LLMOps
Operate LLMs like production software
We design and manage enterprise-grade LLMOps frameworks that transform LLMs into secure, scalable, and observable production systems — covering prompt/version management, evaluation pipelines, cost controls, safety guardrails, and operational runbooks.
How we approach LLMOps
- Prompt/model versioning with release gates
- Continuous evals that catch regressions before customers do
- Cost and safety observability across the LLM stack
At a glance
- Category
- Artificial Intelligence
- Engagement
- 4–8 week pilot → production rollout (typically 3–6 months)
- Related products
- Custom build
What we deliver in this engagement
A practical scope you can evaluate against large consulting SOWs — focused on shippable outcomes.
Use-case discovery & ROI framing
Architecture & model selection
Evaluation & guardrail design
Production hardening & observability
Handover, training & operating model
What success looks like
- Prompt and model versioning with release gates
- Continuous evaluation catching regressions before release
- Full-stack observability for cost, latency, and quality
How we deliver
- 1
Baseline
Define evaluation datasets and quality SLAs.
- 2
Automate
Build CI/CD for prompts, models, and configs.
- 3
Govern
Apply safety, access, and cost policies.
- 4
Operate
Monitor and improve in production continuously.
Questions about LLMOps
Straight answers for buyers comparing VynelixAI with larger analytics and AI consultancies.
Related services
Artificial Intelligence
AI systems that decide and act — with governance built in
Human intuition meets machine intelligence — powering faster decisions and real business impact at speed and scale.
- · Production AI systems aligned to business outcomes
- · Intelligent automation that reduces manual effort
Agentic AI
Agents that work inside enterprise guardrails
Autonomous intelligence that thinks, acts, and adapts — empowering businesses to decide and act in real time.
- · Autonomous agents scoped to safe, governed actions
- · Multi-agent orchestration for complex workflows
Generative AI
GenAI that survives contact with production
Operationalize generative AI into a secure, scalable, and governed enterprise capability.
- · Production GenAI apps with measured quality and latency
- · Cost-optimized model routing and caching strategies
Ready to start LLMOps?
Share your use case — we'll propose a pilot plan and be clear whether a product accelerator or custom build is the faster path.