The Sciencer

Company

The bottleneck is below the model

The limiting factor in applied AI is rarely the algorithm. It is the infrastructure that feeds it.

For a decade, the industry has invested heavily in model architectures, training techniques, and inference optimization. These advances are real. But in production—where AI must operate on live data, at scale, under governance constraints—the bottleneck has shifted. Data infrastructure, not model capability, determines whether AI delivers value or remains a proof of concept.

Most enterprise data systems were designed for an earlier era: batch processing, human-authored queries, periodic reporting. They assume a world where data moves slowly, schemas are stable, and consumers are dashboards. That world no longer exists. Modern AI workloads—feature computation, continuous training, multi-agent orchestration—demand infrastructure that can discover, prepare, and govern data autonomously, at machine speed.

The result is a widening gap between what AI can do in theory and what organizations can achieve in practice. Closing that gap requires more than incremental improvement to existing systems. It requires rethinking data infrastructure from first principles.

Infrastructure should learn, not just execute

A data system designed for AI should exhibit the same property AI demands of data: adaptability.

Traditional data infrastructure is deterministic by design. Pipelines run on schedules. Schemas are manually defined. Quality rules are hand-coded. When data changes—and it always changes—human engineers intervene. This creates a dependency that scales linearly: more data sources, more workloads, more manual effort.

We believe infrastructure should be adaptive. Systems that serve learning algorithms should themselves be capable of learning—inferring schemas from observation, adjusting pipelines to upstream changes, detecting and resolving anomalies without human escalation. This is not automation in the conventional sense. It is a shift from imperative infrastructure, where engineers specify every behavior, to declarative infrastructure, where engineers specify outcomes and the system determines how to achieve them.

This principle guides everything we build. Our systems observe data environments, reason about dependencies, and act to maintain reliability and correctness. The goal is not to eliminate human judgment but to reserve it for decisions that genuinely require it.

Applied AI needs its own infrastructure class

The tools built for analytics and reporting cannot serve the operational demands of production AI.

The data infrastructure landscape is not short on products. It is short on products designed for how AI actually consumes data. Feature stores address one fragment. Vector databases address another. Orchestration tools handle scheduling but not governance. Observability platforms detect failures but cannot prevent them. The typical enterprise assembles five to eight point solutions into a fragile stack that requires constant attention.

We take a different approach. Rather than building another point solution or assembling capabilities through acquisition, we are engineering an integrated infrastructure layer purpose-built for AI workloads—from initial data discovery through governance, quality assurance, and delivery to models and agents. A single system that spans the full operational lifecycle of data in an AI context.

This is a difficult engineering problem. It requires building storage engines optimized for AI access patterns, orchestration systems that adapt to changing workloads, and governance frameworks that operate at machine speed. We are willing to do this work because the alternative—expecting data teams to manually integrate an ever-growing collection of specialized tools—does not scale.

The agent era demands operational infrastructure

Autonomous AI agents will not succeed on infrastructure designed for human-driven workflows.

The emergence of AI agents—systems that query data, make decisions, and take actions with limited human oversight—represents a fundamental shift in how infrastructure is consumed. Agents do not wait for scheduled pipeline runs. They do not submit tickets requesting data access. They query across domains, in real time, and expect governed, trustworthy responses.

This creates infrastructure requirements that no existing platform fully addresses: real-time access control that adapts to agent context, quality guarantees that operate at query time rather than batch intervals, lineage tracking that follows data through agent decision chains, and operational monitoring that can evaluate not just whether data arrived but whether it was used correctly.

We are building for this reality. Not by retrofitting existing systems with agent-facing APIs, but by designing infrastructure where autonomous agents are first-class consumers alongside human analysts, machine learning pipelines, and traditional applications.

We build in the open

The infrastructure layer of AI is too consequential to develop behind closed doors.

Our core technology is open source. We publish benchmarks, share architectural decisions, and engage with the practitioner community directly. This reflects a conviction that foundational infrastructure benefits from open scrutiny, community contribution, and the kind of rigorous testing that only broad adoption provides.

We are scientists, engineers, and builders who have spent a decade working in and around platform engineering, distributed systems, and applied AI—at companies where data infrastructure was both mission-critical and insufficient. We are supported by a network of technical and growth advisers from organizations including Meta, Uber, Grab, Nike, Microsoft, Cisco, Gojek Tokopedia Group, HelloFresh Group, who share our view that the infrastructure problem is both urgent and tractable.

Work with us

We are building the data infrastructure layer for the age of applied AI. If this work interests you — as a technologist, an engineer, a potential collaborator, or someone who believes the current approach is not working — we would like to hear from you.