The hardest problem in AI isn't AI
We've watched this industry pour billions into models, training runs, and inference chips. And the models are extraordinary. But here's what we kept seeing in the companies we worked at, the teams we led, the systems we built: the AI was ready. The data wasn't.
Data engineers spend eighty percent of their time on plumbing. ML teams wait weeks for clean features because the data engineering queue is backed up. And now every company wants to deploy dozens of AI agents querying live data across their systems—and there's no infrastructure to keep those agents grounded and trustworthy.
This isn't a tooling problem. It's an architecture problem. The systems we inherited were built for a world of batch reports and human queries. That world is gone. And patching it with another connector, another catalog, another monitoring dashboard is not going to get us where we need to go.
Infrastructure should be as intelligent as the models it serves
Think about the absurdity of the current state. We build systems capable of reasoning, generating, and planning. Then we feed them data through pipelines that break if a column name changes. Pipelines that run on schedules set by humans. Quality checks defined by hand. Schemas documented in wikis that nobody updates.
We're building infrastructure that learns. Systems that observe your data environment, infer schemas, adapt to upstream changes, detect anomalies, and self-correct—without a human writing a YAML file or a Slack alert. Not automation as you know it. Genuine autonomy, where engineers define outcomes and the system figures out how to get there.
This is the shift from imperative to declarative data infrastructure. It's the same transition that transformed application deployment (Kubernetes), cloud provisioning (Terraform), and CI/CD (GitHub Actions). Data operations is the last holdout. We intend to change that.
The five-tool stack is a failure mode, not a feature
The typical enterprise data team runs Fivetran for ingestion, dbt for transformation, Great Expectations for quality, Monte Carlo for observability, Atlan or Alation for cataloging, and Airflow for orchestration. Six tools. Six vendors. Six billing cycles. And the integration layer between them? That's your data engineers, manually stitching it all together.
We refuse to build the seventh tool. We're building the one system that replaces the need for five of them. A single, integrated infrastructure layer that spans from data discovery through governance, quality, and delivery—purpose-built for AI workloads. Feature computation. Continuous training. Agent orchestration. All of it.
AI agents are the new consumers, and they're unforgiving
Human analysts can look at a dashboard, spot something off, and dig in. They have context and judgment. AI agents don't. They query, they consume, they act—at machine speed, with no patience and no benefit of the doubt. If the data is stale, the agent hallucinates. If access controls are misconfigured, it leaks sensitive information. If lineage is broken, there is no audit trail for its decisions.
OpenAI had to build an entire internal system—metadata graphs, access controls, evaluation loops—just to make one data agent work for their own employees. They built air traffic control for AI. Most companies can't do that. We're building so they don't have to.
Our systems treat agents as first-class data consumers. Real-time access control. Query-time quality guarantees. Decision-chain lineage. Operational monitoring that evaluates not just whether data arrived, but whether it was used correctly. This isn't a feature we bolted on. It's the premise we started from.
Open source is non-negotiable for foundational infrastructure
Infrastructure this consequential cannot be a black box. Our core technology is open source. We publish benchmarks. We share architectural decisions openly. We engage directly with the practitioner community, because foundational infrastructure benefits from scrutiny, adversarial testing, and contributions from the people who actually use it in production.
The data infrastructure ecosystem has been dominated by closed platforms charging six figures for capabilities that should be table stakes. We think that model is running out of time. The future of data infrastructure looks more like Kubernetes and Postgres than like another proprietary vendor lock-in play.
The Sciencer Company started with a frustration that became a conviction. Our founder spent a decade in software development and platform engineering, watching elite business units get held back by the world's most inefficient infrastructure: data. Even a single machine learning task that affects business decisions could take weeks or months, not hours or minutes.
After years of working with—and around—the AI and ML solutions offered by Confluent, Databricks, and Snowflake, the frustration crystallized into a thesis: these platforms were designed for a different era, and incremental improvement would not close the gap. The only path forward was to build intelligence infrastructure from the ground up, in harmony with learning algorithms rather than in spite of them.
That conviction was strong enough to leave a comfortable executive role and begin innovating and engineering in stealth. A network of industry experts—former colleagues, friends, builders—joined as technical and growth advisers.
Come build with us
We are a small, focused team working on a hard problem. We are looking for engineers, practitioners, and builders who share the conviction that data infrastructure needs to be rebuilt—not patched—for the age of AI. If you've felt this frustration in your own work, you already know why this matters.