Talentica Software

Achieving Product-Market-Fit the Lean Way

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Head Quarter: Palo Alto, CA

Industry: Digital Marketing

Amplify.ai is an AI-powered platform with expertise in increasing customer engagement via messengers on Facebook and Instagram. Also, deploys feature-rich bots for actionable outcomes that uses true-to-life and persistent conversations.

Our Challenges:

1. To turn the idea into MVP quickly.
2. Iterating the initial product to find the best product-market-fit.

What We Did:

1. Built a messenger bot to allow page-owners to send updates to followers and engage more customers on social media channels.
2. Built a solution using ML to identify the right triggers on the basis of customer behavior and designed notifications based on it.
3. Developed practical and user-friendly interface for moderating content on social media using NLP.

What We Secured:

1. Achieved product-market-fit.
2. After the success of achieving product-market-fit, client could raise seed funding.
3. Product’s ability to adapt quickly resulted in exiting through successful acquisition.

Building a New Product with an Extended Team

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Head Quarter: Bengaluru

Industry: FinTech

Rupeek is an asset backed digital lending platform that operates using a branchless model. It also ensures fair, fast, and flexible methods.

Our Challenges:

The client was struggling to scale engineering team for managing growing business demands. We took the challenge to quickly ramp up the engineering team and built a new product, Rupeek Quick, to help the client meet customer needs and increase market share.

What We Did:

1. Set up a united team that started working with client’s team.
2. Built the initial product with features like loan rate calculation, partial release of asset, and payment gateway integration.
3. Based on customer feedback, we worked on another set of features like Rupeek quick cards, cashback facility, and workflow frame upgradation.
4. Integrated the product with Loan Management System (LMS) for seamless onboarding.

What We Secured:

1. On-time delivery in just 5 months from teaming up to launch.
2. Innovative, unique, and client-oriented features helped in expanding the market footprint.
3. Better features and market uptake attracted lenders like NBFC partners to invest.

End to End Development of Feature-Rich Mobile Application

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Head Quarter: New Delhi, India

Industry: Food delivery partner (F&B)

LimeTray provides restaurants a platform to market, engage, and sell online. It helps restaurants with online visibility, high content visibility, and in creating efficient online ordering systems.

Our Challenges:

1. Building a feature-rich application that is compatible with the available networks.
2. Launching the application to the soonest to gain an advantage in the online F&B market in India.
3. Building scalable application to handle high growth and be compatible with other mobile platforms.

What We Did:

1. Enabled location-based queries in MySQL to search and order meals from nearby restaurants.
2. Integrated the app with Paytm for hassle-free payments.
3. Followed a parallel path rather than sequential to design and develop the application to reduce the time to market.
4. Used Dagger dependency injection to accelerate the development time of the application by minimizing the excessive coding.
5. Used Queue-based architecture to make the system scalable. Utilized promise-based APIs for distributing system calls and reducing excessive coding to enhance the system’s scalability.

What We Secured:

1. Built a highly responsive application to make the process of meal ordering easy. Integrated features like search, order, pay, track, and feedback option for the users.
2. Rolled out from zero to alpha version in just 50 days.
3. Delivered an application that would handle the client’s expected growth. To achieve this we leveraged highly scalable and distributed system architecture.
4. Achieved our targets without cash burn.

Increasing Revenue Realization by Leveraging Big Data.

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Head Quarter: Silicon Valley, California

Industry: Mobile Marketing Platform

Opera Mediaworks (now AdColony) ensures higher advertising efficiency and better monetization by managing target audiences and publishers effectively for advertisers.

Our Challenges: 

1. Processing large data volumes in real-time to guarantee better revenue management.
2. Utilizing customer segmentation data to improve ad targeting and increase opportunity costs.
3. Re-engineering the product to tackle high growth effectively.

