LLM Workflow Deployment
Retrieval, summarization, classification, and generation pipelines — deployed into your products with evaluation and guardrails built in.
An LLM call is easy. An LLM workflow you can depend on is not. Production pipelines have to handle retrieval quality, context limits, hallucination, cost, latency, and silent quality drift — all while staying wired into live data. We build and deploy LLM workflows engineered for that, not just prompted into existence.
Design the pipeline around your data
Retrieval, chunking, context assembly, and routing decided deliberately for your data and your use case — because most LLM quality problems are data and retrieval problems, not model problems.
Build evaluation in, not on
We instrument every workflow with an evaluation harness from the start: golden datasets, automated scoring, and regression checks — so a prompt change or a model swap can't quietly degrade quality.
Guardrail the output
Structured-output validation, grounding checks, fallbacks, and content guardrails. The workflow fails safely and visibly instead of confidently returning something wrong.
Optimize for real cost and latency
Model selection, caching, batching, and routing tuned against your actual traffic — so the workflow is fast enough and cheap enough to run at the scale you need.
What deploying an LLM workflow with us includes
- ✓Retrieval and pipeline architecture for your data
- ✓Evaluation harness with golden datasets and scoring
- ✓Output validation, grounding checks, and guardrails
- ✓Cost, latency, and model-routing optimization
- ✓Monitoring and drift detection in production
- ✓Deployment into your infrastructure, with full handover
Frequently Asked Questions
What counts as an LLM workflow?
Any pipeline built around language models — retrieval-augmented generation (RAG), summarization, classification, extraction, content generation, or multi-step chains that combine several of these. If it's an LLM doing real work in your product, it's in scope.
How do you stop an LLM workflow from hallucinating in production?
You can't eliminate it, but you can engineer around it: grounded retrieval, structured-output validation, grounding checks, confidence thresholds with fallbacks, and continuous evaluation that catches quality drift before users do.
Can you deploy RAG systems specifically?
Yes — retrieval-augmented generation is one of the most common workflows we deploy. Most of the engineering effort goes into retrieval quality and evaluation, which is exactly where we focus.
Do you work with our existing model provider?
Yes. We're provider-agnostic and will work with whatever you use — or recommend a setup if you're starting fresh. You own the deployment and the code regardless.
Ready to deploy?
Tell us what you're building. We'll tell you what it takes to ship it — reliably, securely, and at scale.