LLMOps
Shipping the agent was the easy half. Now a model deprecation notice lands with 90 days' warning, token spend doubles without a deploy, a prompt tweak from three weeks ago is quietly degrading answers, and nobody can say which version of what produced the output a customer is disputing. Welcome to operating LLM systems — the discipline your pilot never needed and your production system can't live without.
We build the operational layer: prompts, models, and eval datasets versioned together so every output is reproducible; regression gates so changes prove themselves before shipping; cost telemetry per feature and per customer so finance stops discovering surprises; drift monitoring so quality degradation pages someone; and runbooks for the new incident classes — provider outages, model regressions, jailbreak waves — that traditional ops never met.
Version discipline
Prompts, model pins, and eval sets versioned as one unit — every production output reproducible.
Eval-gated releases
Quality regression blocks a prompt change the way a failing test blocks code.
Cost observability
Token spend per feature, per customer, per model — with budgets and alerts, not month-end surprises.
Drift and quality monitoring
Sampled production scoring that catches degradation before users report it.
LLM incident response
Runbooks and fallbacks for provider outages, deprecations, and model regressions.
How is LLMOps different from regular DevOps?
The artifacts and failure modes are new: behavior lives in prompts and model versions, not just code; failures are quality drift, not stack traces; and a vendor can change your system's behavior overnight. The discipline — versioning, gates, monitoring, runbooks — is the same spirit applied to stranger materials.
A model we depend on is being deprecated. How bad is this?
With eval coverage, it's a project; without it, it's a gamble. We run the migration through your golden datasets, fix the prompts that regress, and cut over behind a flag with rollback ready. Teams that treat model upgrades like database migrations do them quarterly without drama.
Our token costs are climbing faster than usage. Why?
Usually invisible waste: verbose prompts duplicated per call, missing prompt caching, premium models doing work a cheaper tier handles, and retry storms nobody graphed. Cost observability finds it; caching, routing, and prompt hygiene typically claw back 40–70% of spend.
Put LLMOps to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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