AI research agents for production machine learning model development
Built on Disarray’s context graph and long-horizon agent harness, our ML agents turn complex proprietary data into production-quality machine learning models, at a fraction of the time and cost of manual development.
Disarray provides an end-to-end solution that helps teams move from high-level modeling goals to evaluated models: defining concrete optimization objectives, discovering relevant data, generating hypotheses, building pipelines, running experiments, and surfacing decisions for human review.
Why ML Models?
The most impactful and differentiated AI use cases, such as clinical prediction, fraud detection, personalized recommendations, are built on proprietary data and task-specific objectives that commodity foundation models cannot handle well. Tackling these complex, custom use cases demands scarce, expensive ML engineers, who are increasingly overwhelmed as organizational data grows in complexity and scale. Massive amounts of institutional knowledge obscure subtle yet crucial relationships, and the resulting context gaps lead to compounding errors: degraded models, failed deployments, wasted cycles.
Disarray empowers developers by eliminating the errors, context gaps, and blind spots that slow down ML teams. This allows them to focus on their core competencies: defining model objectives, applying domain expertise, and making the crucial judgment calls that determine model quality.
Our system is built on decades of research and hard-won lessons from developing production ML systems at scale.
Context is the bottleneck
Years of experience in ML infrastructure, distributed systems, and safety-critical autonomy have consistently revealed the same failure mode: systems break because the data context is unknowable. Inside one organization, core concepts often carry multiple valid definitions depending on lineage and use case. Signals, prior experiments, feature definitions, and business logic are spread across warehouses, pipelines, dashboards, notebooks, and legacy systems. Small semantic inconsistencies snowball into brittle deployments, degraded models, and compliance risk.
Better models with bad context just produce wrong answers faster and more convincingly.
Automation has boundaries
ML engineering requires judgment calls that resist full automation: which definition of an outcome to use, how to interpret missing data, what evaluation trade-offs to accept. Our research found that engineers want to automate repetitive, structured work (data discovery, pipeline construction, iterative experimentation) but keep control over domain-specific, ethical, and contextual decisions and be able to intervene and inspect the process at any point. Since the developer is ultimately responsible for the final model, they need the control and transparency to own and trust the end result.
Institutional knowledge goes to waste
Teams rebuild abandoned features and revisit undocumented approaches. Meanwhile, valuable, proven machine learning techniques are scattered across public forums and private artifacts, undiscoverable and rarely structured for reuse. Instead of leveraging the strongest existing work and best practices to accelerate development, teams start from scratch.
Disruptive technology ≠ disrupted workflow
Machine learning systems are built within complex, long-lived organizations that already rely on established data and ML infrastructure. Warehouses, feature stores, experiment trackers, orchestration frameworks, and monitoring systems encode years of investment, operational knowledge, and organizational constraints. Progress depends on operating within existing workflows and interfaces, allowing new capabilities to compose with the tools and processes teams already trust. The best solutions learn an organization’s conventions and adapt to them by default.
From insights to system
Disarray is built around the insights above: it makes context a core primitive, reuses prior work instead of starting over, fits into existing stacks and deployment constraints, and keeps humans in the loop for high-judgment decisions. At its core are a context graph that unifies internal organizational context (data assets, features, business logic, experiments, dependencies, and lineage) with external best practices, and a long-horizon agent harness that keeps research loops grounded, inspectable, and under human control.
Grounded in this high-fidelity context and governed by the harness, Disarray safely automates the heavy lift across the ML workflow: goal translation, semantic data discovery, intelligent reuse, and iterative experimentation. Teams can run Disarray in fully autonomous mode or delegate specific tasks, with the agent's proposed decisions and action recommendations grounded in the context graph and delivered through transparent handoffs. It integrates with existing warehouses, feature stores, experiment trackers, and orchestration tools, ensuring that every autonomous run leaves behind a structured trace that compounds institutional knowledge over time.
