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.
We don't just consult. We build. Our From GPU to Revenue™ framework bridges the gap in your sales motion, your internal workflows, or your infrastructure. Three engagements. One standard.
Most people don't have 30 minutes with a sales rep for a discovery call. They have five minutes when the calendar clears. They click through a short interactive assessment: a domain-aware engine that understands the product category, with no vendor in the room and no pressure.
The first tool we shipped — built for the GPU infrastructure space. Five minutes: workload, current setup, buying situation. The output is a personalized report — a recommended GPU for their workload, an estimate of whether their model fits on a single GPU or likely needs multi-GPU configuration, a cost estimate relevant to their situation. Findings to inform the conversation, not replace it.
GPU Navigator is one. We build these for any product, any buying decision.
Custom AI for real work — document processing, intelligent search, multi-agent applications, automated pipelines. Scoped to your workflow. Built for your environment.
GPU procurement is one part of the problem. Getting models running reliably on that hardware is another. We deploy the full AI stack — inference serving, model deployment, orchestration, monitoring — built around the workload you actually have.
Not values. Engineering constraints — derived from watching what happens when they're violated at scale.
Every engagement delivers what you need to run the system in your environment — designed and implemented to your specs.
Routing decisions, risk rules, and scoring live in deterministic code — inspectable, testable, auditable. AI does what it's reliable at: synthesizing results and generating narratives. Not making decisions you can't explain.
A retry mechanism only tested on the happy path isn't a retry mechanism. A governance gate that always returns true isn't a gate — it's a comment. Every system is tested against what actually breaks it.
No hype, no thought leadership. The failures, the fixes, and what the architecture actually looks like after it survives contact with a real environment.
$/hr is the last number to calculate. VRAM fit, quantization impact, multi-node threshold, egress cost, and real TCO — in that order.
Four bugs found while hardening a RAG system for FSI — and what they reveal about the gap between a working system and a trustworthy one.
The gap between the demo and production — the reliability argument, the auditability argument, and which one survives model improvements.
Five transport-layer decisions — each driven by a real failure in a KYC onboarding system.
The transport layer is stable. Now the harder questions: who's allowed in, what's happening inside, and how does this hold up when things go wrong in production.
The vendor benchmarks are valid. The procurement question still doesn't have a clean answer — here's the gap and how to close it.
MCP was designed as an LLM-to-tool protocol. The tradeoffs of using it as a service layer between a LangGraph orchestrator and integration servers.
Most professional knowledge lives in people's heads. Here's what it looks like when you structure it as an agentic system — personas, tools, skills, rules, and memory.
All posts at Practical AI Builder →
Soterra Labs exists to put AI to work on real problems: faster decisions, better economics, and workflows that actually change.
The firm's approach is rooted in three decades of production engineering, forged through cycles of extreme scale and volatility — from software built to configure and manage networks in real time at Bell Labs, to the software systems running high-stakes financial operations of Lehman Brothers and JPMorgan Chase, to architecting and implementing cloud stacks for FSI clients in regulated environments at Dell.
Having navigated the complexities of the buy side as engineering leadership and the sell side as Field CTOs, Soterra Labs was built to bridge the gap between technical potential and operational reality.
While our history is in complex cloud stacks and data center operations, our current focus is the modern AI stack — foundation models, generative architecture, and production deployment on real hardware. Soterra Labs does not hand off to an outside engineering team; we are the engineering team.
Need a pre-sale tool for your sales team? Building AI for a real workflow? Getting models running on infrastructure you've already procured? Ready to move From GPU to Revenue™? Reach out directly — every engagement starts with a conversation, not a sales process.