Scaling Products, the Right Way
We work with growth-stage startups to scale for millions of users, optimize throughput, enhance reliability, and ship faster—through modular platforms, lean pods, and scaling playbooks proven across 200+ startups.
We Engineer
Products That Scale –
From 1X to 100X
Scaling brings engineering challenges that early-stage practices can’t solve. At Talentica, we help growth-stage companies strengthen their engineering foundations—so they can ship faster, manage scale effectively, and reduce delivery friction. Our lean, high-performing pods ramp up within a week and are structured to maintain velocity as teams expand.
We bring proven practices like platform-driven development to reduce cycle time, parallel sprint lanes to increase throughput, and integrated tech debt management to ensure long-term velocity. Our focus is execution maturity—early identification of architectural risks, streamlined onboarding, and clean scaling across pods—so your engineering keeps pace with business growth, without compromising on quality or control.
what we offer
We help you scale your product right

Product Scale
Scaling from thousands to millions of users isn’t about more infra—it’s about smart engineering. We’ve done this repeatedly. Our engineers spot and resolve bottlenecks early, using evolving architecture patterns and modern tools to ensure long-term scalability.

Accelerated Feature Velocity
We deliver faster feature rollouts without compromising quality. Our engineers follow a product-first approach, backed by platform-led development and strong internal knowledge systems. Tech leads with multi-product experience ensure low-risk, high-speed execution.

SaaS Product Engineering
With 50+ SaaS products built, we embed self-service, licensing, tenancy, and configurability from day one. No hacks. No retrofitting. Just scalable SaaS architecture designed to flex with customer needs.

Team Augmentation
Every engineer counts. We build lean pods with low variance between top and bottom performers. With our scaling playbook, pod transitions are seamless, and new engineers are productive in a week. Polyglot skills ensure flexibility across stacks.

Tech Debt Management
We treat tech debt as part of your sprint—not something to push downstream. Our continuous improvement culture and re-engineering expertise allow us to modernize without breaking running systems.

Integrations & Connectors
We design integrations using tried-and-tested patterns that support varied formats, protocols, and security requirements. We account for non-functional needs—latency, scale, reliability—from day one.
Customers who grew with us










OUR WORK IN ACTION
Smart engineering, real outcomes

Scaled Adtech Platform to Handle 5Mn Requests/Sec
We worked with a mobile adtech leader to build real-time reporting, campaign pacing, and audience profiling system—handling 5M+ ad requests/sec.
Scaled Adtech Platform to Handle 5Mn Requests/Sec

Background
We worked with a leading mobile advertising and ad-optimization platform that creates and delivers advertisements to mobile devices.
The company needed a solution to generate real-time reporting for Key Business Metrics and Campaign Pacing Engine, insight report with 50+ dimensions to help publishers and advertisers with ad-requests traffic pattern and an Audience Management Platform (AMP) to profile users’ interests and facilitate user targeting.
Challenges
- Needed to handle the velocity of 5mn ad requests/sec.
- User segmentation should support storage and realtime-querying of about one Billion users (device Ids) across 30K+ user characteristics.
- Both Reporting and user-segmentation solutions should scale beyond 50Billion requestsand 1Billion users if the traffic grows further.
Solutions
- Developed a Lambda architecture with Amazon Cloud Platform with a processing layer,storage layer, and messaging system.
- Used Apache Storm based real-time Stream Processing Engine to crunch Key Business Metrics in real-time.
- Used EMR Hadoop/ Spark based batch processing engine to crunch 5-6TB data daily.
- Used MySQL DB for transactional data, Amazon Redshift Data warehouse for analyticaldata, Aerospike DB and Elastic Search for storing and querying user-profile and Amazon S3 for storing raw data.
- Implemented Apache Kafka to collect events data from over 200 ad-server instances.
Outcome
- Managed over 10mn ad requests/sec when traffic peaked, with 2bn+ active users segmented across 80k+ dimensions.
- Developed the solution within an opti mal infrastructure cost.

