Unlock your team's AI innovation
Hyper optimise context for cost and control
AI coding unlocks innovation like never before. But more AI usage means more cost and chaos. LemonCrow hyper optimises agent context for cost and control, without losing accuracy or security. Maximising the innovation you get out of your AI spend.
🇨🇭 Developed in Switzerland
How it works
How 30-65% cost savings and 25% increase in speed are achieved
Context, turns, and control. Get those right and your engineers finish more real work per session, at a lower cost.
Only select the right input
Every extra file, log, or dead-end search sits in the conversation and gets paid for again. LemonCrow only feeds the agent what the task actually needs.
Minimising turns
Most delay isn't one big prompt, it's the agent going back and forth, re-reading what it already saw. Fewer turns means tasks actually finish, faster.
Insights in what's working
Leaders need to know AI usage is turning into shipped work, not just trust that it is. That visibility sits across every team and repo, so scaling AI is a decision you can back up, not a leap of faith.
For Enterprise
Built for teams, not just developers
Everything in the free version, plus what a company actually needs to run this safely at scale.
Usage & outcomes dashboard
Exact output, usage, and cost, broken down by model, developer, and project.
See it below ↓No repo size limits
Index and search your biggest monorepos with no caps on symbols or files.
Shared team context
One code graph and memory layer for the whole team, every agent starts from the same understanding of your codebase.
Role-based permissions
Control exactly who can read, write, or manage shared memory and project context.
Governance policies + audit export
Set the rules once. Export a clean record any time you need to show how AI usage is being managed.
Retention controls + SSO
Data lifecycle and login both follow your company's existing policies, not a separate standard.
Your code never leaves your machines
Parsing, indexing, and retrieval all run on-device, on your own hardware. There's no new destination for your source and no proxying of model traffic. The same model and provider your agents already use, nothing new to route through or trust.
Visibility, not guesswork
See what your AI investment is actually producing
Tasks finished, turns per task, time saved, and what it cost to get there. LemonCrow already tracks this per developer, locally, with LemonCrow session stats. Enterprise puts it on one dashboard for the whole org, by model, by developer, by project.
Cost without outcome is just a number. Alongside spend, the dashboard shows what it produced: tasks completed, turns per task, and time saved. The same metrics from the benchmark proof below, now running on your own repos.
The proof
The measured impact of LemonCrow
Same model, same tasks, same limits, only then with LemonCrow added. Here's what happens:
tasks resolved
vs 80.8% baseline
more tasks completed
same model
fewer turns
for the same work
faster
start to finish
Tested on real, large codebases — a full rebuild of a 1.24M-symbol repository indexes in about 3 min, and stays fast to search after that.
Read the full methodology and raw results →Ready when you are
See it work on your own codebase
No sales deck first. We run it against your repos and show you the numbers — same as the benchmarks above, but yours.
Measured on your repositories, against your own baseline. No commitment to start.