EON anticipates what your agents will do next, so that your agents become reliable, cheaper and faster.
Every run starts from zero.
An agent that finished a task an hour ago will reason through it again now — same steps, same tokens, full price. Across a fleet, most of what you pay for isn’t intelligence. It’s repetition.
Support agents · runs
live
09:14:03
triage_ticket(#4811)
from zero
$0.38
09:14:37
draft_reply(refund-policy)
from zero
$0.52
09:15:02
triage_ticket(#4812)
from zero
$0.38
09:15:29
update_crm(lead-2207)
from zero
$0.31
09:16:11
draft_reply(login-issue)
from zero
$0.52
09:16:48
triage_ticket(#4813)
from zero
$0.38
09:17:20
update_crm(lead-2208)
from zero
$0.31
09:17:55
draft_reply(refund-policy)
from zero
$0.52
09:18:31
triage_ticket(#4814)
from zero
$0.38
3 distinct tasks
billed 9× · full price every time
Hover a run — the same work keeps coming back.
EON remembers.
One layer between your agents and the model. Work your fleet has done before comes back instantly. Only genuinely new work pays for intelligence.
Plug it in. Watch the bill bend.
No rewrites, no new framework. EON sits in front of the models you already run.
01 / 04
Drop in front of the models you already use
Change one line — your API endpoint — and keep everything else. Same models, same frameworks, same outputs. There is nothing to migrate and nothing to rewrite.
OpenAI
Anthropic
LangChain
Bedrock
Integration · one line
client = Client(
base_url="https://api.provider.com/v1"
base_url="https://api.eon.run/v1"
)
same models · same outputsnothing to migrate
02 / 04
Familiar work comes back in seconds, not minutes
When an agent hits a task your fleet has handled before, EON returns the result at a fraction of the time and cost. Genuinely new work passes straight through to the model, untouched.
parse_invoice · two runs
identical output
first time seen
full reasoning
41.0s$0.52
seen before
recognized by EON
1.2s$0.04
latency · −97%cost · −92%
03 / 04
Every run makes the next one cheaper
EON learns the shape of your workload as it runs. The longer your agents operate, the more of their work it recognizes — so your cost per run bends down while your volume grows.
Cost per run · over time
same workload
−83%cost / run
first runrun 10,000
04 / 04
Shortcuts are verified, never assumed
Every reused result is checked against what the model would actually do. If inputs drift or behavior changes, the run falls back to full inference automatically. You never trade correctness for savings.
Verification · continuous
0 silent failures
✓
run 0492
matches live behavior
reused
✓
run 0493
matches live behavior
reused
↻
run 0494
fell back to full inference
input drifted
→
run 0495
full inference
novel task
every reuse checkedfallback · automatic
Savings that compound.
Every agent that runs on EON makes the layer better at spotting work it has seen before. Growth is supposed to inflate your inference bill. Here, it bends it the other way.
Fast is worthless if it’s wrong.
EON only earns its savings when the results hold up. Verified on every run, isolated to your workload, and built on published research.
100%
of reuse verified
Nothing reused on faith.
Every shortcut EON takes is checked against live model behavior. Anything that doesn't match falls back to full inference — automatically, on every run.
✓Checked against live behavior
✓Automatic fallback
✓Zero silent failures
0%
data shared
Your workload stays yours.
What EON learns from your agents serves your agents. Your traces are encrypted, isolated per tenant, and never used to serve anyone else.
✓Encrypted in transit and at rest
✓Per-tenant isolation
✓Zero training egress
20+
patents · papers
Two decades of research.
The core architecture is peer-reviewed and patent-pending — built on published work in collaborative inference and distributed AI at NeurIPS, AAAI, and MLSys.
✓Peer-reviewed foundations
✓Patent pending
✓Validated on live agent data
Two ways in
Don’t take our word for it. Take your own numbers.
For your stack
See your numbers first.
Point EON at an existing workload and get a report of exactly what you’d save in cost and latency — measured on your agents, before you change a thing.
