Building an AI prototype is easy. Running it reliably at scale — with monitoring, versioning, cost controls, and quality gates — is where most brands fail. We provide the LLMOps infrastructure that keeps your production AI fast, cheap, and trustworthy.
LLMOps (Large Language Model Operations) is the engineering discipline of deploying, monitoring, and maintaining AI language models in production. DTC brands need it because AI features — product recommendation engines, AI customer service, content generation pipelines — behave differently in production than in development. Without LLMOps, you get unpredictable costs, inconsistent quality, model drift, and no visibility into what your AI is actually doing at scale.
Once your AI features are making more than 1,000 LLM calls per day, informal management stops working. At that volume, API costs become a significant line item, latency variations affect user experience, and quality drift starts showing up in your data. We typically recommend a structured LLMOps foundation from the moment an AI feature goes live in production — it's far cheaper to build it correctly from the start than to retrofit it after a production incident.
The biggest cost drivers in production LLM deployments are unnecessary model size (using GPT-4 for tasks a smaller model handles fine), prompt verbosity (long system prompts repeated on every call), and cache misses (re-computing identical or near-identical queries). We address all three: intelligent model routing by task complexity, prompt compression and template optimisation, and semantic caching infrastructure that returns stored results for similar queries — typically cutting costs 40–70%.
Yes — we manage the full fine-tuning pipeline: data collection and curation from your historical content, training data formatting (JSONL instruction-response pairs), supervised fine-tuning on OpenAI, Anthropic, or open-source models, evaluation against your quality benchmark, and safe deployment with a shadow-mode testing period before full production rollout.
Our standard monitoring stack covers: per-request latency and token counts with p50/p95/p99 breakdowns, output quality scores from an LLM-as-judge evaluator, cost attribution by feature and model, PII and safety flag rates, user satisfaction signals where applicable (thumbs up/down, correction rates), and daily digest reports with anomaly alerts sent to your engineering team.
Our LLMOps team builds the monitoring, cost controls, and quality gates that keep your production AI reliable as your DTC brand scales.