Plug Kernel Code into your repo. It reads everything for a few days, the way a new hire would. After that, ask it to fix a bug or build a feature, and it ships a pull request. Runs on your cloud. Your code never leaves.
Think of it like onboarding a senior engineer. The first few days are reading. After that, you give them work.
One command deploys Kernel into your cloud account. Uses your existing IAM and networking. Nothing phones home.
Kernel reads your repos, pull request history, docs, and tickets. It builds a map of how your systems actually work.
Assign it a Jira ticket. It investigates, plans, writes the code, runs the tests, opens a PR. Your team reviews.
A tool that has read your entire codebase makes fewer wrong calls than a person who joined last week, and it never loses that context. Here's what that gets you.
Most tools guess from the few files in their window. Kernel has read the whole repo, the history, and the past decisions, so its changes match how your code is actually written. The same retry pattern, the same error handling, the convention your team already settled on.
No feeding it context, correcting it, re-explaining the codebase every session. You assign a ticket and come back to a finished pull request. Your engineers spend their time reviewing, not babysitting.
It keeps the entire codebase in context, something no single engineer can do. It works several tickets in parallel, through the night, and every change still arrives as a normal PR your team reviews and merges.
Kernel runs inside your own AWS, Azure, or GCP. Nothing goes to OpenAI, Anthropic, or any outside service. Your source, your customer data, your architecture never leave your perimeter. The thing that usually blocks AI adoption in regulated teams just isn't a problem here.
BENCHMARKS FROM EARLY DESIGN PARTNER ENGAGEMENTS · MEASURED OVER A 90-DAY WINDOW
Kernel reads it once. Then it just remembers. That's the whole architectural trick.
The expensive part is understanding your codebase. Kernel does that during the onboarding phase. Every task after that uses the map, not the territory.
Routing, planning, edits, and lookups run on a small model fine-tuned on your conventions. The expensive frontier model only fires when the task actually needs it.
Point Kernel at Bedrock, Azure OpenAI, your own GPUs, or any endpoint. You use your existing volume discounts. No middleman markup on tokens.
Plans, embeddings, and tool outputs are cached against the code graph. Running another task in the same area of the codebase is nearly free.
Based on internal benchmarks across a 2M-line Python and Go codebase. Frontier model fires on roughly 12% of tasks.
We're working with a handful of teams to start. Leave your work email and we'll reach out.
We'll reach out from hello@kernelcode.com within 48 hours.