What We Did:

1. Used AWS platform to launch cluster nodes in real-time for auto scale and used Kafka to improve transfer rates between nodes while reducing cost.
2. Deployed Hadoop-based Elastic Map Reduce (EMR) to integrate data and the data processing logic seamlessly.
3. Bolstered real-time processing with Apache Spark.
4. Rationalized the support process for high throughput queries with a replica set of MongoDB clusters.
5. Stored raw data in a single, fail-over compliant location using Amazon’s S3 services.
6. Analyzed & stored data above 60TB using Amazon’s Redshift Data Warehouse. 

What we secured:

1. Better revenue management by optimizing inventory and serving the maximum number of ads.
2. Real-time inventory ensured transparency.
3. Improved analytics-enabled better customer sifting and targeting. 
4. Customized Big Data solution reduced cost by 75%.
5. The solution boosted the system performance by 5X. 

Expanding Engineering Capabilities To Foster Growth

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Head Quarter: San-Francisco, California

Industry: FinTech

The lending experience became easy and flexible for home buyers with Roostify’s digital mortgage platform. Lenders can collaborate with customers on this platform and bring down the turnaround time.

Our Challenges:

1. Client wanted to expand the engineering team to meet business demands.
2. Accelerating specific feature implementation as per pre-planned roadmap to serve existing customers in a better way.
3. Integrating Roostify with banking systems to onboard new customers quickly and smoothly.
4. Integrating the product with third-party software.

What We Did:

1. Set up a dedicated team and expanded it to meet demands.
2. Simplified integration with third-party-system like large banks involving various systems like Loan Origination Systems (LOS), Customer Relationship Management (CRM), and Product & Pricing Engine (PPE) etc.
3. Designed a highly customizable and future-ready middleware by expanding engineering capabilities to promote growth.
4. Integrated the product with multiple third-part software/services to increase product adoption. We created services for each category of software used by banks like PPE (Product and Pricing Engine), Credit Service, eSign Service, Verification, Automated Underwriting System (AUS), etc.

What We Secured:

1. Fulfilled business demand by shipping features quickly and serving customer categories of prospective, existing, and new.
2. Relieved customer onboarding by reducing the time spent on integrations with third-party systems.
3. Reached the next milestone by delivering on the product roadmap and ensuring faster customer acquisition.

Building An Enterprise-Ready Security Software Product

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Head Quarter: Fremont, California.

Industry: Cybersecurity

Tala Security protects mobile and PC users from malicious activities by offering an advanced AI-powered security solution to big enterprises.

Our Challenges:

1. To build an enterprise-ready product for the client to fulfill the demands of big firms.
2. To secure the web experiences for users by protecting the inline scripts. The client wanted to get the product enterprise-ready and onboard interested customers.

What We Did:

1. Our skilled developers followed the product roadmap reduced the response time from 150ms to 2ms for the Apache & NGINX versions.
2. Developed the IIS windows version to serve 40% of the web server market and implemented a single-click installation for IIS.
3. Used React.js, Node.js, Spark, Cassandra, PostgreSQL, ElasticSearch, and Python. Leveraged AWS and Docker for infrastructure.

What We Secured:

1. Quickly built the functionalities as per the client’s expectations.
2. Helped the client convert 5 large enterprises into paying customers. It ensured a new round of funding worth $3 million.

Powering Tech To Pace With Business Demands

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Head Quarter: San-Francisco

Industry: Cybersecurity

AlphaSense, an AI-powered search engine, provides corporations and financial institutions with business insights and market intelligence. It helps them make data-driven decisions and gain a competitive edge.

Our Challenges:

1. Expanding the engineering capability within a tight deadline and cater to the growing customer base.
2. The client wanted to leverage technical capabilities and capitalize on the huge sales opportunity.

What We Did:

1. Created multiple teams comprising developers, QAs engineers, and DevOps engineers to deliver features separately from design to deployment.
2. Enabled search to access real-time data from various sources and added the data selected from more than 1000 sources in the search results.

What We Secured:

1. Our solutions helped the client grow rapidly by getting investment banks and fortune 500 companies as their customers.
2. Matched the market demands by scaling the engineering capabilities 6X in 2.5 years.

