Talentica > Startup > Productized AI
Productize AI
We help you build what it takes to win your first enterprise customer.
The Problem
The Real Challenge
Begins After the AI Breakthrough
A distinctive AI capability creates a competitive advantage. Turning that advantage into business growth depends on how effectively it is productized.
Enterprise customers evaluate the complete product experience, not just the underlying model. Reliability, integration, security, scalability, and usability all play a critical role in adoption.
The right product and engineering foundations determine whether an AI innovation becomes an enterprise-ready product that customers can confidently adopt and scale.
What We Offer
Everything Needed to Productize AI
We help startups transform AI capabilities into products that customers can confidently adopt, trust, and scale. Combining expertise in AI engineering, data platforms, architecture, reliability, security, and performance, we build the foundations required for successful product adoption.
AI products require different user experiences. We help you create experiences built for human-AI collaboration.
- Human – AI collaboration
- Human approvals
- Non-deterministic responses
AI is only as good as the data behind it. We help you create a reliable data layer that continuously powers your product.
- Data Ingestion & CDC
- Streaming & Batch Pipelines
- Feature Engineering
- Multi-source Data Integrations
The best model isn’t always the biggest model. We help you choose, customize, and optimize models for your specific use case.
- Predictive & Decision Models
- Computer Vision
- Generative AI
- Fine-Tuning & Optimization
Building a model is one thing. Running it reliably in production is another. We help you deploy, monitor, and scale AI systems that perform consistently as your product grows.
- Training & Inference Pipelines
- Model Deployment & Scaling
- Monitoring & Observability
- Model Reliability & Drift Management
Enterprise customers don’t just buy capabilities. They buy trust. We help you evaluate, secure, monitor, and govern AI systems for real-world deployment.
- AI Evaluation
- Model Testing & Validation
- Guardrails & Governance
- Security & Compliance
How We Help
From model to market
We bring together the product, data, AI, and platform foundations required to operationalize AI successfully
Design
We design experiences that enable effective human-AI collaboration and help users understand, trust, and adopt AI.
Engineer
We build the data foundations, models, integrations, and infrastructure required to operate AI reliably in production.
Evaluate
We measure quality, reliability, safety, and performance to ensure AI systems meet real-world and enterprise expectations.
Optimize
We continuously improve latency, cost, accuracy, and operational performance based on production usage and feedback.
Scale
We establish the governance, observability, and deployment practices required for sustained enterprise adoption and long-term growth.
Case Study
Proof in Production
Case Study 1
A video communication platform wanted to generate highly realistic videos by transferring facial expressions and body movements from celebrity talk-show clips. Several technical challenges made production deployment difficult:
- Capturing and processing high-quality source footage to accurately replicate facial expressions and body movements.
- Generating poses with expressions presents a challenge, and this aspect is currently in the research phase
- Transferring all expressions to an image is difficult with current technology
We combined deep learning models with classical computer vision algorithms. Implemented a method to refine poses and used advanced technology to transfer facial expressions. Finally, upscaled and restored the final output video.
Generated output resembling a real video. Moved the solution to production. Currently, it is in the alpha-100 release stage
Case Study 2
A design-focused industrial company needed an AI assistant that could understand natural language questions and retrieve information from both databases and internal knowledge repositories.
Implemented an Azure OpenAI-based solution to interpret and reason through user queries, converting them into the appropriate set of database queries.
Additionally, the system determines whether a query is intended for wiki documents or the database. To ensure scalability and meet response time constraints, AWS OpenSearch and Azure’s LLM API were utilized.
The system handled 50 queries with over 90% accuracy. User input in a second iteration corrected the remaining queries.
Case Study 3
A B2B marketplace wanted to improve partner engagement and product discovery by delivering highly relevant recommendations based on user behavior and vendor metadata.
Developed a B2B recommendation system to help partners discover vendor products based on their browsing history and metadata in a vendor-partner scenario.
Recommended Top 20 products of interest to the user
Our Partners
Technologies
Data & Data Engineering
AI & ML
LLMs & Generative AI
MLOps & Infrastructure
Observability & Monitoring
Evaluation & Governance
Ready to Productize Your AI?
Turn your AI capability into a production-ready product built for scale, reliability, and enterprise adoption.