Our AI Development Capabilities

  • Generative AI

    Generative AI comes with exciting possibilities and inherent risks, and our experts know how to handle both with care. They adhere to industry-specific regulations and maintain the highest security standards while fine-tuning LLMs, managing on-prem deployment, developing intelligent assistants, and building models that turn text into images and videos, edit images, clone audio, generate music, and more.

  • Machine Learning & Pattern Recognition

    Building a solution involving machine learning is much more than the model. It is a complex mix of data structures, model training, model integration and architecture. We engage in end-to-end delivery of a machine learning solution tailored to bring product features to life.

  • Natural Language Processing

    There are many NLP APIs and services available today. Some of these services could give 80% accuracy on extraction tasks involving generic data. However, to solve really hard problems involving natural language understanding, especially with proprietary and small data sets, we need to skillfully use machine learning techniques along with traditional NLP algorithms.

  • Computer Vision & Image Processing

    Deep learning techniques have given a fillip to computer vision and image processing solutions. However, training models for proprietary and domain-specific data sets is a challenge. We find innovative ways to transform the domain-specific part of a problem into a generic computational problem in order to deliver practical solutions.

  • Mathematical Optimization

    Optimization algorithms are the foundation of modern-day machine learning. However, there is a rich history dating back to many decades. We strive to use these fundamental algorithms to deliver solutions to problems involving allocation, balancing, routing.

Insights From Our AI Experts

Case Study | March 28, 2023

Enabling Predictive Maintenance using NLP

Automate predictive maintenance for early fault detection, diagnosis, and prevention of the loss of service

Publication | January 13, 2023

Adopting AutoML: Let’s do a reality check

Alakh Sharma, Data Scientist at Talentica Software, has reviewed real-life cases to reveal AutoML’s potential and limitations.

Technical Paper | September 14, 2018

Learning to Fingerprint the Latent Structure (presented at the 17th IEEE-ICMLA 2018)

In this paper a mathematical model to capture and distinguish the latent structure in the articulation of questions is presented.

Publication | May 20, 2022

Operationalizing Machine Learning from PoC to Production

Many companies use machine learning to help create a differentiator and grow their business. However, it’s not easy to make machine learning work as it requires a balance between research and engineering.

Case Study | March 09, 2021

Improving Product Adoption using Conversational AI

Improving user experience for hiring managers and interviewers by adopting Conversational AI

Publication | March 27, 2022

This is what makes deep learning so powerful

The use of deep learning has grown rapidly over the past decade, thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to Emergen Research, which expects deep learning to become a $93 billion market by 2028.

Technical Paper | January 5, 2018

Solving a Network of Sensors Problem using Gradient Descent

In this research report, we highlight a problem formulation involving multiple sensors that collectively determine “characteristics” of targets in an environment. We show how the formulation can be solved with Lagrangian relaxation.

Publication | March 13, 2023

Data Science Bows Before Prompt Engineering and Few Shot Learning

While the media, general public, and practitioners of Artificial Intelligence are delighting in the newfound possibilities of Chat GPT, most are missing what this application of natural language technologies means to data science.

Meet the
Expert

Abhishek Gupta
Abhishek Gupta
Principal Data Scientist
  • Generative AI
  • Applied mathematical optimization
  • Natural Language Processing
  • Machine Learning & Pattern Recognition
  • Recognition algorithms for Video
Email Abhishek

Testimonials

Marketing

Client tenure: 1+ year

Asim Mohammad bgoverlay play_btn

“What I like the most is Talentica’s proactiveness to engage the product team and technology team and guide us in some alternative ways of thinking about different approaches that can be valuable to us. They also help us in going the extra mile by developing and prototyping ideas for us.”

Edtech

Client tenure: 10+ years

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“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.”

Networking

Client tenure: 6+ years

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“Talentica has been part of the family at Mist, and they have been a key part of our engineering team. They bring us startup spirit and a wide range of required skills like Data Science, AI, Cloud, DevOps, UI, and Embedded.”

Fintech

Client tenure: 2+ Years

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“For an early-stage startup like ours, Talentica understood what we thought about user needs and the problems we were trying to solve. They imbibed our vision and helped us design and build a product that will sell and get to the market successfully. They brought expertise in emerging technologies like artificial intelligence and blockchain to enable innovation for us.”

Fintech

Client tenure: 4+ Years

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“The teams at Talentica are focused on delivering outcomes towards growth. The expertise they have in cloud operations, data, QA, and micro-services have been very pleasing and something I like the most working with this team.”

Marketing

Client tenure: 10+ years

Matt Highsmith bgoverlay play_btn

“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.”

Marketing

Client tenure: 4+ years

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“During our hunt for a reliable technology partner, Talentica stood out in terms of constructive criticism with a fiercely innovative bent. We could see that commitment and motivation were two of their strongest ethics, which is why Talentica has become an organic part of our organization.”

Fintech

Client tenure: 8+ Years

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“Talentica has engineers who are not only technically savvy but also inherently problem-solvers. They solved some of our hard technology problems and provided answers to questions we didn’t have answers to. This was one of the biggest factors to trust Talentica with our engineering.”

Project Management

Client tenure: 10+ Years

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“Talentica has a strong sense of ownership that gets reflected in the quality, execution, and responsiveness. Also, they have a great mix of flexibility and discipline, which is essential for a startup type of environment.”

Looking to implement AI? We can help.

Careers

Data Scientist

Using analytical techniques, identify patterns and anomalies in data. Apply collective insights to derive predictive and analytic solutions.

