Technology vendors lose deals because prospects can't buy with confidence. Businesses with AI ambitions stall because the right system never ships. We work the full stack — from pre-sale intelligence tools that close your discovery gap, to production AI systems and infrastructure built to run.
We don't consult. We build. Whether the gap is in your sales motion, your internal workflows, or your infrastructure — we deliver working systems, not recommendations. Three engagements. One standard.
Most people don't have 30 minutes for a discovery call. They have five
minutes over coffee. Meet them there. A domain-aware engine that understands
the product category — no calendar, no vendor in the room, no pressure.
They get a report with findings they can bring into any vendor conversation.
The right context, before anyone gets on a call.
Not a questionnaire. Built around the domain — the logic, the tradeoffs,
the real questions that determine fit.
Vendors deploy these for their prospects. Buyers use them to walk into
any vendor conversation already knowing where they stand.
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.
We design and build AI that handles real work — document processing, intelligent search, automated workflows, and multi-agent applications. You don't need an internal AI team. You need a system that works, delivered with everything your people need to run it.
GPU procurement is the easy part. Getting models running reliably on the hardware is where most teams get stuck. We design and deploy the full AI stack — inference serving, model deployment, orchestration, and monitoring — designed around the workload you actually have.
Not values. Engineering constraints — derived from watching what happens when they're violated at scale.
Every engagement delivers with tests, documentation, and artifacts your team can run without us. We don't build proof-of-concepts that stall after the demo. We build systems that are owned by you from day one.
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. A practitioner's account of building production AI systems — 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, workflows that actually change. Not demos. Not pilots that never ship.
For ten years I was the engineering manager at three major banks — responsible for finding, evaluating, implementing, and handing off whatever the business needed across the full technology stack. The category changed every time. The job was the same.
The first hurdle was always discovery. Gathering requirements from internal stakeholders who each had different priorities, translating that into technical criteria, then finding — out of all the options — which few were worth a POC. I did all of that with Excel sheets, mental notes, and post-its. Manual work every time.
Then I moved to the sell side — Field CTO, Executive Advisor — trying to reach people exactly like I used to be. I understood the problem from that direction too. Not frustration. Just a clear view of something that had never been automated, from someone who'd been on both ends of it.
Three decades of production work — software development and infrastructure always running together: cloud stacks, AI systems, data center operations, at Bell Labs, through the 2008 collapse at Lehman, at JPMC, at Dell. The last couple of years concentrated in AI: foundation models, GenAI application architecture, production deployment on real hardware.
That domain depth is the foundation. AI is the layer on top. GTM Intelligence for technology vendors who need a pre-sale edge. AI Systems for businesses ready to automate real workflows. AI Infrastructure for teams who need models running reliably on hardware they've already procured.
I don't hand off to an engineering team. I am the engineering team — and now that engineering runs on AI.
— Srikanth Samudrla, Founder & CTO
Need a pre-sale tool for your sales team? Building AI for a real workflow? Getting models running on infrastructure you've already procured? Reach out directly — every engagement starts with a conversation, not a sales process.