From Prototype to Production — LLM & MLOps
Production AI engineering for a retail platform, moving from experimentation to governed, repeatable LLM operations.
SOURCE-BACKED · CLIENT ANONYMISED
01 / CHALLENGE
The business problem
The engagement source describes an environment without production MLOps, with model-drift risk and slow release cycles.
02 / INTERVENTION
What Teckventus delivers
- Production MLOps pipelines
- Model registry
- Low-latency inference infrastructure
- LLM deployment
- Governance playbooks
03 / ARCHITECTURE
Engineering architecture
Data & Model Layer
MLOps Pipeline
Model Registry
LLM / Inference Layer
Application APIs
Governance & Monitoring
04 / DELIVERY
Delivery model
TEAMAI / ML engineering delivery team — composition not publicly disclosed
DURATIONProduction pipeline reported live in 8 weeks
MODELAI / ML engineering delivery — commercial model not publicly disclosed
05 / TECHNOLOGY
Technology stack
LLMMLOpsModel RegistryInference InfrastructureData EngineeringGovernance
06 / IMPACT
Measured impact
8 wksProduction LLM pipeline reported live
3×Reported increase in deployment frequency
07 / SCALE
Repeatability & expansion
The delivery pattern can extend across enterprise GenAI applications, model operations, evaluation, monitoring and governed AI productionisation.
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