We Don’t Just Plug in AI. We Engineer Intelligent Systems End-to-End.
At Talentica, we go beyond model integration — we build intelligent systems from the ground up. Our AI-native team brings together GenAI specialists, classical ML experts, and full-stack engineers who deeply understand how to turn AI into actual product outcomes.
We take full ownership of the AI lifecycle — from product engineering and data pipelines to AI architecture and model tuning — ensuring your product isn’t just AI-enabled, but AI-capable and production-ready.
Our strength in classical ML lets us sharpen GenAI outputs for greater accuracy,reliability, and control. Combined with reusable AI prefabs and proven frameworks, we accelerate time-to-impact without compromising quality.
what we offer
We make AI work for you.
AI Agents
Autonomous, accountable and always learning. We build intelligent agents that collaborate, adapt, and execute- at scale and in sync with your business goals.
- Multi-Agents at Scale
- MLOps
- Orchestration AI agents
- Goal-Oriented Task Planning
Vertical LLMs
Your domain is unique, your models should be too. We build vertical LLMs : context-aware, fine-tuned, and scalable, to give your business a strategic edge.
- Fine Tuning
- Pre Training
- Open Source LLMS
- Scale
- Domain Adaptation
Classical ML
Generative AI isn’t always the answer. We apply classical ML where it fits best—delivering explainable, accurate, and production-grade models built to scale with your product.
- Predictive Analytics
- Computer Vision
- Reinforcement Learning
AI-Driven Insights
Data alone doesn’t drive decisions—insights do. We build pipelines, dashboards, and conversational tools — so your teams can act faster and make confident decisions.
- Data pipelines
- Knowl. Graphs
- Anomaly Detection
- Dashboards
- Conversational Data Analytics
AI in Architecture
AI needs the right foundation. We design modular, governed architectures—ready for scale, modernization, and edge deployment to ensure long-term AI success.
- Modernisation
- Edge AI
- Multi- Agents architecture
- Integrations
- Governance
AI in Security
Security shouldn’t wait for an incident. We enhance it with Gen AI or ML— detect threats early, fix vulnerabilities fast, and protect data continuously—at enterprise scale.
- Threat Detection
- CVE fixes
- Privacy preserved AI
- Security Incident Prediction
what we offer
We make AI work for you.
AI Agents
Autonomous, accountable and always learning. We build intelligent agents that collaborate, adapt, and execute- at scale and in sync with your business goals.
Vertical LLMs
Your domain is unique, your models should be too. We build vertical LLMs : context-aware, fine-tuned, and scalable, to give your business a strategic edge.
Classical ML
Generative AI isn’t always the answer. We apply classical ML where it fits best—delivering explainable, accurate, and production-grade models built to scale with your product.
AI-Driven Insights
Data alone doesn’t drive decisions—insights do. We build pipelines, dashboards, and conversational tools — so your teams can act faster and make confident decisions.
AI in Architecture
AI needs the right foundation. We design modular, governed architectures—ready for scale, modernization, and edge deployment to ensure long-term AI success.
AI in Security
Security shouldn’t wait for an incident. We enhance it with Gen AI or ML— detect threats early, fix vulnerabilities fast, and protect data continuously—at enterprise scale.
Customers who grew with us
OUR WORK IN ACTION
Transforming businesses with AI
Automated Marketing Campaigns with Agentic AI Systems
A marketing startup wanted to build autonomous AI workers to plan, execute, and optimize customer outreach.
Agentic AI-Powered Database Query System for Marketing Solutions
A B2B partner marketing platform needed a natural language chat interface to simplify campaign execution for both technical and non-technical users.
Streamlining Business Processes with Autonomous AI
A company aimed to build next generation of business applications that could autonomously perform complex tasks with high accuracy.
Recommendation System (B2Ba)
A curated products online company wanted to increase user engagement.
Computational Histopathology
A biotech firm wanted to recognize cell shapes and detect cells undergoing mitosis in H&E-stained tissue samples.
Predicting Viewer Ratings
A TV advertising company wanted to build an in-house rating prediction solution that is comparable with third-party solutions.
Increasing Email Engagement (Multi-modal)
An email marketing platform wanted to boost email engagement using AI-generated images and AI subject line helpers.
Pose and Expression Transfer in Videos (Video)
A platform for video correspondence wanted to use generative technology to map faces and body movement from celebrity clips for immersive visuals.
Creating Support Assistant (Text)
The company providing project flow management software wanted to replace videos and PDFs with an AI chatbot for guiding users through complex Gantt flows.
Domain-Specific LLM Fine-Tuning
A fintech company needed a domain-specific LLM to understand financial jargon, reduce hallucinations, and address privacy issues.
OUR WORK IN ACTION
Transforming businesses with AI
Automated Marketing Campaigns with Agentic AI Systems
A marketing startup wanted to build autonomous AI workers to plan, execute, and optimize customer outreach.
Agentic AI-Powered Database Query System for Marketing Solutions
A B2B partner marketing platform needed a natural language chat interface to simplify campaign execution for both technical and non-technical users.
Streamlining Business Processes with Autonomous AI
A company aimed to build next generation of business applications that could autonomously perform complex tasks with high accuracy.
Recommendation System (B2Ba)
A curated products online company wanted to increase user engagement.
Computational Histopathology
A biotech firm wanted to recognize cell shapes and detect cells undergoing mitosis in H&E-stained tissue samples.
Predicting Viewer Ratings
A TV advertising company wanted to build an in-house rating prediction solution that is comparable with third-party solutions.
Increasing Email Engagement (Multi-modal)
An email marketing platform wanted to boost email engagement using AI-generated images and AI subject line helpers.
Pose and Expression Transfer in Videos (Video)
A platform for video correspondence wanted to use generative technology to map faces and body movement from celebrity clips for immersive visuals.
Creating Support Assistant (Text)
The company providing project flow management software wanted to replace videos and PDFs with an AI chatbot for guiding users through complex Gantt flows.
Domain-Specific LLM Fine-Tuning
A fintech company needed a domain-specific LLM to understand financial jargon, reduce hallucinations, and address privacy issues.
Our Partners
Customer Speak
“What I like most about Talentica is their ability to solve tough, cutting-edge problems with skilled engineers who are proactive and committed. They’ve consistently delivered high-quality products on tight timelines, making them a reliable partner for building innovative solutions from the ground up.”
Sudhir Menon
Co-founder & CPO
“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.”
Bob Friday
Co-founder & CTO
“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.”
Carmelle Cadet
Founder & CEO
“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.”
Luke Jubb
President & COO
Meet Our AI Expert
ABHISHEK GUPTA
Principal Data Scientist & Head of Data Science
Alumnus of IISc Bangalore and IIT Varanasi
Abhishek brings 10+ years of expertise in generative AI, NLP, mathematical optimization, and video recognition and pattern recognition algorithms to help startups and tech companies build production-grade AI products.
DIG DEEPER
Insights from our AI ecosystem
Watch Video
Will Agentic AI Replace or Reinvent SaaS?
Alakh Sharma, Senior Data Scientist
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.
- 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.
- Data privacy—Security is still a big concern for many companies, particularly tech giants, as they want to prevent data breaches at all costs.
- 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.
- 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.