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We build the routing, the traces, the workload identity and the eval suite a production LLM feature needs. Most teams get the demo working. The gap between that and ten thousand requests an hour is the job.
LLM routing demo
Written from engagements, not from conference talks.
Your traffic isn't one shape. Most requests are classification or extraction that a cheap model handles at a fraction of the price. Route on that difference and spend drops by 60–80% with no movement in your quality metric. Here's the architecture, including the two failure modes we hit first.
An inference server might live ninety seconds before it's replaced. You can't hand it a certificate. SPIFFE/SPIRE gives it a cryptographic identity it can prove in that window, and it works with vLLM and LiteLLM without touching your application code.
One user request can fan out into three tool calls, two model calls and a synthesis step. When it goes wrong you need to see the whole shape at once, not four dashboards. This is the span structure that makes that possible.
We read what you have and find where it breaks. Then we put a number on what the current setup costs you every month.
We design against your ceilings, not a reference architecture. If the routing layer cannot hold your p95, we find that out on paper.
We build it with you. The eval harness and the regression pipeline ship in the same week as the feature, because after that nobody goes back for them.
We stay ninety days, tune thresholds with your team, then leave you the runbooks and go quiet.
Every library here came out of a client’s problem. We kept them because the next client had the same one.
agentgatewaystableThe routing layer we deploy on every engagement. Classifies each request, opens a circuit when a provider degrades, and attributes every dollar to the team that spent it.
Details →llm-benchmarkingstableAn eval harness that runs your prompts against 40 benchmarks and tests whether a change moved the number. Blocks the merge when it made things worse.
Details →spire-agent-aibetaSPIRE workload API for AI runtimes. Rotates X.509 SVIDs without restarting the inference server, which is the part nobody wants to do at three in the morning.
Details →73% bill reduction
$1.1M a month down to $297K. LiteLLM routing and SPIFFE identity across three regions.
6 weeks to production
A full AI platform for a 400-engineer company, built by eight of their own people.
SOC 2 in 4 months
HIPAA-grade inference with residency enforced in three jurisdictions.
If you’re staring at an LLM bill you can’t explain, that’s usually where the conversation starts.