AI & Automation Solutions
Service area
Data Analytics & AI
Focus
AI features in production
Category
AI & Automation
Engagement
Assessment, then build
Putting AI features into production, with the guardrails and monitoring that keeps them there.
A prototype that works in a notebook is a long way from a feature customers depend on. The gap is data quality, latency, cost per call, evaluation, and knowing when the model has quietly started being wrong.
Decide whether AI is the right tool
Some problems are a model; many are a rule, a query or better data entry. We say so before anyone commits a budget to inference costs.
Data readiness
Sources connected, cleaned and made repeatable, because model quality is bounded by the data underneath it.
Evaluation before rollout
A test set and scoring method agreed up front, so "is it working?" has an answer that is not somebody's impression.
Production guardrails
Prompt and model versioning, cost and latency budgets, fallbacks when the provider degrades, and monitoring for drift after launch.
What you get
- Feasibility assessment with a recommendation
- Data pipeline and evaluation harness
- Deployed service with versioning and fallbacks
- Cost, latency and quality monitoring
Typical stack
- Python
- LLM APIs
- Vector databases
- Airflow
- Docker
Working on something similar? Tell us the constraints and we will come back with questions, then a scope. Related service: Data Analytics & AI.