Proactive intelligence
GeniOS watches your context continuously. When something matters, a deal going cold, a follow-up overdue, a risk emerging, it surfaces before anyone asks.
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Agents don't need more memory. They need intelligence.
GeniOS reasons over the tools, memory, and agents you already run, then tells your agents what to do and tells you what matters, before anyone asks.
intelligence core · live _
Trusted by builders from
Works with most tools
Gmail
Notion
Google Calendar
Google Drive
Linear
Jira
Asana
HubSpot
Intercom
GitHub
GitLab
Figma
Airtable
Dropbox
Gmail
Notion
Google Calendar
Google Drive
Linear
Jira
Asana
HubSpot
Intercom
GitHub
GitLab
Figma
Airtable
Dropbox
The problem
Your agents remember everything. You still do the thinking.
Context and memory are solved. Deciding what matters, catching what slips, and acting before you would, is still on you.
Without GeniOS With GeniOS
Your agent sends every prospect the same generic follow-up.
It tells the agent what to lead with for this buyer, and what to avoid.
Up to 10x more repliesYou find out a deal was slipping once it is already lost.
It reads the signals, predicts the deal is cooling, and flags it early.
Caught 2–3 weeks earlyYour agent is about to break your own pricing rule to close faster.
It catches the wrong move, tells you why, and suggests a better one.
Costly mistakes stopped before they shipThe investor update goes out the day you finally remember it.
It knows what actually moved and has it ready on the 15th.
100% on time, never missed*Illustrative examples
Capabilities
What GeniOS actually does.
Three things your agents can't do on their own. GeniOS does them in the background, before you ask.
Agent intelligence
Before your agent acts, GeniOS gives it context: what to say, how to say it, what to avoid. It reasons from your company's actual patterns, not a generic prompt.
Decision intelligence
When an agent is about to do something risky, the wrong discount, an irreversible action, a policy violation, GeniOS flags it, with the reasoning attached.
60–80% lower token spend on recurring workloads
4–5× fewer LLM calls per agent workflow
50% better results than doing it all manually
*Estimates, pre-benchmark
Book a call Early access · Analyse your use cases
How you integrate
Drop GeniOS between your data and your agents.
GeniOS sits as a thin intelligence layer over the tools and agents you already run. You don't rebuild your stack. You add intelligence on top.
See it work
GeniOS thinks. Your agent acts.
Memory and an LLM act only on what you spell out for them. GeniOS supplies the intelligence to get it right, without the hand-holding.
You → Hermes Agent
Draft the follow-up to Priya at Neon Capital after the demo.
Memory returns the raw facts
Supermemory · Gmail · HubSpot · Granola transcripts
- Granola, demo call: Priya said "the SSO/SOC2 stuff I assume you have. What I care about is reps not missing follow-ups."
- HubSpot: Neon Capital, stage Demo Done, last contact 18 days ago
- Gmail: an earlier reply to Priya from March (full text in store)
Memory + LLM → Agent your current stack
Memory + GeniOS → Agent the intelligence layer
A smart LLM reasons
Reads the transcript, leads with her real pain, keeps security brief. A solid, competent draft.
The catch: You have to hand it the angle every time. Get the brief wrong and it sends a generic note that gets ignored.
GeniOS adds what no LLM has
- Your red line: No greeting + recap on Demo-Done deals, a rule you set after the last Neon email died.
- Sales psych: She called security table-stakes, so drop it to zero, not just soften it.
- Your history: Your proof-first 3-line openers reply at 38% vs 11% for greeting + recap.
Drafted
Hi Priya, great speaking last week. Follow-up tracking is exactly our focus, SSO and SOC2 handled. 20 mins next week?
Drafted
Priya, you said reps keep missing follow-ups. Here is a 90-second clip of exactly that catch. Thursday 2pm or Friday 11am?