Detected Anomalies from 10Mn/Sec IoT Data in Real-Time
We worked with a wireless tech company to build a real-time anomaly detection system that processes 10M+ IoT events/sec.
Detected Anomalies from 10Mn/Sec IoT Data in Real-Time

Background
The client introduced enterprise-grade Wi-Fi, BLE and IoT together to deliver personalized, location-based wireless services without requiring battery-powered beacons. Its AI-driven wireless platform makes Wi-Fi predictable, reliable and measurable.
The company needed a solution to generate data for Analytics dashboard for WiFi users and real-time alerts for anomaly events. It also wanted to develop a Virtual Network Assistant to help troubleshoot anomaly events.
Challenges
- The velocity of IoT data was at the scale of 10mn/sec.
- The anomaly condition should be deduced and needed to be broadcasted in Real-time(within few seconds)
Solution
We designed the solution using following Big-Data tech stack
- Processing Layer
- 80+ Nodes Storm-Cluster to process data from over 80 topologies in Real-time to intercept anomaly conditions and crunch data for Analytics dashboard.
- Storage System
- 40+ Nodes of Cassandra cluster with capacity to hold 5+Terabytes data for Analytics dashboard and Anomaly alerts.
- 10+ Nodes of Elastic Search cluster to store detailed data (50+ dimensions) for Virtual Network assistant to facilitate troubleshooting.
- Messaging System
- 10+ Node Kafka-Cluster housing over 200+ Kafka topics, average data transfer of 100+MB/sec. Each message having from 50 to few hundred fields.
- Cluster Manager
- Apache Mesos on AWS using Docker.
Outcome
The solution we built has been robust and handles over 10mn IoT data/sec and the alerts go out within 30sec of any anomaly condition. The solution worked in various cloud environments – Amazon, Google & Azure.

Enabled Concurrency Control for Large Datasets
We helped a global supply chain platform modernize its system to support 10K rows and 600 columns for 100+ concurrent users.
Enabled Concurrency Control for Large Datasets

Background
We worked with a supply chain management software platform that has operations in 20 countries with 10K buyers and 200K suppliers on the platform. Expansion of the business made it clear that modernization is necessary to handle 10K rows and 600 columns concurrently for 100 users.
Challenge
- The existing solution was using an online excel to manage Bill of Material (BOM) from buyers and responses from sellers. But it could handle only 500 rows and 20 columns concurrently.
- The company had to process larger BOMs manually. It used to take 5-6 months to reward single BOMs, and they wanted to reduce it to 5-6 weeks.
Solution
- We built a frontend, like excel, with responsive backend APIs, which could handle a concurrency of 3K requests/second. It allowed 100 users with different roles to make changes/format the sheet simultaneously.
- To support 10K rows and 600 columns for every BOM, we migrated existing SQL to scalable NoSQL (MongoDB) and migrated .NET monolith to node.js based microservices.
- For awarding, we created an online excel supporting over 10 million with search, filtering and sorting options using our own database on Lucene search.
Outcome
Scaled a platform to handle more than 10K rows and 600+ columns for 100+ concurrent users.

Enabled Multi-User Collaboration for Project Management Software
We modernized a legacy project management system to enable multi-user editing and support 100K+ tasks per project.
Enabled Multi-User Collaboration for Project Management Software

Background
The company built project flow management software to improve the on-time delivery ofprojects. Their existing solution was using a thick client granting edit access to only a single user. It had a limitation of handling X tasks that impacted handling long-duration projects.
Challenges
- Allowing concurrent access to multiple users.
- Scaling the existing system to handle 100K tasks for a single project.
- Migrating monolith application to decoupled subsystem.
- Upgrading technology to ease development and improve skill availability.
Solution
Phase-wise implementation
- First phase – Built basic Gantt UI and exposed scheduling algorithms.
- Second phase- Added complex features and scaling to handle bigger projects.
Layer-wise breakdown
- Exposed API Layer for processing patented algorithms as a stateless webservice.
- Implemented a separate API layer to handle database/storage.
- Segregated the UI layer and user interactions.
Refactored into decoupled subsystems
- Improved the software maintainability and upgrades.
- Covered end-to-end business with unit, integration and regression test suits.
Outcome
- Enhanced end-user collaboration with notifications for conflicts, merge and resolve mechanism.
- Made integrations easier with other applications using web services.
- Built a powerful query engine to analyze project data and enable custom reports.

