From hypothesis to production

IR Labs is a product studio for applied AI systems. We build fast, instrument early and make decisions from real-world results

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our story

IR Labs is a product studio for applied AI. We exist to close the gap between technical possibility and production reality.

Many labs produce prototypes that never leave the lab. Our model is different: we build measurable systems, evaluate them in real conditions and scale what works.

Our first program is Agentic SQA, but the studio is designed to support a broader portfolio of applied AI programs over time.

How we operate

Frame the problem

We start with a narrow, high-value problem and a clear user or buyer. If the problem is vague, we do not build yet

Build a measurable prototype

We prioritize working systems over polished demos. Every prototype is built with logging, evaluation and operational constraints in mind

Evaluate in real conditions

We test against real workflows, failure modes and operational constraints - not just synthetic success cases

Scale or retire

If it moves a metric, we harden and scale it. If it doesn’t, we document the learning and move on

Current focus: Agentic SQA

Our primary focus today is Agentic SQA: an evidence-first software verification platform that turns code-change and quality signals into prioritized, reviewable risk assessments and developer-ready actions.

We’re starting where the pain is immediate: too much signal, not enough usable signal. The goal is not just detection—it’s faster, higher-quality decisions and less developer toil.

Evidence-backed risk assessment

Prioritize findings with traceable rationale

Context-aware verification workflows

Interpret signals in code and workflow context

Developer-ready outputs

Produce reviewable actions, not opaque model responses

What’s Next

IR Labs is designed as a studio, not a single-product team. In parallel with Agentic SQA, we continue to evaluate additional areas for future research and prototyping.

Reinforcement Learning Ops (RLOps)

Exploration

Automated RL model training, tuning and deployment with foundation models and real-time monitoring. Enables a seamless environment without requiring deep RL experience.

automotive software verification

Exploration

Applied intelligence for complex vehicle software systems: improve validation coverage, prioritize high-risk changes and drive measurable reliability outcomes across tightly coupled hardware/software environments under real-world guardrails.

Not every exploration becomes a product. Programs advance when they show clear evidence and measurable value.

Journal

(Launching soon)

We’ll use the Journal to publish technical notes, build updates and mechanism explainers as programs progress. This is the place to dive deeper and learn more about who we are, what we do and how we think about it.

  • General lab updates

  • How we frame and evaluate programs

  • Technical deep-dives on Agentic SQA mechanisms

  • Build updates

  • Technology trends influencing our direction

Evaluating Agentic SQA?

If you’re looking to improve software quality workflows in complex codebases, we’d like to talk

Want to build systems that ship?

We’re hiring engineers and researchers who want their work to move from concept to production