LLMOPS SERVICES
LLM deployment, evaluation and operations
The demo worked. Then it went to production, and started drifting in ways nobody could see.
- An evaluation harness scored on real long-tail cases, not demo cases
- Guardrails for PII, toxicity, and jailbreaks in the live request path
- Per-feature cost observability with budgets and alerts
Without instrumentation, a GenAI app is a black box that feels fine right until it doesn't.
These are the failure modes we see repeatedly, and the countermeasures we build in.
LLMOps is the language-model track of Scaled GenAI and AI Platforms, built on the same foundation as MLOps. It adds a layer classical MLOps never needed, built around evaluation and control, not just deployment.
RAG rot
The retrieval index goes stale or was chunked badly; answers degrade while the model still sounds confident. Countered by retrieval evaluation, freshness monitoring, and re-indexing pipelines.
Prompt sprawl
Prompts live inline across the codebase, edited by anyone, versioned by no one. Countered by centralized prompt management with versioning and rollback.
No eval harness
A prompt change looks fine but silently regresses the long tail, and nobody can tell. Countered by an evaluation suite scored on real cases, not demo cases.
Unbounded outputs
No guardrails on toxicity, PII leakage, jailbreaks, or off-topic responses. Countered by input and output guardrails and policy checks in the request path.
Cost creep
Token spend climbs with no attribution; one verbose prompt quietly doubles the bill. Countered by per-feature cost observability with budgets and alerts.
Evaluating on the happy path
Quality measured on the cases that already work, not the ones that don't. Countered by a long-tail eval set curated from production traffic.
Evaluation is the center of gravity. If you cannot tell whether a change helped or hurt, everything else is guesswork.
Built around evaluation and control, not just deployment.
Everything needed to operate a language-model app, and prove it is behaving.
- Prompt and version management with rollback and change history.
- An evaluation harness scored on real long-tail cases, gating promotions.
- Retrieval monitoring for freshness, relevance, and grounding.
- Guardrails for safety, PII, and topicality in the request path.
- Cost observability with attribution, budgets, and alerts.
- Governance hooks aligned to your Responsible AI requirements.
Five steps, and it starts with making the app visible.
Instrument first
Add tracing so you can see prompts, retrieval, tokens, and cost.
Build the eval set
Curate real cases, including the ones that fail today.
Gate changes
Wire evaluation into CI so regressions cannot ship silently.
Add guardrails and cost controls
Enforce safety and budgets in production.
Close the loop
Feed production failures back into the eval set continuously.
Built cloud-native on the platform you already run.
A representative stack by layer, weighted toward observability and evaluation. Figures are industry-typical placeholders; Softobiz to verify against your environment.
From a black-box prototype to an observable capability.
Challenge: A [global enterprise client] shipped a RAG assistant that drifted quietly after launch, with no way to tell whether a prompt change helped or hurt.
Result: An eval harness gating every change, retrieval freshness monitoring, and per-feature cost budgets. (Softobiz to verify.)
The tracks that share this foundation.
MLOps
Training, deployment, and drift for predictive models on the same foundation.
LLM Fine-Tuning
When behavior needs adapting beyond prompting and retrieval.
AI Agent Builder
For multi-step systems that need evaluation and control at every hop.
AI Platform Design and Implementation
The layered platform LLMOps instruments and runs on.
Dedicated Teams
The senior pod that instruments and runs your GenAI apps with you.
Scaled GenAI and AI Platforms
The parent practice this language-model track belongs to.
What GenAI teams ask us first.
MLOps governs training, deployment, and drift for predictive models. LLMOps adds prompts, evaluation, retrieval (RAG) monitoring, guardrails, and cost control, the concerns unique to language models. Most enterprises need both on one foundation.
Deployment is the start. Without retrieval evaluation and freshness monitoring, RAG quality decays invisibly. LLMOps keeps it accurate after launch.
Yes, we instrument what you have with tracing, an eval harness, and guardrails, without a rebuild.
Find where your language-model app is drifting, and instrument it before users do it for you.
An eval harness, guardrails, and cost controls, added to what you already run without a rebuild.