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Current Openings

Have we got you excited? Take a look at our current openings

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AI Development FAQs

It depends on your AI readiness. Approximately 85% of AI and ML models fail due to a lack of technical expertise, the absence of the right tech team, ill-defined user personas, a mismatch between vision and product, misguided expectations, and many other reasons. You can avoid all these issues by starting your implementation plans with an AI implementation checklist. Drawing from our experience of building over 40 production-ready AI models, we have created a comprehensive checklist for you. Download it to review and enhance your plans accordingly.

We have deployed more than 40 AI models across industries. The list includes 

  • Image processing models for a healthcare startup
  • Video generation models for marketing platforms
  • Chatbots (RAG-enabled, assistants, and others) for industries like recruitment, IT, and security companies
  • Predictive analytics models for IT, healthcare, RealTech, and FinTech companies
  • Generative AI models for RealTech, FinTech, IT, marketing, and other industries
  • Automated workflow management for a marketing company

Multiple factors can impact the cost of AI development services. The project’s complexity is a big challenge as the complication of the algorithms can drive the costs high by demanding more resources and time. Sophisticated models also require more support in terms of GPUs and cloud services. 

A lack of data can also impact, as building the right data pipeline is a costly affair. Deciding between LLMs and classical models is another. LLMs are cheaper, but they can trigger privacy issues. In addition, LLM cost is incremental, and it increases with the number of users. Integration with the existing system, deployment, and maintenance of the model can also influence the overall cost.

The timeline for AI development projects varies based on type and complexity. Building a helpdesk or assistant and RAG pipeline can take at least 2 months while automating workflows can take at least a year.

The AI development team structure varies according to the product stage and project complexity. At a bare minimum, a Data Scientist can manage the Proof of Concept (POC) stage. For MVP development, a team comprising a Data Scientist and a Backend Engineer is necessary. For full product development, the team should include a Data Scientist, a Backend Engineer, a DevOps Engineer, and a CloudOps Engineer. Additionally, there may be a need for UX/UI Designers and QA Experts to ensure a seamless user experience and robust product quality.

Our approach is mostly requirement-driven. However, some questions fit most GenAI development processes and help decide the approach. Here they are-

  • How crucial is data privacy? 
  • What is the breakeven point for Open AI services and open-source models?
  • If OpenAI is the platform, then at what rate requests come?
  • What is the cloud environment we are using?
  • Are we okay with not having real-time responses?
  • Can we have open-source models with their own GPUs?
  • Do we have to generate pure images?
  • Do we have to use Llama models or Anthropic?

For effective generative AI implementation, always onboard product engineers with experience in Large Language Models (LLM), Prompt engineering, Agents, and Data Science.

We have deployed more than 15 AI models across industries. The list includes 

  • Image processing models for a marketing platform
  • Audio generation models for entertainment and animation companies
  • Video generation models for a marketing platform
  • Chatbots (RAG-enabled, assistants, and others) for industries like recruitment, IT, and security companies
  • Info extraction models for analytics, retail, and e-commerce companies
  • Automated workflow management for a marketing company

Generative AI has proven its capabilities in terms of improving productivity, managing workflow, and optimizing resource utilization. However, its proper impact depends on four major factors.

  1. ROI—GenAI pilots should establish clear success criteria before launch, focusing on measurable outcomes in two key areas: enhancing customer experience and reducing unit costs. This will help close the gap between their promise and reality.
  2. Data privacy—Security is still a big concern for many companies, particularly tech giants, as they want to prevent data breaches at all costs.
  3. Performance quality and response time- Sometimes, these two factors can adversely affect each other. For instance, while GPT-4o delivers results faster than GPT-4, the quality may be inferior. Prioritizing requirements based on the use case is absolutely necessary.
  4. Human supervision is required to ensure accuracy, ethical compliance, and quality control.

The ideal team composition for a generative AI project includes 

  • Project Manager to oversee timelines and coordinate efforts 
  • Data Scientists to manage data acquisition and preprocessing. 
  • Machine Learning Engineers implement and optimize the models
  • DevOps Engineers handle deployment and maintenance
  • UX/UI Designers focus on user-friendly interfaces
  • QA Engineers validate the software’s performance and reliability
  • Ethics and Compliance Officers ensure adherence to ethical standards, 

This comprehensive team structure can ensure the successful development, deployment, and maintenance of generative AI projects.

Implementing machine learning can help your business by improving decision-making, increasing efficiency, automating processes, and uncovering valuable insights from data. We have identified a faster method by initially leveraging LLMs to understand their scope and potential solutions. Subsequently, if there is a need, we transition to pure machine learning, which, although more time and resource-intensive, offers robust and precise results.

We have deployed around 30 models across industries. Some of these projects are- 

  • Reinforcement learning model for a mobile advertising company
  • Predictive analytics model for real estate, wireless network, TV advertising and email marketing platforms
  • Deep learning model to build a domain-specific Q&A system for a channel marketing company
  • Image processing technology for a biotech startup
  • Assistants for a recruitment platform

Developers with a good knowledge of either statistics or mathematics and Machine Learning Algorithms can build successful models. Expertise in Data Science is always a plus.

There are four main steps: business understanding, data acquisition, model development, and deployment. However, using LLMs can reduce cycle time by speeding up business understanding and data acquisition.

The required timeline in a machine learning development project is problem-specific and depends heavily on complexity. A simple project can be completed in 2 to 4 months, whereas a pure ML project with a high level of complexity may take 8 to 18 months to complete.