Your team adopted AI.
The bottleneck didn't move.
Until now.

Superhuman.
Power tools for your product.

Superhuman supercharges your team — handles the boring, structures the work, automates the mundane, leaving you in charge of taste and judgement. Same team. 10× the work.

For VP Product, CPO, and Heads of Product running 8+ concurrent product cycles.

Now in private beta · Enterprise-grade · Per cycle, not per seat

A
PM Agent
Atlas
Sparring partner & living knowledge repository
Active
S
Research Agent
Sage
Continuous discovery & insight synthesis
Active
P
Design Agent
Pixel
Experience strategy & design systems
Active
F
Engineering Agent
Forge
Technical feasibility & spec translation
Active
P
Marketing Agent
Pulse
Launch strategy & market positioning
Active
Built by product leaders from
Meta Miro McKinsey Booking.com
What's actually broken

Product was built for a slower world.

Cursor writes the code in a day. Briefing the work still takes three weeks. Meetings still consume the calendar. Decisions still get re-litigated. The "AI transformation" landed in your stack — not in your cycle.

Problem 01

The bottleneck moved from code to spec

Your engineers use Cursor and Copilot. They ship features in days. Your PMs still take three weeks to write the brief. Your designers still wait two weeks for the handoff. The bottleneck didn't disappear when AI arrived — it migrated. From build to spec. From engineering to product.

3 weeks
To brief a feature your engineers could ship in 3 days
Problem 02

Translation is the job

A PM's day is rephrasing research for design, design for engineering, engineering for marketing. Every handoff means a meeting, loses context, adds a week. The thinking they were hired for is the 10% that survives.

90%
Of a PM's day is translation, not judgement
Problem 03

AI in the IDE — not in the org chart

Every engineer got a 10× tool. The PM got a meeting summariser. The designer got better stock photos. Half the product function still runs on the workflow of 2019, while engineering ships at AI speed. The cycle gets jammed exactly where humans hand off.

1 of 5
Product functions actually augmented by AI
Problem 04

Decisions get re-litigated, every cycle

Why was self-serve killed in Q3? The PM who decided left. The Slack thread is buried. The Notion page is unread. Every new cycle relearns what the last one already knew — at the cost of months.

Months
To resurface a decision your org has already made
The agents

Five agents. One system.

Not five tools. One system. Agents share context, hand off work, and challenge each other's outputs — across the full product cycle.

A
Product Strategy
Atlas
Remembers every decision your org has ever made.
Ask why self-serve onboarding was killed in Q3. Atlas pulls the churn data and the Slack thread in four seconds.
S
User Research
Sage
Runs discovery the week you need it.
Personas, interview synthesis, competitive signal — without scheduling a research sprint or waiting on the quarterly cycle.
P
Design Intelligence
Pixel
Turns strategy into Figma your designers can ship.
Component-level handoff specs that match your design system, not generic frames. Fluent in your tokens, your patterns, your conventions.
F
Engineering Bridge
Forge
Turns design into engineering tickets your team can pick up.
Converts Pixel's frames into user stories, dependency maps, and effort estimates — codebase-aware, sprint-sized, routed straight to Linear or Jira.
P
Product Marketing
Pulse
Writes the launch the week of the build.
Messaging, analyst briefings, sales enablement — drafted, reviewed, ready the week the product ships, not the week after.

The critical differentiator: agents brief each other, challenge each other's outputs, and hand work off without you in the middle.

How It Works

From brief to shipped in days.

You define the goal. The agents do the work. Human sign-off at every key decision gate — without the alignment overhead that buries your team in meetings.

01
Initiation
You define the goal
Describe a product initiative in plain language. Atlas parses intent, activates the right agents, and injects your org's strategy, OKRs, and history into context.
02
Orchestration
Agents brief each other
Atlas writes the PRD. Sage runs discovery. Pixel reviews for UX feasibility. Forge flags scope and complexity — automatically, in parallel, before a meeting is scheduled.
03
Execution
Parallel workstreams
Wireframes, architecture docs, data models, go-to-market briefs — all running in sync. Work that used to take months of alignment overhead happens in days.
04
Human Gate
You review & ship
You stay in control at key decision gates. High-judgment humans direct the system. The agents handle the weight. You ship with confidence, not chaos.
10×
Faster to first prototype vs. traditional product cycles
1 : 8
One orchestrator operating with the output of a full product team
Concept demos · One product cycle, end to end

One cycle. Five agents.

Here's how the SMB self-serve onboarding initiative actually flows through the system — from Atlas opening the brief, through discovery, design, engineering, and launch — in one continuous cycle.

Stage 01 · Strategy & Memory

Atlas writes the brief with everything your org already knows.

When you describe the SMB self-serve initiative, Atlas pulls the Q3 decision log, the churn data, and three years of relevant Slack — then writes a brief grounded in everything your org has already learned. The institutional memory that stays when people leave.

"Decisions don't get re-litigated — Atlas remembers why."
🧠
Org Lexicon
A shared vocabulary for your org, champion-reviewed before any work begins.
📡
Org Radar
Always-on duplicate-work detection. Knows who's building what, where.
🔬
Scientific Method
Validate, experiment, recommend, monitor — and track whether assumptions held.
🔒
Shadow Mode
Mandatory parallel operation before live deployment. Confidence gap surfaced.
Atlas · Product Strategy Agent
Ingested 847 docs · 12 OKR cycles · 3 yrs Slack READY
Org Radar · 0 overlaps detected MONITORING
We're thinking of building a self-serve onboarding flow for SMB customers. Is this worth exploring?
Three things your org already knows that are relevant here:

1. Q3 2024 decision log — you de-prioritised self-serve after churn data showed SMB churned at 3× the rate of mid-market. The context: onboarding quality was the primary variable.

2. Sage discovery (Oct): 74% of churned SMB customers never completed 3 key setup steps. That problem hasn't been addressed.

3. Forge flagged that the auth service doesn't yet support guest sessions — a dependency for any self-serve flow.

Recommend: before committing, run a 2-week experiment with guided in-app onboarding. I can write the brief.
↳ Confidence: High · Sources cited: 6
Stage 02 · Discovery

Sage validates with users in hours, not quarters.

Atlas hands off the SMB self-serve brief. Sage runs a synthetic persona session against a 12-user cohort, surfacing pain points and drop-off patterns — without scheduling a research sprint or waiting on a recruiter.

"What used to be a quarterly research cycle now happens before standup."
Sage · User Research Agent
Cohort: Synthetic SMB Ops Manager · n=12 READY
47 pain points · 3 themes synthesised LIVE
MR
Maya R. · Synthetic Persona
SMB Ops Manager · 8-person team · Mid-tier SaaS stack
Walk me through your first hour with a new tool.
I tried for 90 minutes then closed the tab. Setup had 14 steps and I didn't know what half of them meant. The CRM step needed engineering.
Where exactly did you drop off?
Step 7 — connecting our CRM. I just gave up.
↳ Insight: 74% of cohort drops at CRM integration · Confidence: High
Stage 03 · Design · Handoff

Pixel turns research into Figma your team can ship.

The handoff in action: Sage's findings arrive in Pixel as structured insights. Pixel maps each pain point to component logic and pushes wireframes directly to your Figma file — fluent in your tokens, your patterns, your conventions.

"Designers receive specs ready to ship, not starting points to argue over."
Pixel · Design Intelligence Agent
3 insights received from Sage · 1 min ago HANDOFF
Linked to acme.figma · 247 components SYNCED
From Sage
  • Setup overwhelming (14 steps)
  • CRM integration unclear
  • Needs engineering help
Pixel reasoning

Compress to 4 progressive steps. Replace CRM step with guided wizard. Make technical setup optional.

Output
SMB Onboarding v3
3 frames · 12 reused · 2 new
↳ Pushed to acme.figma/file/abc123 · Forge notified
Stage 04 · Engineering

Forge turns design into shippable tickets.

Forge receives Pixel's frames and converts them into engineering artifacts — user stories, dependency maps, effort estimates, and technical specs. Reads your codebase to size each story accurately and surface cross-team dependencies. Pushed straight to Linear or Jira.

"Engineering opens the sprint with sprint-sized tickets — not a PRD to re-read."
Forge · Engineering Bridge Agent
3 frames received from Pixel · 1 min ago HANDOFF
Linked to acme.linear.app · /team/eng SYNCED
12
Stories
3
Epics
14d
Est. effort
ENG-247 Replace 14-step setup with 4 progressive steps 3 SP onboarding-api
ENG-248 Build guided CRM connection wizard 5 SP onboarding-api
ENG-250 Magic-link auth for guest sessions 4 SP auth-service
Cross-team dep surfaced: ENG-250 (magic-link auth) requires auth-service work. Routed to platform team, scoped to 4 SP.
↳ 12 tickets pushed to Linear · 1 cross-team dep routed
Stage 05 · Launch

Pulse drafts every launch artifact.

As the cycle nears ship, Pulse generates launch artifacts — positioning brief, sales enablement, analyst briefing, customer email — directly into your Google Drive. Your PMM reviews and approves instead of writing from scratch.

"The launch is drafted the week of the build, not the week after."
Pulse · Product Marketing Agent
drive.acme.com · linked to /Launch folder CONNECTED
Cycle stage: Pre-launch · T-14 days DRAFTING
G
SMB Onboarding v3 — Launch Brief
Positioning
Key Messages
Target Personas
Launch brief
Published · 2m ago
Sales enablement
Pending PMM review
Analyst briefing
Drafting · 40%
Customer email
Queued
↳ 4 artifacts in Drive · 2 awaiting human sign-off
FAQ

Common questions.

Is this replacing our PMs?+
No. Superhuman Systems is designed around the principle that high-judgment humans remain essential — as Orchestrators directing the system, not as execution bottlenecks. The first phase of deployment (Shadow Mode) explicitly runs agents in parallel with your existing team, with zero job announcements. The shift happens through natural attrition and strategic non-backfill, not layoffs.
How long does onboarding take?+
Atlas begins ingesting your org's documents — PRDs, OKRs, strategy docs, Slack, org charts — on day one. The Shadow Mode protocol requires three structured parallel tests before any agent goes live. Most teams complete this in 4–6 weeks. Your internal champion reviews Atlas's first output (the Org Lexicon) before any work reaches other stakeholders.
What is a "product cycle" for billing purposes?+
A product cycle is defined as an initiative running from idea through post-launch monitoring — typically covering discovery, definition, design, engineering handoff, launch, and the first 30 days of signal collection. Maintenance cycles and minor iterations are included in your tier's cycle count. Volume discounts apply as you scale.
What data does Atlas ingest — and is it secure?+
Atlas ingests strategy documents, OKRs, past PRDs, meeting notes, Slack conversations, and org charts. All data is processed with enterprise-grade security controls. Enterprise tier includes SSO, custom data residency, and a dedicated security review. No customer data is used to train shared models.
Do we need to use all five agents?+
Atlas is the mandatory starting point — it's the institutional memory layer every other agent depends on. The others (Sage, Pixel, Forge, Pulse) activate based on what a given cycle requires. You don't pay to unlock them; they're all included. The system activates what's needed and idles what isn't.
How is this different from using Claude or GPT-4 directly?+
General LLMs are powerful but context-agnostic. They don't know your org's history, your OKRs, your engineering constraints, or the decisions you've already made and abandoned. Superhuman Systems is purpose-built for the product function — with domain-specific agents that share context with each other, built-in PM methodology, and an institutional memory layer that gets smarter the longer you use it.

Ship a product cycle without the meetings.

The product function that used to take a floor of people can run leaner — higher output, faster cycles, and institutional memory that doesn't walk out the door.

Enterprise-grade · Per product cycle · Deployment in 4–6 weeks