Managing Over 8 Billion Ad Requests Per Day

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Head Quarter: Silicon Valley, California

Industry: Mobile advertising

Opera Mediaworks, the world's leading mobile advertising platform, is a part of Opera Software. It allows advertisers to reach their target audience by improving advertising standards and monetization.

Our Challenges:

1. Rapid scaling of engineering capabilities.
2. Re-engineering of product to improve efficiency and reduce effective cost/thousand ad impressions (eCPM).

What We Did:

1. Focused on database and infrastructure scaling along with modules decoupling.
2. Reduced I/O operations and leveraged big data analytics to achieve faster processing.

What We Secured:

1. Scaled the product from managing 22mn ad requests/day to support 8bn ad requests/day.
2. Ensured eCPM reduction by 94% in just 2 years.
3. Expanded engineering team from 2 engineers to 150 engineers.

A Journey From An Idea To Acquisition 

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Head Quarter: Mumbai, India

Industry: Financial & payments

Citrus Pay offers web and mobile payment solutions for merchants, it makes the online checkout processes easier and safer for its customers.

Our Challenges:

1. Ensuring high-quality engineering.
2. Building a seamless payment experience.
3. Upgrading to a version for improved success rate consisting of fraud detection, better security, dynamic pricing, and refund using live transaction.
4. Building a mobile app for android and iOS.
5. Implementing vault management system for consumer’s payment data storage.
6. Integrating third-party applications.

What We Did:

1. Introduced wallet application for customers.
2. Enhanced customer experience through turbo-checkout.
3. Improved the product with an updated version for a better success rate.
4. Added the feature of prepaid wallet with send and receive functionality.
5. Integrated third-party applications like LazyPay for consumers and UPI for merchants/consumers.

What We Secured:

1. Enhanced customer experience with seamless payments. Adding new features helped the client to raise $1.8M in Series A funding.
2. The new version improved the success rate. Users in this version could cancel and get a refund using the live transaction search feature. The client was able to achieve 2M payment transactions per month & raised $5.5M in Series B funding.
3. Introduced the citrus cash mobile app for android and iOS with prepaid wallet functionality, in-app payments, and native mobile software development kits. The client successfully reached the 10M payment transactions per month and spike in customer base to 10M+.
4. Created a vault management system for storing the consumer’s payment data.
5. The client further raised $25M in Series C funding.
6. Incorporated UPI functionality for merchants/consumers to transfer funds immediately.
7. The product was finally acquired by PayU for $130M.

Building Future-Ready Product with Blockchain

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Head Quarter: Mumbai, Maharashtra

Industry: Personal Finance

Cube simplifies banking services on app/cloud by automating user wealth, expenses, payments, documents, and philanthropy. It also provides real-time information movement with high security, using blockchain.

Our Challenges:

1. Building credibility by safeguarding the identity and personal data of users.  
2. Creating trust with banks and billers to initiate data sharing.
3. Reducing transaction friction by eliminating paperwork and simplifying Know Your Customer (KYC) processes across service providers. 
4. Allowing real-time settlement with banks and billers.

What We Did:

1. Decided to go with blockchain. By judging all the parameters, we opted for multichain.
2. Used distributed blockchain ledger to store KYC.
3. Developed a custom key-based search for KYC.

What We Achieved:

1. Helped Cube to establish trust with the customer and onboard 350,000 users in a short period.
2. Kept the product future-ready. With Reserve Bank of India and State Bank of India opening up to blockchain, Cube can leverage this edge over others.

Rationalizing Real Estate Investment Decisions Using Data Science

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Head Quarter: Irvine, California

Industry: Real Estate

HomeUnion is a leading online marketplace for real estate investment that offers fully managed properties sourced through due diligence and data analysis. It also offers post-purchase market intelligence, portfolio analysis, and management oversight for investors.

Our Challenges:

1. Handling an extremely large volume of data. Updated feeds needed reprocessing that made data processing complex.
2. Data requirement on various factors like crime rate, employment, income, schools, rental trends, etc. It required a Big Data pipeline processing factory.
3. Building algorithms, rating various attributes, and creating statistical models for arriving at the relevant predictive indices were necessary.

