Deployment and model ops that keep a model good after launch.
Shipping a model is the beginning. Data drifts, products change, and new base models arrive every quarter. We run the loop that keeps your model ahead.
A fine-tuned model in production needs the same operational care as any critical service, plus some that are unique to machine learning: quality can degrade silently while latency and error rates look perfect. Our model-ops practice connects serving, monitoring and retraining into one loop tied back to the evaluation suite built during the engagement.
We deploy into your cloud account or on-prem cluster using open serving stacks — vLLM, SGLang, TensorRT-LLM — so there is no dependency on us at runtime. Then, on retainer, we watch quality on sampled production traffic, collect corrections from your users, and retrain on a schedule or when drift crosses a threshold. When a better base model ships, we run the bake-off and migrate if it wins.
Signs you need deployment & model ops.
The right technique, chosen by the eval.
- Quantization. FP8, INT8 and INT4 (AWQ, GPTQ) builds, each validated against the eval.
- Serving. vLLM, SGLang, TensorRT-LLM, multi-LoRA and speculative decoding.
- Release engineering. Shadow traffic, canaries, automated rollback on eval or latency regressions.
- Continuous training. Feedback capture, retraining pipelines and quarterly base-model bake-offs.
- Infrastructure-as-code serving stack
- Dashboards for quality, cost and latency
- Retraining pipeline and release gates
- Runbook and on-call handover
All assets transfer to you at handover. No licence fees.
Questions buyers ask us
Can you deploy on our existing platform?
Yes. We deploy onto managed endpoints on every major cloud, onto Kubernetes clusters, Databricks model serving, or bare-metal GPU servers.
How often should a fine-tuned model be retrained?
It depends on how fast your data changes. Monthly or quarterly is typical; we trigger off drift metrics rather than the calendar where possible.
What GPUs do we need to serve it?
An 8B model quantized to INT4 or FP8 serves comfortably on a single mid-range data-center GPU; 70B-class models need one to four high-memory GPUs. We benchmark on your target hardware before committing.
Bring us the task. We'll bring the model.
A 45-minute technical briefing with a senior research engineer. You leave with a feasibility read, a data plan and a fixed quote — whether or not you hire us.
Book a technical briefing →