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 that actually 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.
Your reps spend 30 minutes on discovery calls that could have been a five-minute tool your prospect completes on their own. Your best qualification logic lives in your two senior SEs and nowhere else. We fix both — pre-sale tools built for your product, licensed under your brand, configured to your catalog. Ready in a week.
The first tool we shipped — built for GPU cloud and bare metal providers. A prospect spends five minutes on their own: workload, infrastructure, buying situation. They get a personalized report — the right GPU for their workload, whether their model fits on a single GPU or needs multi-GPU configuration, a cost comparison against what they're paying today. Your rep gets the fit score and findings before the first call.
GPU Navigator is one. We build these for any product, any sales motion.
Request a vendor walkthrough →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 — sized for 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 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 →
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.