false positives, and real-time insights across thousands of homes.

Read More
25%
More Continuous Coverage
30%
False-Alarm Reduction
100%
Homes Keep Existing Detectors

From Manual to Magical: How thinkbridge Helped A Field Services SaaS Platform Secure Its IP and Revolutionize Technician Quoting With AI Services

thinkbridge built a secure, AI-powered pricing assistant for a field services SaaS platform—cutting quote time by 40%, protecting pricing IP, and boosting technician satisfaction by 27%.

Read More
40%
Reduction In Average Quote Time
60%
Decrease In Onboarding Time
100%
Elimination Of Direct Database Access

How to have a Tech-Forward Business

That will actually increase your bottom line

The Solution

  • Cloud-Native IoT Backbone: Serverless ingestion and Kafka-based streams ensure <1 s end-to-end latency across thousands of homes.
  • Behaviour-Adaptive Anomaly Detection: Unsupervised ML models (Isolation Forest + LSTM auto-encoders) train on each resident’s first two days of data, then refresh nightly to stay aligned with evolving routines. This self-learning loop keeps false positives under 2% while catching subtle deviations such as prolonged bathroom stays or skipped meals.
  • Sound Classification Suite: A lightweight CNN, distilled from AudioSet, identifies ANSI-standard smoke-alarm patterns, CO detectors, glass breakage, and even specific voice cues. Retrofit kits simply clip a mic near existing alarms—no rewiring needed.  
  • Explainable Insight Layer: Rule-engine overlays translate ML outputs into plain English insights (e.g., “Mom has spent 35 % longer than usual in the restroom this morning”) inside caregiver apps.
  • Privacy-First Architecture: All sensor and audio features are encrypted in transit & at rest, with on-device pre-processing so raw sound never leaves the home.

Result:

25%
More Continuous Coverage
30%
False-Alarm Reduction
100%
Homes Keep Existing Detectors

Result

  • Monitoring reliability: +25% uptime. More continuous coverage—even during ISP blips
  • False-alarm reduction: -30% alerts. Personalized models suppress noise, letting caregivers focus on true risks.
  • Anomaly detection accuracy: <2% false positives Self-tuning thresholds maintain precision as routines change
  • Smoke-alarm retrofit coverage: 100% of homes keep existing detectors. No costly hardware swap; alerts now arrive on mobile in <3 s
  • Insight adoption: 90% of caregivers engage weekly. Plain-language explanations build trust and drive action

How to have a Tech-Forward Business

That will actually increase your bottom line

How to have a Tech-Forward Business

That will actually increase your bottom line