Analyzed Sentiments Across All YouTube Videos Every 12 Hours
We built a data processing platform for a media-tech company to fetch and analyze YouTube data every 12 hours.
Analyzed Sentiments Across All YouTube Videos Every 12 Hours

Background
Our customer was a media company that helped creators, brands, and traditional media firms reach the right viewers. In order to understand the user sentiments, they wanted to analyse entire data on YouTube every 12 hours.
Challenges
- Build a data processing platform that can efficiently acquire, ingest and process entire data from YouTube every 12 hours.
- Understand the mood and sentiment of the users on a daily basis based on comments, description.
- Recognize the audience demographics their likes and dislikes.
Solution
- Built a custom crawlers to pull data from various sources and collect data.
- Amazon S3 as a data lake where all the fetched data is stored.
- Snowflake is an analytic data warehouse provided as Software-as-a-Service (SaaS) for data processing. It pulls data from S3 processes and stored data in relational form in Snowflake as well as loads it to postgres where reporting
application connects
Outcome
Analysed the sentiments and mood of users across YouTube videos every 12 hours.

Handled Raw Data from 1000+ Sources & Data Management Software
A financial intelligence company wanted to scale integrations of third-party and private organizational data into search. We built a system that made it searchable, structured, and AI-ready.
Handled Raw Data from 1000+ Sources & Data Management Software

Background
The company has a financial search engine platform for business insights and market intelligence powered by AI and NLP technology. It empowers leading corporations and financial institutions with data-driven decisions and a competitive edge.
It came to us with its initial product version using publicly available data and a few reputable third-party sources. They wanted us to scale the integrations of industry- specific data from third parties and include private organizational data in search results.
Challenge
- Enabling search engines to use organization-specific private data stored on software like Evernote, OneDrive, and SharePoint to improve search results.
- Adding high-value curated content from more than 1000 third-party sources in search results for credible search results.
Solution
Implemented integration framework
- To handle unstructured private data from data management software like Evernote, OneDrive, and SharePoint.
- To manage bulk data spikes and refresh the data at set intervals using file sync.
Built data pipeline
- To add high-value curated content from more than 1000 industry-specific credible third-party sources in search results.
- To allow users access to paid content with various payment options like pay per report, per page, etc.
Made data AI-ready
- Implemented data annotations for private as well as third-party data to provide improved search results for organizations.
Outcome
- Accelerated customer acquisition as the inclusion of private and third-party data increased the efficiency of search engine.
- Helped in raising Series B round funding of $50M.
OUR WORK IN ACTION
Smart engineering, real outcomes

Scaled Adtech Platform to Handle 5Mn Requests/Sec
We worked with a mobile adtech leader to build real-time reporting, campaign pacing, and audience profiling system—handling 5M+ ad requests/sec.
Scaled Adtech Platform to Handle 5Mn Requests/Sec

Background
We worked with a leading mobile advertising and ad-optimization platform that creates and delivers advertisements to mobile devices.
The company needed a solution to generate real-time reporting for Key Business Metrics and Campaign Pacing Engine, insight report with 50+ dimensions to help publishers and advertisers with ad-requests traffic pattern and an Audience Management Platform (AMP) to profile users’ interests and facilitate user targeting.
Challenges
- Needed to handle the velocity of 5mn ad requests/sec.
- User segmentation should support storage and realtime-querying of about one Billion users (device Ids) across 30K+ user characteristics.
- Both Reporting and user-segmentation solutions should scale beyond 50Billion requestsand 1Billion users if the traffic grows further.
Solutions
- Developed a Lambda architecture with Amazon Cloud Platform with a processing layer,storage layer, and messaging system.
- Used Apache Storm based real-time Stream Processing Engine to crunch Key Business Metrics in real-time.
- Used EMR Hadoop/ Spark based batch processing engine to crunch 5-6TB data daily.
- Used MySQL DB for transactional data, Amazon Redshift Data warehouse for analyticaldata, Aerospike DB and Elastic Search for storing and querying user-profile and Amazon S3 for storing raw data.
- Implemented Apache Kafka to collect events data from over 200 ad-server instances.
Outcome
- Managed over 10mn ad requests/sec when traffic peaked, with 2bn+ active users segmented across 80k+ dimensions.
- Developed the solution within an opti mal infrastructure cost.

