Onpar Labs is a forward deployed engineering firm. Since 2018, its engineers have embedded within AI-native enterprises to take frontier models from pilot to production, under the governance, data, and operational constraints that define real institutions.
Onpar engineers operate within the client's environment, codebase, and operating rhythm. They inherit the institution's constraints rather than abstracting them away.
Retrieval, evaluation, orchestration, and governance infrastructure. The systems that separate a capable model from a dependable one.
Every deployment concludes with the client's engineers owning the system in full. Documentation, runbooks, and operational readiness are deliverables, not afterthoughts.
A written deployment thesis grounded in the institution's data and operational reality, not its roadmap.
One workflow, end to end, in the production environment, with real users and measured performance from the first day.
Failure modes, fallbacks, cost controls, and audit trails, resolved until the system meets its service-level objectives.
The client's team assumes full operation. Onpar Labs steps back by design, with documentation and pairing complete.
The distance between a working model and a working institution is measured in data, governance, and operational detail. Onpar Labs exists to close it.
Onpar Labs does not run workshops or produce strategy. It partners with organizations that have already decided frontier AI is core infrastructure, and makes that decision operational.
Every forward deployed engineer has shipped production machine learning systems prior to joining. Median field tenure: nine years.
Deployments span every frontier provider and the leading open-weight models. Recommendations are made in writing, with evidence.
The first artifact of any engagement is a benchmark of the institution's real task. Every subsequent decision is measured against it.
Nothing leaves the client perimeter. Onpar engineers work inside the client's cloud, identity, and key management, and have cleared security review at banks, hospitals, and defense primes.
Engagements are priced per outcome with written exit criteria. The firm is structurally aligned to make itself unnecessary.
A retrieval-grounded agent replaced a legacy rules engine, validated against 40,000 historical claims. Live across three regions in nine weeks.
Three engineers embedded within the credit desk delivered a decisioning system under model-risk governance, with full lineage. Passed audit on first review.
A language-model planner over a legacy transport system, with a deterministic optimizer in the loop. Human override rate below two percent by week eleven.
A system that is not handling live traffic under real constraints has not yet been built.
Failure lives in the data. Diagnosis begins there.
Every claim is backed by an evaluation the client can rerun independently.
The system an on-call engineer can debug at three in the morning is the system that remains deployed.
Integration, permissions, edge cases, and institutional process are the work, not the overhead.
Success is the day the client no longer needs Onpar Labs. Every engagement plans for it from the outset.
Every engagement begins with an evaluation of the institution, not a pitch to it. Five questions. If the workflow, the data, and the mandate are real, Onpar Labs responds with a written view on whether it would deploy, and on what terms.
Five questions, then a name and an address. Answers are read by a forward deployed engineer, not a sales team. A written decision follows within five business days. Press Enter to continue.
Name the workflow, the team that runs it, and the cost of a bad day. Onpar Labs deploys against consequences, not use cases.
Doing what, for whom, and under whose governance. “Pilot” and “sandbox” are honest answers and are evaluated as such.
The executive with the budget, and the mandate to put a system into production in twelve weeks. Title is enough.
One number. How it is measured today, and what it must read at week twelve. If it cannot be measured, say so; that is a finding.
Vendors, internal builds, consultancies. What was learned. Onpar Labs does not repeat other people's twelve weeks.
Institution, your name, and your role.
A work address at the institution's domain.
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