We Engineer
GenAI to Differentiate,
Not Just Function
Success with GenAI starts with accuracy—and accuracy doesn’t come prepackaged. It comes from carefully finetuned models, grounded in your domain, embedded in AI-centric architecture, and deployed with precision.
At Talentica, our AI-native engineers build full-stack GenAI systems where everything—architecture, data flows, DevOps, and UX—is engineered for AI from day one.
Because anyone can implement a model, but only a few know how to make it work in production with accuracy, uptime, and speed.
what we offer
We make GenAI work for you
Text Generation
Your data has its own dialect. We build LLM-based systems that speak your domain’s language—relevant, refined, and ready for action.
Multi-modal Intelligence
Not just cross-modal, but cross-intent. We design systems that fluidly reason across text, image, and structure—to mirror real-world complexity.
Autonomous Intelligence
Autonomous, accountable, and always learning. We build intelligent agents that collaborate, adapt, and execute—at scale and in sync with your business goals.
Image Generation
Where creativity meets context. We build GenAI systems that generate and edit images with precision—understanding structure, detail, and intent.
Video Generation
Motion with meaning. We engineer video-generation models that understand transitions, context, and visual storytelling — not just frames, but flow.
Audio/Music Generation
Engineered to echo emotion. We engineer models that generate, remix, and clone audio with precision—balancing fidelity, intent, and creative flow.
Customers who grew with us
OUR WORK IN ACTION
Helping businesses differentiate with GenAI
Increasing Email Engagement
An email marketing platform wanted to boost email engagement using AI-generated images and AI subject line helpers.
Pose and Expression Transfer in Videos
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
The company providing project flow management software wanted to replace videos and PDFs with an AI chatbot for guiding users through complex Gantt flows.
Cloning Artist’s Voice
An animation company wanted us to use GenAI to create audio software that could mimic artists’ voices.
Agentic AI-powered NL2SQL 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.
Multi-agent System for an Autonomous Marketing Campaign
The customer wanted to automate the entire campaign lifecycle to maximize the return on investment.
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 GenAI Expert
Suman Saurav
Senior Software Engineer, Data Science
Alumnus of NIT Agartala
A data scientist with 16+ years of experience, including 5+ years building GenAI solutions—from recommendation engines and agentic RAG systems to production-grade generative AI products. He’s passionate about applying LLMs to solve real-world business challenges across industries.
DIG DEEPER
Insights from our GenAI ecosystem
Watch Video
Beyond LLMs- The Power and Pitfalls of Multi-Agent AI
Principal Data Scientist
Technologies
FRAMEWORKS
PLATFORMS
LANGUAGE
FAQs
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.