Detected Anomalies from 10Mn/Sec IoT Data in Real-Time
We worked with a wireless tech company to build a real-time anomaly detection system that processes 10M+ IoT events/sec.
Detected Anomalies from 10Mn/Sec IoT Data in Real-Time

Background
The client introduced enterprise-grade Wi-Fi, BLE and IoT together to deliver personalized, location-based wireless services without requiring battery-powered beacons. Its AI-driven wireless platform makes Wi-Fi predictable, reliable and measurable.
The company needed a solution to generate data for Analytics dashboard for WiFi users and real-time alerts for anomaly events. It also wanted to develop a Virtual Network Assistant to help troubleshoot anomaly events.
Challenges
- The velocity of IoT data was at the scale of 10mn/sec.
- The anomaly condition should be deduced and needed to be broadcasted in Real-time(within few seconds)
Solution
We designed the solution using following Big-Data tech stack
- Processing Layer
- 80+ Nodes Storm-Cluster to process data from over 80 topologies in Real-time to intercept anomaly conditions and crunch data for Analytics dashboard.
- Storage System
- 40+ Nodes of Cassandra cluster with capacity to hold 5+Terabytes data for Analytics dashboard and Anomaly alerts.
- 10+ Nodes of Elastic Search cluster to store detailed data (50+ dimensions) for Virtual Network assistant to facilitate troubleshooting.
- Messaging System
- 10+ Node Kafka-Cluster housing over 200+ Kafka topics, average data transfer of 100+MB/sec. Each message having from 50 to few hundred fields.
- Cluster Manager
- Apache Mesos on AWS using Docker.
Outcome
The solution we built has been robust and handles over 10mn IoT data/sec and the alerts go out within 30sec of any anomaly condition. The solution worked in various cloud environments – Amazon, Google & Azure.

Enabled Concurrency Control for Large Datasets
We helped a global supply chain platform modernize its system to support 10K rows and 600 columns for 100+ concurrent users.
Enabled Concurrency Control for Large Datasets

Background
We worked with a supply chain management software platform that has operations in 20 countries with 10K buyers and 200K suppliers on the platform. Expansion of the business made it clear that modernization is necessary to handle 10K rows and 600 columns concurrently for 100 users.
Challenge
- The existing solution was using an online excel to manage Bill of Material (BOM) from buyers and responses from sellers. But it could handle only 500 rows and 20 columns concurrently.
- The company had to process larger BOMs manually. It used to take 5-6 months to reward single BOMs, and they wanted to reduce it to 5-6 weeks.
Solution
- We built a frontend, like excel, with responsive backend APIs, which could handle a concurrency of 3K requests/second. It allowed 100 users with different roles to make changes/format the sheet simultaneously.
- To support 10K rows and 600 columns for every BOM, we migrated existing SQL to scalable NoSQL (MongoDB) and migrated .NET monolith to node.js based microservices.
- For awarding, we created an online excel supporting over 10 million with search, filtering and sorting options using our own database on Lucene search.
Outcome
Scaled a platform to handle more than 10K rows and 600+ columns for 100+ concurrent users.