What We Did:

1. Implemented Data Science-based solution to collect, process, model, and analyze data and generate accurate insights.
2. Developed an automated data ingestion ecosystem to receive data from multiple public/private and free/paid sources.
3. Configured APIs to collect feature data, based on the frequency of its publishing/updation.
4. Adopted Big Data based solution using Map/Reduce, Hadoop, R, and Python systems to process large volumes of data.
5. Used exploratory analysis and feature engineering techniques to reduce the number of features to the most integral ones. This would produce the most accurate and relevant insights when modeled.
6. Used open source tools/programming languages like R, Python, and Hadoop, to build machine learning algorithms and statistical models.
7. Generated data-driven insights and analysis for investment risk, sale price range, rent prediction, and price appreciation, etc. at multiple geographical locations.

What We Secured:

1. Launched an end-to-end solution for real estate investment portfolio management and developed a feature-rich product.
2. Simplified portfolio investment decisions through data-driven assessment of investment worthiness. It helped the client close more deals than before.
3. Enabled multiple revenue streams.

Improving Product Adoption using Conversational AI

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Headquarter: Pune, India

Industry: Recruitment Software

Talentpool is software developed for recruitment purposes that address the requirements of small and mid-sized businesses. It works as a collaboration platform for recruiters, interviewers, and hiring managers.

Our Challenges:

1. Talentpool’s chatbot interface, called Maya, had limitations for its template-based approach.
2. A high number of cards on display made it less mobile-friendly.
3. Due to restricted visibility, the hiring managers and interviewers could only access the information set in the predefined flow.
4. Maya lacked the conversational flow.
5. Architectural limitations held back the template-based platform from supporting the Natural Language Processing (NLP).

What We Did:

1. To overcome the limitations of template-based UI, we decided to implement conversational AI.
2. While developing Maya 2.0, we leveraged RASA for code modifications and the addition of customized pointers.
3. Selected a proper ML model for the implementation of RASA.
4. Generated data using flexible template-based generators based on DSL (Domain Specific Language).
5. Used the training dataset to optimize RASA’s classifier algorithms & extract the right intents.
6. Customized RASA NLU to extract the required information.
7. Integrated RASA with the Knowledge Base to maintain the conversation's context and respond to the user queries accurately.

What We Secured:

1. Improved the user experience and increased adoption by making the system mobile-friendly.
2. Implemented AI/ML to resolve queries quickly and reduce the time spent by users for resolution.
3. Enabled new conversations by retraining the structure to deliver better responses.

Idea to MVP in 120 days

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Head Quarter: Mumbai, India

Industry: Risk Management

The founders wanted to build a product that simplifies decision-making for credit, risk, compliance, and supply chain professionals in the B2B domain.

Our Challenges:

1. The founders wanted to automate the process by leveraging technology.
2. Wanted to roll out the product’s first version within 120 days.
3. Looked for a strong technology partner to complement their expertise in risk management and analytics.

What We Did:

1. Developed the core functionality to assess risk.
2. Validated data with different data sets to improve accuracy.
3. Formed different teams with designers and DevOps to cover end-to-end design & deployment.
4. Reduced time-to-market by implementing battle-tested frameworks like React-Redux-Saga & Spring Boot.

What We Secured:

1. Reduced time to market.
2. Enabled MVP to scale with growing business demand.

We solved critical problems and introduced new features and brought bespoke innovation to the table every single time. When the client approached us with a ‘first-of-its kind’ and untapped idea of embedding videos into emails, we readily started working towards it. Thought it was a challenging one, our approach towards it was brilliant and that, in turn, drove some excellent results for the client.

Mist was looking for expertise in Data Science, AI, cloud DevOps, UI, and embedded, and we turned out to be a one stop for all their technology needs. Juniper Networks, who acquired Mist Systems for a $405M deal in 2019, considers it one of their best decisions till date.

During their hunt for a reliable technology partner, we stood out in terms of constructive criticism with a fiercely innovative bent. Commitment and motivation were two of our strongest ethics, which made us an organic part of the client's journey toward growth.