DigiEdze AI · LLMOps
Production AI Demands Production Engineering
Prompts, models, indexes and adapters are production dependencies. We build the lifecycle, versioning, deployment and monitoring layer that lets you change them safely and know immediately when something regresses.
- model lifecycle
- prompt lifecycle
- evaluation
- deployment
- monitoring
- routing
- cost
Reference architecture
- 06Prompt & config registry
- 05Model & adapter registry
- 04Evaluation gates
- 03Inference gateway — routing · quota · fallback · cache
- 02Serving runtimes
- 01Telemetry — quality · latency · cost · drift
The Problem
Ungoverned LLMOps Lead to
Prompts live in application code and change without review, testing or a rollback path.
Nobody can say which model version answered a given request last month.
A provider updates a model and quality shifts before anyone notices.
What We Build
LLMOps Capability Grid
Prompt lifecycle
Versioned, reviewed and tested prompts, deployable and revertible independently of application releases.
Model registry
Every model, adapter and index version registered with lineage, evaluation results and approval state.
Deployment strategies
Canary, shadow and A/B rollout for model and prompt changes, with automatic rollback on regression.
Inference gateway
One entry point enforcing routing, quotas, retries, fallback and caching across providers.
Monitoring and drift detection
Quality, latency, cost and refusal rates monitored continuously, with alerting on drift.
Cost controls
Token accounting per feature, team and tenant, with budgets enforced at the gateway.
How it works
A tooling layer that makes LLMOps operational as gateways and registries offer control, experiment tracking and CI/CD bring software rigor to prompt and model changes, and observability platforms plus semantic caching keep performance and cost in check.
Technology
- Inference gateways
- Model registries
- Experiment tracking
- CI/CD
- Observability platforms
- Semantic caching
Use cases
Where this lands in practice.
Technology
Weekly prompt and model changes shipped safely behind regression gates.
BFSI
Full version lineage for every AI decision, retained for audit.
Telecom
Routing and caching applied at gateway level across high-volume workloads.
Outcomes
What We Measure
It’s an operational discipline for faster changes with predictable performance and cost at scale. It covers metrics like how quickly a change moves from proposal to production.
- Change lead time
- Regression escape rate
- P95 latency
- Cost per thousand requests
Questions
LLMOps, Answered Directly
Is LLMOps just MLOps with a new name?
It inherits the discipline but the artefacts differ. Prompts, retrieval indexes, tool definitions and adapters are all deployable dependencies with their own lifecycle, and evaluation is probabilistic rather than a fixed accuracy metric. The gateway and the evaluation gate are the two components MLOps did not need.
Where do we start if we already have AI in production?
Get prompts and model versions under registry control, put an inference gateway in front of every call for telemetry and cost attribution, then add regression gates. That sequence gives you visibility and rollback before you attempt optimization.
Related services
Choose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Build Your LLMOps Discipline
Evaluation starts before deployment and continues thereby. Our teams set benchmarks and alignment with your business goals. We support operationalization, deployment, and continuous monitoring of LLMs.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT