Now in Beta

Your AI Chief of Staff for Sales

Six AI agents run your pipeline. You run your deals. Zero data entry.

70%of rep time is non-selling tasks
91%of CRM data goes stale in a year
0 hrsmanual entry with Otto
The Morning Briefing
8 AM. Phone rings. Otto.
O
Otto — Morning Briefing
8:00 AM · Thu, Mar 13
Meridian Corp waiting on demo slots. Followed up again.
Alex needs SIP details before tomorrow's meeting.
James Park confirmed for March 26.
$48K PoC kicked off. Invoice sent.
☎

Voice-first

2-minute call. No screen needed.

⚡

Signal-driven

Only what changed. Only what needs action.

✎

Actionable

"Send it." Follow-up goes out. Done.

📚

Full context

Every email, call, and signal — already read.

The Agents
Six agents. Always on.
01

Lead Scoring

Otto scores every lead against your ICP, twin customers, and live engagement signals. Continuously. Not quarterly.
ICP MatchingTwin CustomersReal-Time Signals
→
02

Meeting Prep

Otto builds your briefing before every meeting. Stakeholders, risks, intel, talking points. Delivered 30 min before.
Account ResearchStakeholder MapCompetitive Intel
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03

Post-Call Coach

Otto analyses every conversation. Extracts signals. Logs actions. Coaches you before the next one.
Signal DetectionObjection LoggingTalk Ratio
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04

CRM Updates

Every deal updated after every interaction. Every change justified. Your pipeline stays perfect.
Field UpdatesStage MovesApproval Flows
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05

Insights

Otto surfaces risks, opportunities, and next actions across your entire pipeline. Role-specific. Always learning.
Deal Risk ScoringRole-Based ViewsForecast Intelligence
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06

Deep Research

Living intelligence dossiers on every account in your pipeline. Auto-built. Always current. Already waiting.
FirmographicsTech StackNews Monitoring
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Architecture
How Otto thinks.
✦ Otto System Architecture3 layers · 29 signals · 8 actions
Input Layer
Email + Calendar + Calls + Web
Gmail, Outlook, Zoom, Meet, Slack
Ingestion
Real-time
Zero manual entry
Context Engine
Otto's Memory
Contacts, deals, threads, transcripts, signals, scores
Signal Engine
29 signals
Email, meeting, deal, contact
Action Engine
8 actions
Draft, send, task, book, close, move, update, create
AI Policy
Plain English rules
Versioned, auditable
Human Workbench
Review + Approve
Corrections train the system
Scoring
Deal health
Updated per interaction
Output Layer
Morning Call + Drafts + Briefs + Tasks + Alerts
Voice, email, Slack, in-app
Learning Loop
Gets smarter
Every correction, every outcome
The Journey
Lead to close. No grind.
1
Lead In
Inbound, outbound, or signal
→
2
Research
Dossier auto-built
→
3
Score + Prep
Scored, briefing ready
→
4
You Sell
Walk in prepared. Close.
→
5
Otto Does Rest
Coach, update, next actions
Under the Hood
Three layers of control.
⚙

AI Policy Layer

Rules in plain English
Versioned, full audit trail
Role-based permissions
◈

Stateful CRM Core

Events trigger updates, not clicks
Every change justified
Velocity, decay, momentum
☑

Human Workbench

Updates held for sign-off
Overrides train the system
Otto recommends. You decide.
The Premise
CRM serves managers. Otto serves you.
Traditional CRM
Rep types data after every call
Pipeline reviewed weekly, always stale
Research done ad-hoc before meetings
Coaching from self-reported outcomes
Built for reporting, not selling
Otto
CRM updates itself from every interaction
Pipeline current in real-time, always
Briefing delivered before every call
Coaching from actual conversations
Built for the person selling
The system that works for you.
CRMs asked: how do we get reps to update?
Otto asks: why should they have to?
No credit card. First morning briefing is tomorrow.