AI-native engineers · Build
AI-native engineers who build production AI
Engineers fluent in agents, RAG, evals, and the current AI stack, embedded with your team to ship your AI roadmap in days, not the months a hire takes.
What they build
Production AI, end to end
Engineers fluent in the current AI stack, building with LLMs, agents, and the modern toolchain every day.
AI agents & agentic workflows
Tool-using agents and multi-agent systems, with the guardrails and traces to run them in production.
RAG & retrieval
Retrieval pipelines over your docs and data, tuned for relevance, latency, and cost.
LLM workflows & features
Product surfaces and back-end workflows built on LLMs, wired into your app and your data.
Integrations
Connections to your systems of record, third-party APIs, and customer environments.
Internal tools
The agents and copilots that take repetitive work off your team's plate.
Reliability & guardrails
The guardrails, tests, and traces that keep production agents dependable, built and owned by your team.
One execution system
Build, deploy, and validate
Plank is one embedded team across three modes, the one you’re on is highlighted. Pull in the others as a project needs them.
AI-native product engineering
Engineers who build your AI product, agents, RAG, and LLM features.
Who it’s for
When your roadmap is bigger than your capacity
For founders, CTOs, and engineering leaders at AI companies whose AI features, agents, and integrations are stuck behind limited engineering capacity, and who want trusted, high-context engineers who can contribute from week one.
Trained for production AI
Formed through the Plank AI Accelerator
Every Plank engineer is developed through the Plank AI Accelerator, hands-on training in agents, RAG, customer deployment, and modern AI tooling, then embeds with pod-mates they have already shipped with.
Inside the Plank AI AcceleratorHow it works
From scoping call to shipping in days
01
Scope
A short call to map the work, your stack, and where you're blocked. NDAs and IP assignment in place before any code.
02
Embed
Your engineer joins your repos, standups, and tools the same day, reads the codebase, and starts shipping small PRs against main.
03
Ship
Production AI in days, with evals your team owns and QA on every release. Scale the pod up or down as the roadmap moves.
Common questions
What is an AI-native engineer?
An AI-native engineer is a software engineer fluent in modern AI development, agents, LLM workflows, RAG systems, evals, and AI product features, who builds production AI rather than only conventional software.
Can AI-native engineers work with our existing team?
Yes. Plank engineers embed in your repositories, standups, and tools and ship alongside your team as additive capacity, not a black-box project with a handoff.
What AI products can Plank build?
Agents and agentic workflows, RAG systems, LLM features and integrations, voice and multimodal AI, internal tools, and the eval harnesses that keep them reliable.
How fast can an AI-native engineer start?
The same day. After a short scoping call, your engineer embeds, reads the codebase, joins standups, and ships to production within days, not the months a hire takes.
Is this an alternative to hiring full-time AI engineers?
Yes. Hiring AI-native engineers typically takes two to four months. Plank gives you embedded, high-context engineers in days, with no permanent headcount risk, while you keep hiring at your own pace.
Are Plank engineers vendor-neutral?
Yes. Plank works across OpenAI, Anthropic, and open models, so your architecture stays yours.
Add AI-native engineering capacity this week
Tell us what you’re building. We’ll embed engineers who can ship it.