Hire AI Native Nearshore Engineers
We embed senior, AI-native engineers into your team to build, deploy, and ship production AI faster than you can hire one. Vendor-neutral. SOC 2. Aligned to your time zone.
Embedded Engineers who Own the Outcome
A forward-deployed engineer works in your repos, sits in your standups, participates in your customer calls, and owns the path from idea to production.
Forward-deployed AI engineering applies that to the AI stack: agents, RAG, LLM workflows, evals, and the integrations that take a demo to production. We staff it as a pod.
An AI augmented senior team who embed into your team to build, deploy, and operate.
Build
AI-native engineers fluent in the current stack ship your roadmap: agents, RAG, LLM features, fine-tuning, and the internal tooling your product needs.
Deploy
Forward-deployed engineers integrate with your systems of record and your customers' environments, taking pilots to production that sticks.
Operate
We build and manage scalable cloud environments for ML and GenAI workloads. Our team creates automated training and inference pipelines that optimize GPU utilization, cost, and performance.
AI-Native and AI-Augmented Development
Our forward deployed engineers carry two traits you benefit from. One lets us build your AI more intelligently. The other lets us build it faster.
AI Native
Our engineers know the modern AI stack. They are fluent in agents, RAG, LLMs, fine-tuning, and evals, so they build production AI applications, not just the software around them.
AI Augmented
Our engineers build with AI every day, coding with Claude Code and Codex, so a small pod ships like a much larger team. A daily audit of the whole codebase keeps that speed from costing you quality.
We've been shipping production code our founding in San Francisco
Production AI since 2016 for Twitter, Facebook and Discovery Channel. We built ML solutions and LLM reliant systems before most firms had a strategy.
- Enterprise-safe SOC 2 certified. Your code stays in your repositories from day one, with clean IP assignment and NDAs by default.
- Vendor-neutral We work across OpenAI, Anthropic, and open-weight models. Valkyrie, our production layer primitive, makes one REST call to any model
- Embed in days A pre-vetted senior bench means a pod starts fast, and our Bench Strength protocol keeps a backup engineer who already knows your app in reserve.
Our forward deployed engineers build production AI every day for our clients and use our primitives to accelerate time to market. That's the difference between a team that's used AI and one that ships it.
Juan Pablo Lorandi
CTO, Azumo · 25+ years of software architecture experience.
Certified Claude Architect
Ship faster without letting quality drift.
A fast-moving pod ships a lot of code. To keep quality from slipping, we run an automated audit across the full codebase on day one, then every day after. The scan surfaces security, cost, and architecture risks before they reach production.
Baseline audit
Full review when we take on the codebase
Regression scan
The codebase is re-checked as new work ships.
Graded findings
Every issue includes severity, file, and line number.
Security
Auth gaps, exposed secrets, missing rate limits, injection risks, and permissive CORS.
Cost Control
API calls without timeouts, retry storms, uncached tokens, inefficient cloud usage, and work that should run in parallel.
Maintain Architecture
God classes, dynamic types in critical paths, missing error boundaries, and debt-prone patterns.
Already in Production
The model is not theoretical. Azumo teams have shipped AI, data, and software work for enterprise customers, high-growth companies, and production teams that cannot afford stalled delivery.
300+
Embedded Pods with real customer outcomes
Each pod is embedded with the client and shipping in production right now, across financial services, advisory, and energy.
Brierstone
Financial Services
Building a portfolio management platform from the ground up for portfolio managers.
Syntrove
Risk & technology
Rebuilt a legacy application with Claude Code in 5 weeks. The original took years to build.
NGL
Energy Infrastructure
Building a real-time digital twin that models a multi-billion-dollar network of physical infrastructure.
Start with a pod. Scale when you're ready.
Most teams start with a small forward-deployed pod and grow it as the roadmap proves out. You pick the model, and scale up or down without renegotiation drama. Pricing depends on scope and seniority; tell us what you're building and we will come back with a clear proposal.
Embedded AI Pod
A senior, AI-native team that owns build, deploy, and operate alongside yours.
AI Staff Augmentation
Add vetted AI engineers to an existing team to close a specific skills gap.
Full Project Build
We scope, build, and ship the system end to end, then hand it over.