Agentic Engineering for Production
Buying with confidence is hard when the technology is moving faster than the buyer can evaluate it. Shipping AI is hard when the gap between pilot and production is wider than it looks. We work the full stack, from pre-sale intelligence tools that close the discovery gap, to production AI systems and infrastructure built to run.
Where we fit
What sits in that gap is engineering work — scoping the right system, getting the spec right, making the rollout actually run. That's where we engage.
You bring the relationships. We bring the engineering.
What we build
Structured assessment tools for the buying moment.
Enterprise sales processes are built to help sellers map solutions to buyer needs. But the buyer often walks away uncertain: were all my constraints factored in? How did that recommendation come about?
Soterra builds the other side of that motion. Our tools let buyers work through those dimensions themselves, either alongside a rep or on their own, and arrive at a sized infrastructure blueprint, a data readiness score, a hardware fit verdict they can stand behind. The first meeting goes deeper, moving past discovery toward a solution decision. The seller gets a more informed buyer; the buyer gets a recommendation they are confident about.
See our products →Custom agentic systems for the operating moment.
Our agentic systems are built on a single principle: deterministic code owns the decisions, LLMs own the prose. The LLM is used only for what it is reliable at today — narrative synthesis, classification within a constrained schema, summaries over structured input. ReAct loops and LLM-as-router patterns didn't meet the reliability bar for the systems we ship now; that may change as models do.
See our systems →For public reference data — cloud GPU pricing and MLPerf inference benchmarks — see Anvil →
Published thinking
No hype, no thought-leadership posturing. The failures, the fixes, and what the architecture actually looks like after it survives contact with a real environment.
GPU · Infrastructure · TCO
$/hr is the last number to calculate. VRAM fit, quantization impact, multi-node threshold, egress cost, and real TCO, in that order.
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RAG · FSI · Production
Four bugs surfaced while hardening a RAG system for FSI. The fixes reveal what separates a working RAG from one that survives audit.
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Agents · Architecture
Reliability and auditability are the two arguments. Which one holds up when the next model release ships?
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MCP · LangGraph · KYC
MCP was designed as an LLM-to-tool protocol. The tradeoffs of using it as a service layer between a LangGraph graph and integration servers.
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About
Soterra Labs exists to put AI to work on real problems: faster decisions, better economics, and leaner operations.
Our approach is rooted in three decades of production engineering, forged through cycles of extreme scale and complexity. We've been on both sides of the table: building systems as engineering leadership at Lehman Brothers and JPMorgan Chase, and selling them as Field CTOs at Dell. That dual perspective shapes everything we build — we know which questions buyers actually ask, and which answers hold up under scrutiny.
Today our focus is the modern AI stack: foundation models, generative architecture, and production deployment on real hardware.
Ready to move From GPU to Revenue™? Reach out directly. Every engagement starts with a conversation. Tell us what you're working on.
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