The operating system for the post-AI business era
Four native desktop apps — one shared brain. Chat, meetings, agents and product surfaces fuse into a single workflow the way Slack + Zoom + Notion + a stack of vertical SaaS should have — but never did.
The problem
Knowledge work is fragmented across ~15 tools per employee.
Enterprise AI copilots (ChatGPT, Copilot, Gemini) are bolted on top — they don't know your customers, your repo, your fleet, or your papers. Chat is one silo, meetings another, product data a third. The "AI" sees none of it.
- • Context switching burns 40% of engineering time (McKinsey 2025).
- • Enterprise AI has 6% adoption among ICs — because it doesn't know their work.
- • Chat platforms don't own the product surface; product surfaces don't own the chat.
The solution
One native app per vertical. One agent identity per app. One shared context ledger.
A user in Enterprise can decide something with Ada; Devon in Develop recalls that decision the next day. Every message, meeting minute, and call transcript is auto-analysed into decisions / actions / insights and piped to the right product feature.
- • Same runtime under every app · same messenger · same calls · same E2E.
- • Different persona + tools per vertical — because a robotics agent is not a CRM agent.
- • Live now. Not a demo. Backend deployed. Every tab tested end-to-end.
Four apps · shared runtime · per-vertical depth
One shell. Four verticals. Four agents.
Midcore Enterprise
Ada · ops copilot
Products: Close · Mind · Genome · Loom · Council · Babel · Conscience · LedgerEye · Tracks · Pivot · Levy · Vault Banking + 39-agent Automations gallery
Replaces: Salesforce · HubSpot · Notion · DocuSign · Workday
Midcore Develop
Devon · staff-engineer copilot
Products: Vibe Coding · Automations (22 dev agents) · Studio · Loom · Tracks · Brain · Mind (Journal)
Replaces: Cursor · GitHub Copilot · Linear · Sentry · Datadog
Midcore Research
Iris · senior-scientist copilot
Products: 60-paper research corpus · real Welch t-tests + random-effects meta · Automations (20 research agents) · Brain
Replaces: Zotero · ChatGPT Deep Research · Overleaf · arXiv + Semantic Scholar workflows
Midcore Robotics
Rex · fleet-ops copilot
Products: Robotics (fleet · missions · telemetry · safety · sim · kitting) · Designer (URDF→GLB) · Automations (20 robotics agents) · Brain
Replaces: Foxglove · MoveIt/Gazebo · Isaac Sim + fleet tools
Shared across all four
The Communication Hub
One rail slot per app. Agent pinned first. Inter-user threads below. Real A/V. Real E2E. Real analyzer. Real pipeline into product features.
Unified Communication Hub
One rail slot per app. Agent pinned first, human threads below.
Real A/V via LiveKit
Room-join · mic/cam/screen-share · remote video grid · recording.
Agent-in-thread
Auto-extracts intent · entities · decisions · from every message + call transcript.
RRULE meetings + .ics export
Real recurrence rules · one-click add to Outlook/GCal.
E2E encryption
AES-256-GCM today · MLS-ready envelope. Retention + legal hold enforced server-side.
Inline previews · AV rejection
Image · video · audio · PDF preview via Blob URLs · EICAR rejection at upload.
Slash commands + mentions
/schedule /pin /agent /mute · @mention parsing · draft persistence per thread.
FTS5 bilingual search
Full-text search across every thread · unicode-aware · fast.
Why it wins
Five defensible moves
Cross-app context ledger
Ada's decisions, Devon's PR notes, Iris's paper summaries, Rex's incidents in one ledger per user. Every agent recalls every sibling's work. No competitor owns four verticals under one shell.
Real product actions
260+ typed, safety-gated manifest actions (click / fill / navigate / run workflow / open form). Each agent does work inside its own product — not just generic chat.
Enterprise-grade from day one
E2E encryption · retention policies · legal hold · audit ledger · per-tenant isolation. Not bolt-on. Enterprise-auditable by default.
Native desktop, not a web wrapper
Real WebSocket messaging · real LiveKit A/V · real file access · offline resilience · OS notifications. Beats every browser-based competitor on latency and offline behaviour.
One codebase, four verticals
Every foundational feature is shared (chat, calls, agents, minutes, retention). New verticals ship by adding a manifest — not by rebuilding a stack.
Traction · as of Jul 2026
Not slides. Working software.
Native apps built end-to-end
Packages in one pnpm workspace
Foundational products shipped
Chat + hub + phase-exit tests · all green
Messenger backend on Docker · healthchecked
Agents with distinct personas + capabilities
Messenger build ladder shipped
End-to-end encryption + retention + legal hold
Recent shipping timeline
- 2026-07-09 — Communication Hub merged with per-product agents. Ada / Devon / Iris / Rex live. Cross-app context ledger shipped. Docker backend deployed + verified end-to-end with real users.
- 2026-07-08 — Enterprise Automations gallery (39 agents), Develop gallery (22), Research gallery (20), Robotics gallery (20). Real procedures against fixtures. Cinematic UX across four apps.
- Q2 2026 — Native shell rewritten: Electron + React + TS · 150-package pnpm workspace · CI green.
Market
Big enough to matter. Focused enough to win.
Global SaaS TAM · Gartner 2026
Enterprise chat + collab
Vertical AI copilots
Beachhead
Mid-market software companies (Develop). Replace Cursor + Linear + Slack + Zoom with one app that already knows the repo. High willingness to pay. Fast sales cycles. Land-and-expand into Enterprise.
Adjacent moves
Enterprise ops · research labs · robotics ops. Same runtime, different manifest. Each expansion adds a whole new vertical without rebuilding infra — this is the "operating system" wedge that vertical SaaS can't play.
Business model
Three revenue lines
Anchor ARPU · 4 tiers per app (Free / Builder / Squad / Enterprise).
Overage on agent runs. Aligns cost with model tier + usage.
For regulated verticals. Support + SLA + audit exports included.
The ask
$8–12M
Seed · 18-month runway12-month goals: 25 design partners · 5 paying enterprise pilots · $500k ARR · Develop vertical at product-market fit.
| Bucket | Allocation | What it funds |
|---|---|---|
| Engineering | 55% | 8 senior IC hires · close last 15% of ladder · harden multi-tenant infra |
| Go-to-market | 25% | 3 AE + 2 SE · design-partner motion in Develop and Enterprise |
| Model + infra | 15% | LLM inference · LiveKit media · S3 attachments · on-call rotation |
| Reserve | 5% | Contingency |
Team
Built by the Neurobazar atelier
Founded and built by the practitioners behind Neurobazar — a working atelier of engineers, designers, and researchers shipping real-world AI systems. Full team + portfolio at neurobazar.com/en/atelier.