Result
Opened once, then ignored
Result
auditable Reply rate ~11% → 38%
from your own sends
You → Chief of Staff Agent
Write the renewal check-in to Orbit Logistics, keep it positive.
Memory returns the raw facts
Supermemory · Salesforce · Slack · Granola
- Salesforce: Orbit, renews June 27, $84k ARR. Owner field changed May 29
- Shared Slack: last Orbit message May 26, 4 messages the prior 11 days
- Granola QBR: one line tying warehouse automation to renewal justification
Memory + LLM → Agent your current stack
Memory + GeniOS → Agent the intelligence layer
A smart LLM reasons
Notices the new owner and the warehouse use case in the QBR. Writes a smart check-in to the right person.
The catch: You have to spot the risk yourself and tell it. Brief it to keep things positive and it walks the account into churn.
GeniOS adds what no LLM has
- At-risk 0.78: Owner change inside the renewal window carries ~3x the churn weight of silence alone.
- Learned pattern: This signature lost Veho and Tideport at renewal week. Flagging now buys ~17 days.
- Your red line: After Veho: silence in the renewal window means ask the deciding question, not a warm note.
Drafted
Hi, ahead of your June 27 renewal, how is the warehouse automation rollout landing? Anything we can unblock?
Drafted
2 of 5 sites are live. Are sites 3 to 5 still on for Q3, yes or no? (CSM-led, the play that went 3 of 3.)
Result
Reads healthy. It is not.
Result
auditable Flagged ~17 days early
$84k ARR · risk 0.78
You → OpenClaw
Summarise this week's feedback so I can pick what to build next sprint.
Memory returns the raw facts
Supermemory · Slack · Gmail · Granola · Linear · Salesforce
- Vertex: "export times out on the full quarter" · Halo: "CSV for the whole pipeline?" · Kestrel: "I screenshot the dashboard, can't get data out"
- Slack: Muralist asked again about scheduled reports · Gmail: 2 requests for dark mode
- Linear GEN-412 "bulk/scheduled export", status Backlog, opened Feb by an engineer
Memory + LLM → Agent your current stack
Memory + GeniOS → Agent the intelligence layer
A smart LLM reasons
Clusters the export asks into one job, down-ranks dark mode by revenue, even spots ticket GEN-412. A sharp digest.
The catch: You have to tell it what actually matters. Get the brief wrong and you ship the wrong thing.
GeniOS adds what no LLM has
- Learned cluster: From a past loss, a CSV ask, a timeout, and a screenshot are one job, which is why Kestrel is in.
- Ticket intent: GEN-412 is an internal cron, it will not unblock these customers. Pulling it forward looks done.
- Churn pattern: Same ask 3+ times churned 5 of your last 6. Muralist just hit 3, renews in 47 days.
Drafted
One theme: bulk export (Vertex, Halo, Kestrel). Dark mode is cosmetic. Maps to GEN-412, pull it forward.
Drafted
Re-scope the export job, not GEN-412, and brief the AE before Muralist's renewal.
Result
Ships the wrong fix
Result
auditable $140k renewal protected
$140k ARR · renews in 47 days
*Illustrative examples
Three modes
Reactive. Proactive. Predictive.
Three modes of one intelligence, built for your agents, not your inbox. It answers when you ask, surfaces what you'd miss, and stops the wrong move before it ships.
Reactive
Answers when you ask.
You asked
Draft the monthly update for our investors.
GeniOS reasons
Your investor tracks CAC payback, and your rule is no ARR before finance signs off. So the agent leads with payback and holds ARR.
$250K follow-on committed
Proactive
Acts before you ask.
Nobody asked
Mid-market win rate dropped to 12% this month.
GeniOS reasons
Your last three losses named the same rival's pricing, and your rule is reposition, not discount. So the agent reworks outreach to match.
Win rate 12% → 21%
Predictive
Predicts before you act.
Agent about to act
Agent about to offer Vega a 25% discount.
