About S3Nex
We built S3Nex because the conversation about AI in engineering was missing something important
Everyone was talking about models, tools, and prompts. Nobody was talking about whether the organizations adopting them were actually ready to use them well.
AI amplifies what's already there. Strong engineering foundations get dramatically more capable. Weak ones get dramatically more fragile — faster.
S3Nex works at that boundary. We help engineering organizations become structurally ready for AI — not just tooled up for it.
How We Think About the Work
Diagnose before prescribing
We don't arrive with a solution. We arrive with questions. The first thing we do in every engagement is understand the actual system — not the architecture diagram on the wiki, the one running in production. Where knowledge lives in people rather than systems. Where implicit assumptions are load-bearing. Where AI would help and where it would accelerate the wrong things.
Structure before speed
The organizations we've seen get the most from AI aren't the fastest movers. They're the ones that got their foundations right before they accelerated. We help you build those foundations — explicit contracts, clear boundaries, fast feedback loops, defined failure modes — so that when you move fast, you're building the right thing correctly.
Delivery, not documentation
We don't produce strategy decks and hand them over. We stay in the work. Architecture decisions, engineering practices, platform design, team capability — we're present through all of it until the system is working and the team owns it.
What Drives Us
Built on three pillars
Diagnosis-first
We don't sell packages. Every engagement starts with understanding your specific situation — the gaps, the constraints, the things leadership believes vs. what engineers experience. The solution comes from the diagnosis, not the other way around.
AI as a system component
We design systems where AI operates as a genuine component — with defined inputs, explicit contracts, validated outputs, and governance for continuous operation. Not a chat interface bolted onto an existing product.
We work where it's hard
Distributed systems, edge infrastructure, zero-trust architectures, continuous AI operation frameworks. We have deep experience in the problems that require senior engineering judgment, not just implementation skill.
Client Feedback
Before S3Nex, our reporting lived in spreadsheets that three different teams maintained separately. They built a custom BI warehouse tied to the metrics our leadership actually uses — not a generic dashboard template. For the first time, when a number changes, we know why and we know who owns the response.
We needed storage that worked with our HCI stack, not against it. S3Nex designed and built a custom storage layer that fit our existing platform constraints — performance, failover, and operational visibility included. It runs in production today and our ops team owns it.
We didn't want another off-the-shelf DevOps bundle forced onto our workflow. S3Nex stood up a fully dedicated toolstack — CI/CD, infrastructure automation, monitoring — built around how our teams actually ship. No shelfware. Everything has an owner and a purpose.
S3Nex built us a SaaS platform to manage cloud resources across multiple environments — provisioning, cost visibility, access control, all in one place. It replaced a patchwork of console tabs and manual spreadsheets. Our platform team uses it daily.
How We Work
From diagnosis to deployment
Discovery & Diagnosis
We start by understanding the actual state of your engineering organization — not the intended state. We talk to engineers, not just leadership. We map where implicit knowledge lives, where boundaries are weak, where validation is slow. This is the work most engagements skip. We don't.
Architecture & Design
We design for the real system, not an idealized one. Technology choices, data flows, AI component boundaries, failure modes, human oversight points — all defined before implementation starts. The spec is the foundation. Everything else is execution.
Development & Iteration
We build in iterative cycles with visible progress at every stage. You see working software, not status updates. We course-correct early and often — because clarity improves during building, and the architecture should reflect that.
Deployment & Continuous Operation
We don't hand over and disappear. Launch is the beginning of the operational phase — monitoring, validation, drift detection, optimization. For AI systems especially, deployment is where the real engineering starts.
Our Team
Two teams, one delivery engine
A small senior human team directs the work. Behind them runs a full AI operations layer — LLMs on three kinds of compute, feeding sixty specialized agents that do real parallel work on every engagement.
Human Team
Senior engineers who set direction, validate every AI output, and own client relationships. Nothing reaches a client without human sign-off.
Senior Engineering Lead
Architecture, diagnosis, and technical direction across engagements.
AI Systems Architect
Model selection, agent orchestration, and validation frameworks.
Platform Engineer
Cloud infrastructure, deployment pipelines, and continuous operation.
Delivery Lead
Engagement ownership, outcome tracking, and client handover.
AI Operations Layer
Not assistants. A production fleet.
We run roughly 23 LLMs across three compute tiers and 60 agents with defined roles, input contracts, and validated outputs. Agents do parallel work during engagements — research, code drafts, test generation, review — so senior engineers aren't bottlenecked by execution capacity.
- 23 LLM instances across 3 compute tiers
- 60 specialized agents with defined roles
- 3 inference backends: in-house, rented, cloud API
Compute Infrastructure
Where our LLMs run
Three inference backends — each chosen for what it does best. In-house hardware for speed and privacy. Rented GPUs for heavy workloads. Cloud APIs for frontier reasoning on the hardest problems.
In-house GB10s
Local inference on our own hardware
Four NVIDIA GB10 nodes on-premises — each named after a Titan, each running a dedicated open-weight model. Used for fast iteration, codebase analysis, and workloads where data must stay in our environment.
Rented GPUs
Dedicated cloud GPU clusters
Rented GPU instances for heavier inference, batch processing, and fine-tuning. A mix of datacenter and workstation-class cards — spun up on demand when an engagement needs more compute than local hardware provides.
Cloud APIs
Frontier models via API
Opus-class and frontier models accessed through cloud APIs. Reserved for architecture decisions, complex reasoning, and problems where model capability matters more than latency or cost.
Live Orchestration
Ideas in motion — LLMs, agents, results
Each object follows a defined route with a verb that matches the work — scan from GB10 to Research, draft from GPU to Codegen, audit from API to Security — then loops back for refinement before a validated result lands.
Agent Fleet
What our 60 agents actually do
Sixty specialized agents run in parallel during engagements — each with a defined role, explicit inputs, and validated outputs. Below are representative roles from the fleet; not chatbots, but operational tools orchestrated by senior engineers.
Discovery & Research
Scans documentation, codebases, and prior art in parallel before human review.
Distills large findings into actionable briefs for senior engineers.
Maps data structures, API contracts, and system boundaries across a codebase.
Cleans, transforms, and validates datasets before they enter pipelines.
Engineering & Implementation
Produces implementation drafts from specs — always reviewed before merge.
Restructures existing code against defined patterns and architecture rules.
Wires services together, generates adapters, and validates interface contracts.
Plans and executes data or service migrations with rollback checkpoints.
Quality & Validation
Writes tests from acceptance criteria and edge-case definitions.
Runs structured code review against project standards and security rules.
Checks outputs against defined contracts before they reach production.
Measures performance baselines and flags regressions across releases.
Operations & Deployment
Manages release pipelines, rollouts, and environment promotion.
Watches system health, alerts on anomalies, and tracks SLA metrics.
Detects model and config drift in AI systems running in production.
Coordinates other agents, manages task queues, and routes work by priority.
Design, Docs & Security
Drafts API specifications, OpenAPI schemas, and interface documentation.
Generates and maintains technical documentation from the codebase.
Enforces style, formatting, and static analysis rules across the repo.
Scans for vulnerabilities, misconfigurations, and secrets in code and infra.
Humans set direction and validate every output. LLMs on GB10s, rented GPUs, and cloud APIs power the agents that do parallel execution work — so senior judgment scales without being bottlenecked. Learn how we use AI internally →
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