122 integrations. 10+ expert agents. Six client surfaces.
A single operator layer with durable context, governed workflows, and policy-aligned execution.
The average knowledge worker context-switches between 9 apps per day. Every switch costs 23 minutes of refocus time. That's 3.5 hours lost daily.
ChatGPT, Claude, Gemini — powerful alone, but using them for real work means prompt engineering, copy-paste, and no memory. Every conversation starts from zero.
General-purpose assistants rarely ingest your project topology, data classifications, or internal workflows as first-class state; users repeatedly restate constraints, which increases latency and error rates in recurring tasks.
Sensitive data goes to third-party APIs. Enterprises, healthcare, legal — they need AI that runs locally, on their terms.
Intelligent agents that plan, write, analyze, code, and automate — with persistent memory, 122 integrations, visual workflows, and full privacy control.
10+ specialized modules engage in structured debate, verification, and synthesis—reducing single-model overconfidence relative to monolithic chat interfaces.
Run fully offline with Ollama and MLX, or route approved cloud models; on-device execution keeps sensitive payloads off third-party inference where policy requires it. Data residency and provider selection follow customer policy.
Slack, GitHub, Notion, Salesforce, Stripe, Google Sheets, and 116 more — connected and automatable from day one.
10+ expert modules — math, coding, planning, risk, search, vision. Agents debate and verify. Persistent memory across all conversations.
Visual n8n-style workflow builder. Chain AI agents + integrations into automated pipelines. HTTP, code, webhooks, Slack, GitHub, CRM — all connected.
Kanban board with AI execution. Create tasks, move through stages, run the same multi-expert pipeline — now with tracked progress and history.
Real-time camera + voice AI agent. Analyze what you see, get voice responses. TTS with OpenAI, ElevenLabs, and browser built-in.
AGI Dev — a multi-panel macOS development environment with multi-agent coding, autocomplete, quick edit, Git integration, and terminal. MLX on Apple Silicon for on-device inference where elected.
Deploy agents on macOS, Linux, or Windows. They execute shell commands, manage files, run scripts — autonomous AI operations on your infrastructure.
Phone calls, SMS, email, camera, clipboard, location, notifications, haptics, flashlight, and more — all controllable by AI from any platform.
Built-in web search with LLM relevance filtering. MCP marketplace for extending with ChatGPT, Claude, Gemini plugins. 15+ skill sets included.
| Languages | UI: EN & RU. Chat: model-dependent (typically 50+; cloud often 100+). |
|---|---|
| Platforms | Web, iOS, iPadOS, Android, macOS Chat, macOS IDE, agents (macOS/Linux/Win), Telegram, Discord, Slack. |
| Chat | Multi-expert, debate & critique, streaming, web search in chat, memory, per-expert provider. |
| Notes | Voice orb capture, week/day strip, sync; iOS + macOS Chat sidebar. |
| Workflows | Visual orchestration + 122+ integrations (Slack, GitHub, Notion, CRMs…). |
| Tasks | Kanban board on the same AI pipeline with history. |
| IDE | Native macOS: multi-agent coding, Git, terminal, MLX on Apple Silicon. |
| Storage | FAISS vector memory; PostgreSQL for users/settings — you control retention on self-host. |
| Interfaces | HTTPS, WebSocket, MCP, OAuth connectors, DMG/PKG/DEB/EXE agents. |
Voice UX + calendar filtering; iOS/macOS parity.
Live voice/video sessions; web search + optional LLM verification.
Canvas: triggers, approvals, agent nodes.
Board + execution alignment; MLX IDE loop; Telegram, Discord, Slack same experts as web.
Writers, developers, analysts, and founders who use AI in delivery roles and require personalization, productized integrations, and controllable data paths. Pro (~$19/mo) enables full cloud-backed capability on published tiers.
Workgroups that require production-grade automation without multi-quarter bespoke AI programs—122 connectors productized in the workflow layer, with time-to-value oriented onboarding.
Privacy-first, self-hosted deployment. Run entirely on your infrastructure. No data leaves your network. SSO, audit trails, compliance-ready.
Native IDE with multi-agent coding, system agents for operations, MCP connectivity, and seven specialized agent archetypes—an integrated toolchain for software delivery and platform automation.
$0
$19/mo
Custom
Multi-expert AI with memory, world model, debate system, web search, streaming — live on AWS with blue-green deployment, Neptune graph DB, Redis, S3.
iOS (App Store), Android (Play Store), macOS Chat, macOS IDE, Web, Desktop Agent (3 OS), Telegram Bot, Discord Bot, Slack Bot — all with offline mode.
n8n-style integration system: Slack, GitHub, Jira, Notion, Salesforce, HubSpot, Stripe, Google Sheets, S3, PostgreSQL, MongoDB — 540+ individual actions.
Llama 3.2 1B runs entirely on iPhone. MLX models on Mac. No cloud needed. Offline chat, vision, and code completion.
Beta users across web, mobile, and Telegram. Strong engagement with AI workflows, task orchestration, and multi-agent coding in the IDE.
| Capability | ChatGPT / Claude | n8n / Zapier | AAAAI |
|---|---|---|---|
| Multi-expert reasoning | Single LLM | No AI | 10+ experts debate |
| Persistent memory | Session only | No memory | Graph memory |
| Offline / local AI | Cloud only | Cloud only | Ollama + MLX |
| Visual workflows | No | Yes | AI + integrations |
| Integrations | Plugins only | 500+ | 122 + growing |
| Native mobile + desktop | Web + basic app | Web only | 9 platforms |
| Voice + vision (live) | Basic voice | No | Camera + voice + TTS |
| System agents (DevOps) | No | No | 7 agent types |
Intelligence embedded in operational workflows—governed, auditable, and aligned with organizational policy.
Persistent organizational memory, private execution paths, and measurable throughput on recurring knowledge work.
Across task management, communications, software delivery, document analysis, CRM automation, and infrastructure operations, one stack unifies context, integrations, and multi-expert reasoning—with continuous model and product iteration reflected in release cadence and evaluation metrics, not slogan-scale claims.
Capital deployment targets platform scale, connector density toward 500+, and systematic adoption among knowledge workers—execution milestones are tied to product and GTM metrics in diligence materials.
aaaai.me — Integrated · Governed · Measurable
aaaai.me