Current work
A portfolio of applied AI programs—built to move from technical insight to measurable systems
Today, our primary build is Agentic SQA. We also evaluate additional program areas that may advance into future prototypes.
Program Spotlight:
Agentic sqa
Stage: Active Build
Status: beta workflow development
Overview: Applied autonomy for software quality assurance: improve verification coverage, prioritize high-risk changes and drive measurable quality outcomes through evidence-based validation under real-world engineering guardrails.
Problem Statement: Software quality assurance in complex systems is still too manual, slow, and reactive. Teams face growing codebases, fragmented toolchains, noisy signals, and expensive test cycles, making it difficult to identify which changes are truly risky, run the right validation efficiently, and produce reliable evidence for release decisions.
Example Use Case: Change-Aware Verification Orchestration
Analyzes code changes and system context to prioritize the most relevant validation workflows, reducing unnecessary test execution and triage effort
Supports targeted escalation for higher-risk changes, helping teams focus engineering time where failures are most likely
exploration areas
RLOps
Stage: Exploration
Status: architecture and feasibility evaluation
Overview: Applied autonomy for operational systems: improve signal handling, support constrained actions and drive measurable performance outcomes under real-world guardrails.
Problem Statement: Reinforcement learning is powerful but too complex, expensive, and inaccessible for most businesses. It requires domain-specific engineering experts, costly training and comes with black-box decision-making, making real-world adaptation difficult.
Example Use Case: Model Optimization
Automates hyperparameter tuning, reward engineering, and experiment tracking, reducing iteration time
Enables real-time model monitoring and automated retraining, ensuring RL policies stay optimized
Automotive software verification
Stage: Exploration
Status: problem definition and external validation
Overview: 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.
Problem Statement: Automotive software validation is expensive, slow, and highly dependent on specialist knowledge across multiple layers of the stack. Teams must manage complex interactions between hardware, drivers, system services, configurations and vehicle interfaces, making it difficult to detect environment-specific regressions early and maintain confidence as systems evolve.
Example Use Case 1: Evidence-Based Validation for Release Confidence
Generates structured evidence from targeted validation workflows, helping teams assess release readiness with greater confidence
Supports audit-ready reporting and decision support for complex software updates in tightly controlled environments
program stages
IR Labs evaluates programs at different stages. Some are active builds. Others are exploration theses under evaluation for future prototyping.
We use the same standard across the portfolio: clear problem framing, measurable outcomes and explicit scale-or-retire decisions.
Define the problem, target users, and technical thesis. Evaluate whether the opportunity is specific, important and measurable.
Exploration
Build a production-oriented prototype with instrumentation and evaluation criteria to test feasibility under realistic constraints.
Prototype
Advance into focused development when early evidence supports clear value and a credible path to deployment.
Active build
Scale programs that move meaningful metrics. Re-scope or retire programs that do not meet the bar.
Scale \ retire
what moves a program forward
Programs advance based on evidence, not momentum. Advancement decisions are tied to measurable criteria and a realistic path to production testing.
Problem clarity — the target workflow and pain are specific
Technical feasibility — there is a credible mechanism to solve it
Measurable value — success can be evaluated against real metrics
Operational fit — the path to testing and adoption is realistic
portfolio discipline
Portfolio discipline is part of the model
IR Labs is designed to build and evaluate programs quickly. That includes making explicit scale decisions—and explicit stop decisions—when evidence does not support continued investment.
This is how the studio stays focused: resources move toward programs with real technical and commercial traction.
Interested in Agentic SQA or a future program area?
We’re open to conversations with teams facing real technical and operational pain, especially where evaluation criteria can be defined clearly.