Inferstack helps mid-market and enterprise leaders move agentic AI from pilot to production. We design the systems, embed with your team, and stay on for the long arc — measured in earnings impact, not slideware.
MIT's 2025 GenAI Divide study found 95% of enterprise AI pilots produce no measurable P&L impact. We design every engagement against that curve.
Our open-source contributions to the SGLang inference scheduler — including a Rust runtime rewrite — cut TTFT by 40% under concurrent agentic load.
The same scheduler work delivers 50% more tokens per second per GPU on multi-agent workloads — the difference between an agent that ships and one that doesn't.
Most consultancies stop at the deck. Most product vendors stop at the API. We do the work in between — the systems-engineering layer where agents become reliable, governed, and worth keeping in production.
Production agent architectures across your highest-leverage workflows — submission triage, claims processing, research, ops automation. Tool orchestration, memory, evaluation, and the human-in-the-loop controls that keep regulators comfortable.
Most enterprise platform deployments fail not on the platform but on the integration. We deploy the leading agent platforms into your real systems — your CRM, your policy admin, your SharePoint, your data warehouse — with the change management to make it stick.
RAG engineered for production: hybrid search over millions of documents, structured retrieval, citation integrity, and the evaluation harness to know — quantitatively — when retrieval is failing in the wild.
Latency budgets, scheduler tuning, evaluation pipelines, incident response. We treat agentic AI as a real-time system — not a demo — and bring open-source inference work to clients who need agents to scale past the pilot.
Built an agentic submission intake and triage system that cut time-to-first-quote from 3 days to under 1 hour, reduced underwriter admin time by 80%, and improved hit ratio by 3 points.
Designed a multi-agent KYC enrichment system across regulatory data sources. Cut review time per file by 60% and reduced false-positive escalations by half — passing audit on first review.
Deployed a claims triage and denial-management agent across a multi-site provider group. Reduced first-pass denial rate by 28% and shortened claim-to-cash by 11 days on average.
If your team is moving agentic AI from pilot to production, we would like to hear what you are working on. The more specific, the better — what you have tried, where it broke, what good looks like.
We read every submission personally and will follow up shortly.