The data infrastructure crisis is real, and it's getting worse
The gap between AI ambition and data readiness is the defining infrastructure challenge of the decade.
The observation that motivated this company is not abstract. Over a decade in platform engineering and software development, we watched the same pattern repeat: teams with extraordinary AI capabilities, held back by data infrastructure that was never designed to serve them. Feature computation that should take minutes takes weeks. Governance that should be automatic requires dedicated headcount. Pipeline failures that should self-resolve require 3 a.m. pages.
The numbers bear this out. Data teams spend roughly eighty percent of their time on infrastructure maintenance rather than analysis. AI project failure rates now exceed seventy percent, and the primary cause is not model quality but data architecture. Meanwhile, the demand for data engineers is growing at roughly fifty percent per year, far outpacing the supply of qualified practitioners. The human-intensive approach to data operations has reached its limit.
We founded The Sciencer Company because we believe incremental improvements to existing platforms—Confluent, Databricks, Snowflake—will not close this gap. These platforms were designed for an earlier era of data consumption. What's needed is infrastructure built from the ground up for how AI actually works: continuously, autonomously, and at machine speed.
From imperative to autonomous infrastructure
Data systems built for learning algorithms should themselves be capable of learning.
Traditional data infrastructure operates imperatively. Engineers write pipeline definitions, specify schemas, configure quality checks, and set scheduling parameters. When conditions change—a new data source, a schema migration, an upstream failure—engineers intervene manually. This model creates a linear dependency between infrastructure complexity and human effort.
We are pursuing a fundamentally different model: autonomous data infrastructure. Systems that observe data environments, infer structure, adapt to change, and maintain correctness without continuous human intervention. The analogy is precise: the same shift from imperative to declarative that transformed application deployment (Kubernetes), cloud provisioning (Terraform), and continuous integration (GitHub Actions) is overdue for data operations.
The goal is not to replace data engineers. It is to redirect their expertise from manual maintenance to strategic work: designing data products, defining governance policies, and architecting systems that create competitive advantage. Infrastructure should handle the plumbing. Humans should handle the decisions.
One platform, not six
The fragmented data stack is an accidental architecture, not an intentional one. It is time for consolidation at the infrastructure layer.
The typical enterprise data team operates five to eight specialized tools: ingestion, transformation, quality, observability, cataloging, orchestration, and governance. Each tool addresses a real need. But the integration burden falls entirely on the data team, creating a fragile, expensive, and manually-maintained meta-infrastructure that is itself the primary source of operational risk.
We take a different approach. Rather than building another specialized tool or assembling capabilities through acquisition, we are engineering a single, integrated platform that spans the full data operations lifecycle: discovery, ingestion, transformation, quality assurance, governance, and delivery—purpose-built for AI workloads including feature computation, continuous training, retrieval-augmented generation, and agent orchestration.
This approach carries engineering risk. Building an integrated platform is harder than building a point solution. We accept that trade-off because the market evidence is clear: organizations cannot sustain the current approach. The Fivetran-dbt Labs merger, Databricks' accelerating acquisition pace, and Snowflake's platform expansion all point toward the same conclusion. Consolidation is happening. The question is whether it will be assembled from parts or designed as a whole.
Infrastructure for the agent era
The rise of autonomous AI agents introduces infrastructure requirements that no existing platform fully addresses.
When a human analyst encounters suspect data, they investigate. When an AI agent encounters suspect data, it acts on it. This difference is not incremental—it's categorical. Agents query data across domains, at machine speed, with no tolerance for latency, staleness, or access control failures. A misconfigured permission doesn't result in a support ticket; it results in a data breach. Stale data doesn't cause a delayed report; it causes hallucinated decisions propagated through an automated chain.
Gartner predicts that over forty percent of agentic AI projects will be canceled by the end of 2027, primarily due to inadequate operational infrastructure. We believe this is correct, and we believe it represents a generational infrastructure opportunity. The companies that build reliable agentic AI will be those that have solved the data infrastructure problem underneath it.
Our platform treats AI agents as first-class data consumers. This means real-time access control that adapts to agent context, quality guarantees enforced at query time rather than batch intervals, lineage tracking through agent decision chains, and operational monitoring that evaluates not just data delivery but data usage correctness.
Any Lab is the product expression of these convictions. It automates the data infrastructure that companies need before AI actually works—for data teams, ML pipelines, and AI agents. Teams plug it in, and their data operations run themselves.
The platform autonomously discovers data sources, infers schemas, constructs and maintains pipelines, and handles drift and failure recovery across federated environments. One platform. Every data workload.
Built in the open, informed by experience
Foundational infrastructure benefits from open scrutiny. Our conviction comes from lived experience.
Our core technology is open source. We publish benchmarks, share architectural decisions, and develop in public. This is not an ideological position; it is a practical one. Infrastructure that organizations build their AI operations on must be auditable, extensible, and free from vendor lock-in. Open source provides all three.
The Sciencer Company was founded on a specific frustration: that after a decade in software development and platform engineering—working with and around Confluent, Databricks, and Snowflake—the AI and ML solutions offered by mainstream data infrastructure providers were insufficient for modern workloads. That frustration was strong enough to leave a comfortable executive role and start building from first principles.
We are supported by a network of technical and growth advisers from organizations where data infrastructure is mission-critical, who share our view that the current approach is inadequate and that the problem is tractable.
Work with us
We are building the data infrastructure layer for the age of applied AI. Whether you are a potential customer evaluating how to future-proof your data operations, a practitioner interested in autonomous systems, or an engineer who has felt this problem firsthand—we would welcome a conversation.