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zynoptes.

Stop developing in the dark.

Zynoptes is the AI Performance Engineer for Robotics, finding the critical optimizations that live between nodes.

scans the whole autonomy stack at oncecross-node reasoning · trace depth 7every optimization ships its evidence chaincontrol-loop, memory & hot-path passesthe intelligence is in the harnessscans the whole autonomy stack at oncecross-node reasoning · trace depth 7every optimization ships its evidence chaincontrol-loop, memory & hot-path passesthe intelligence is in the harness
The blind spot

The slowdown isn't in a node. It's between them.

01

Two files. Each one passes review.

A fusion node and a cloud-pool helper, three modules apart. Read alone, both are textbook-clean.

perception/fusion_node.cpp
✓fusion_node.cpp · clean
1void FusionNode::on_scan(const CloudMsg &msg) {
2 for (auto &cluster : clusters_) {
3 Cloud *ref = pool_get_cloud(pool_, cluster);
4 for (int d = 0; d < 16; d++)
5 score_ += match_cost(cluster, ref, d);
6 pool_put_cloud(ref);
7 }
8 publish(score_);
9}
perception/cloud_pool.cpp
✓cloud_pool.cpp · clean
1Cloud *pool_get_cloud(CloudPool *p, int idx) {
2 Cloud *f = &p->slots[idx & SLOT_MASK];
3 if (f->gen != p->gen) {
4 f = refresh_slot(p, idx);
5 }
6 /* defensive copy, “almost never” taken */
7 f->refcount++;
8 copy_cloud(&f->cache, f->raw, CLOUD_BYTES);
9}
critical · hidden memcpy on the hot path
zynoptes optimization #001critical

Full cloud copy in the scan callback

pathpool_get_cloud → copy_cloud → per scan
zero-copybroken on every call
files2 files · perception/ · trace depth 4
Impact: a full point-cloud copy per scan. The fusion node's hottest loop lives in memcpy. Evidence chain attached.
The harness

Four passes. Zero guesswork.

01 INGEST02 LABEL03 SEGMENT04 ANALYZErepo →drivers/perception/control/planning/fleet/ctx 01✓ctx 02✓ctx 03✓ctx 04✓ctx 05✓VERIFY

Ingest. The whole stack, not a sample. Every file, node, and topic lands in a structured graph model: dependencies, callers, data flow.

Why now

Every fleet is asked for more autonomy on the same compute. Deep optimization used to need a frontier lab. We built the harness that gets it from public models: whole-repo reasoning, focused context, optimizations that arrive verified. The intelligence is in the harness.

What it finds

Optimizations that need the whole system.

The latency you only catch by reading everything.

optimizations — live queue
CRITICAL2 files · perception/ · trace depth 4
fusion_nodeon_scan()cloud_poolcopy_cloud()zero-copybroken ✕a full cloud copy honored on every scanthe fusion loop lives in memcpy

Hidden memcpy in the cloud pipeline

A pool helper defensively copies a full point cloud inside the per-scan callback. The fusion node's hottest loop spends its life in memcpy.

evidencepool_get_cloud → copy_cloud → per scan · full cloud copy
Pricing

Connect a repo. Priced to fit it.

Every engagement analyzes your whole stack and backs every optimization with evidence.

Every codebase

Let's talk

Quoted from repo size, complexity, and usage — no rate card, no procurement cycle.

  • Whole-stack optimization workflow
  • Control-loop, memory & hot-path passes
  • Every optimization ships its evidence chain
  • Continuous re-analysis on merge
  • First results in hours
  • VPC, on-prem & air-gapped available

Monthly or annual · cancel anytime

FAQ

Fair questions.

Profilers show you hot symbols one run at a time; linters pattern-match one file at a time. Zynoptes builds a graph model of your entire stack, nodes, topics, and call graphs included, and has frontier models reason across it, surfacing the cross-file, algorithmic inefficiencies that no per-file rule or single profile can express. Most teams run both.

Turn the
lights on.

See what's been slowing your robot down all along.

founders@zynoptes.com