GeniOS reasons
Your rule caps discounts at 15%, and your own deals show 25%+ saves renew 31% vs 78%. So the agent drops it and runs the re-onboard play.
$90k renewed, no discount
*Illustrative examples
Core features
Built to compound, not just respond.
Not a tool you call. A layer that watches, explains, learns, and already knows your domain.
Proactive by default
It pushes what matters before you ask. Not a query tool you call, a layer that watches.
Nothing falls through
Explainable every time
Every recommendation carries its derivation: which facts, which rules, what confidence.
You always know why
Learns from corrections
Every time you override it, it updates, and the next decision is sharper. It compounds.
Less babysitting over time
Domain expertise built in
Deep, domain-specific expertise across sales, revenue, and product, not a blank model.
Sharp from day one
The outcomes
What changes when your agents have intelligence.
Illustrative outcomes across sales, revenue, and product. Brutal and specific, on purpose.
Sales
Reply rate went from 2% to 9%. The agent did it, you didn't touch it.
Revenue
At-risk renewals caught weeks before quarter end. No surprises.
Product
Shipped what the at-risk accounts needed, not the loudest request.
Ops
Follow-up miss rate: zero. Commitments tracked on their own.
Founder
Supervision dropped from 2 hours a day to 15 minutes. Same output.
Policy
The wrong discount blocked before it sent. Deal closed on better terms.
Agents
Your agent stopped asking what to write. GeniOS already told it.
Trust
Every action came with its reasoning. You saw exactly why.
Sales
Reply rate went from 2% to 9%. The agent did it, you didn't touch it.
Revenue
At-risk renewals caught weeks before quarter end. No surprises.
Product
Shipped what the at-risk accounts needed, not the loudest request.
Ops
Follow-up miss rate: zero. Commitments tracked on their own.
Founder
Supervision dropped from 2 hours a day to 15 minutes. Same output.
Policy
The wrong discount blocked before it sent. Deal closed on better terms.
Agents
Your agent stopped asking what to write. GeniOS already told it.
Trust
Every action came with its reasoning. You saw exactly why.
Trust
Every action came with its reasoning. You saw exactly why.
Agents
Your agent stopped asking what to write. GeniOS already told it.
Policy
The wrong discount blocked before it sent. Deal closed on better terms.
Founder
Supervision dropped from 2 hours a day to 15 minutes. Same output.
Ops
Follow-up miss rate: zero. Commitments tracked on their own.
Product
Shipped what the at-risk accounts needed, not the loudest request.
Revenue
At-risk renewals caught weeks before quarter end. No surprises.
Sales
Reply rate went from 2% to 9%. The agent did it, you didn't touch it.
Trust
Every action came with its reasoning. You saw exactly why.
Agents
Your agent stopped asking what to write. GeniOS already told it.
Policy
The wrong discount blocked before it sent. Deal closed on better terms.
Founder
Supervision dropped from 2 hours a day to 15 minutes. Same output.
Ops
Follow-up miss rate: zero. Commitments tracked on their own.
Product
Shipped what the at-risk accounts needed, not the loudest request.
Revenue
At-risk renewals caught weeks before quarter end. No surprises.
Sales
Reply rate went from 2% to 9%. The agent did it, you didn't touch it.
Get started Your agents already execute.
Your agents already execute.
Give them intelligence.
Take your agents from guessing to real intelligence.
20-minute call · no credit card · real applications
Emerging category the intelligence layer for AI agents YC · Company Brain "the single intelligence layer that does not exist yet" a16z "system of record → system of intelligence" a16z "a foundation model is not, by itself, a GTM application" YC · Tom Blomfield "every company is going to need one" The open lane proactive, not reactive Emerging category the intelligence layer for AI agents YC · Company Brain "the single intelligence layer that does not exist yet" a16z "system of record → system of intelligence" a16z "a foundation model is not, by itself, a GTM application" YC · Tom Blomfield "every company is going to need one" The open lane proactive, not reactive