Talentica > Enterprise > Productionizing AI
Productizing AI
Build and scale AI systems with LLMOps, security, governance, and production optimization.
The Problem
Production introduces constraints a pilot never sees
The path between the proof of concept and production is where most AI initiatives break down. The model does great in a demo only to struggle with real data, scale, and edge cases. Evaluation is often manual, data is messy, and guardrails added late. Model and user behavior can drift even after launch. Getting AI to work is half the job. Keeping it working is the rest.
What we do
The production foundation for AI
We build beyond the model, adding the infrastructure, controls, and assurance required to deploy AI securely, reliably, and cost-effectively at enterprise scale.
Build the compute, serving, data, and orchestration foundation required to run AI at scale.
- Compute & accelerator architecture
- Model serving infrastructure
- Data & vector infrastructure
- Platform orchestration
- Resilience & scalability
Automate how models, prompts, datasets, and releases move safely from development into production.
- Training & inference pipelines
- Model, prompt & dataset versioning
- Experiment tracking & registries
- CI/CD for AI systems
- Safe release management
- Feedback & retraining workflows
Test models, RAG systems, and agents against quality, failure, and real-world performance criteria.
- Model & LLM evaluation
- RAG evaluation
- Automated regression testing
- Adversarial & failure testing
- Human evaluation
- Release certification
Protect AI systems with controls for data, access, prompts, tools, outputs, and governance.
- Prompt injection & jailbreak protection
- Data protection
- Identity & access controls
- Input & output guardrails
- Agent & tool security
- Auditability & governance
Track system behavior, quality, drift, latency, and failures across live AI workloads.
- End-to-end tracing
- Runtime monitoring
- Quality & drift monitoring
- LLM & agent observability
- Dashboards & alerting
- Incident analysis
Improve inference speed, infrastructure efficiency, and unit economics without compromising quality.
- Model selection & routing
- Inference optimization
- Prompt & context optimization
- Caching & batching
- GPU & infrastructure efficiency
- Cost monitoring & controls
How we work
A production-first approach to AI
We design for real data, real users, and real operating conditions from the start.
Architecture & solution design
End-to-end system design across data, models, apps, and integrations, with scalability, security, and compliance built in before development begins.
AI engineering & platform build
The production foundation across MLOps/LLMOps pipelines, data workflows, model serving, application layers, and system integrations.
Model, agent & system validation
Models, prompts, retrieval, and agent workflows stress-tested against real-world constraints for accuracy, latency, security, and robustness.
Production operations & optimization
Monitor live systems for performance, behavior, cost, and drift, and continuously optimize them using production and usage signals.
Case Study
Shipped, scaled, and running
80 billion bid requests a day, scored in 3ms
Revenue was capped by how efficiently the platform could handle very high-volume real-time bidding traffic, needing per-bidder optimal floor pricing computed inside the live request path.
- Approximate matching via MinHash and Locality-Sensitive Hashing for Traffic Optimization
- A custom classifier for bid-request-level traffic optimization
- Reinforcement learning setting floor-price policy at the individual bidder level
- A request-time feature pipeline feeding low-latency inference (3–5ms) directly in the bid path
- Throughput engineering sustaining 80+ billion requests a day with response-rate and revenue monitoring
The platform now scores 80B+ requests a day at 3–5ms latency, lifting response rate by 3.2% and daily average revenue by 4%.
Our partners
Tools & technologies
Data & Data Engineering
AI & ML
LLM & Generative AI
MLOps & Infrastructure
Observability & Monitoring
Use case ready? Let's build the product.
Move from a defined use case to a product built for production.