Enabled Multi-User Collaboration for Project Management Software
We modernized a legacy project management system to enable multi-user editing and support 100K+ tasks per project.
Enabled Multi-User Collaboration for Project Management Software

Background
The company built project flow management software to improve the on-time delivery ofprojects. Their existing solution was using a thick client granting edit access to only a single user. It had a limitation of handling X tasks that impacted handling long-duration projects.
Challenges
- Allowing concurrent access to multiple users.
- Scaling the existing system to handle 100K tasks for a single project.
- Migrating monolith application to decoupled subsystem.
- Upgrading technology to ease development and improve skill availability.
Solution
Phase-wise implementation
- First phase – Built basic Gantt UI and exposed scheduling algorithms.
- Second phase- Added complex features and scaling to handle bigger projects.
Layer-wise breakdown
- Exposed API Layer for processing patented algorithms as a stateless webservice.
- Implemented a separate API layer to handle database/storage.
- Segregated the UI layer and user interactions.
Refactored into decoupled subsystems
- Improved the software maintainability and upgrades.
- Covered end-to-end business with unit, integration and regression test suits.
Outcome
- Enhanced end-user collaboration with notifications for conflicts, merge and resolve mechanism.
- Made integrations easier with other applications using web services.
- Built a powerful query engine to analyze project data and enable custom reports.

Analyzed Sentiments Across All YouTube Videos Every 12 Hours
We built a data processing platform for a media-tech company to fetch and analyze YouTube data every 12 hours.
Analyzed Sentiments Across All YouTube Videos Every 12 Hours

Background
Our customer was a media company that helped creators, brands, and traditional media firms reach the right viewers. In order to understand the user sentiments, they wanted to analyse entire data on YouTube every 12 hours.
Challenges
- Build a data processing platform that can efficiently acquire, ingest and process entire data from YouTube every 12 hours.
- Understand the mood and sentiment of the users on a daily basis based on comments, description.
- Recognize the audience demographics their likes and dislikes.
Solution
- Built a custom crawlers to pull data from various sources and collect data.
- Amazon S3 as a data lake where all the fetched data is stored.
- Snowflake is an analytic data warehouse provided as Software-as-a-Service (SaaS) for data processing. It pulls data from S3 processes and stored data in relational form in Snowflake as well as loads it to postgres where reporting
application connects
Outcome
Analysed the sentiments and mood of users across YouTube videos every 12 hours.

Handled Raw Data from 1000+ Sources & Data Management Software
A financial intelligence company wanted to scale integrations of third-party and private organizational data into search. We built a system that made it searchable, structured, and AI-ready.
Handled Raw Data from 1000+ Sources & Data Management Software

Background
The company has a financial search engine platform for business insights and market intelligence powered by AI and NLP technology. It empowers leading corporations and financial institutions with data-driven decisions and a competitive edge.
It came to us with its initial product version using publicly available data and a few reputable third-party sources. They wanted us to scale the integrations of industry- specific data from third parties and include private organizational data in search results.
Challenge
- Enabling search engines to use organization-specific private data stored on software like Evernote, OneDrive, and SharePoint to improve search results.
- Adding high-value curated content from more than 1000 third-party sources in search results for credible search results.
Solution
Implemented integration framework
- To handle unstructured private data from data management software like Evernote, OneDrive, and SharePoint.
- To manage bulk data spikes and refresh the data at set intervals using file sync.
Built data pipeline
- To add high-value curated content from more than 1000 industry-specific credible third-party sources in search results.
- To allow users access to paid content with various payment options like pay per report, per page, etc.
Made data AI-ready
- Implemented data annotations for private as well as third-party data to provide improved search results for organizations.
Outcome
- Accelerated customer acquisition as the inclusion of private and third-party data increased the efficiency of search engine.
- Helped in raising Series B round funding of $50M.
Our Partners



Customer Speak



“With Talentica, you get your engineering solution in one place. You can depend on them as you would depend on a family member. It allows you to be confident that all your engineering team needs will be met and grow in one space as opposed to trying to find them (solutions) with individual services or individual skill sets of people from the outside.”



“Be it solving critical problems or introducing new features, the team at Talentica made sure they bring bespoke innovation to the table every single time. When we approached them for a first-of-its-kind idea of embedding videos into emails, their approach towards it was brilliant, thereby driving some excellent results.”
news & events
Insights

Will Agentic AI Replace or Reinvent SaaS?
Alakh Sharma, Senior Data Scientist

Will Agentic AI Replace or Reinvent SaaS?
Ready to Scale Right?
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