Build an AI Assistant for Your Business — Without Stitching 6 Tools Together.
Most teams trying to build a real business AI assistant end up gluing OpenAI Assistants API + LangChain + a custom auth proxy + a Chrome extension + a hand-rolled audit log + a vector DB. Six tools, six bills, six failure modes, six weeks. Aeion replaces the whole stack with four native layers — Claw + AI + Neural-Bus + WebMCP — that already share your database, your auth, and your audit trail.
The Four-Layer Native Stack
Aeion AI — The Provider Gateway
One API in front of 57 LLM, image, voice, 3D, and embedding providers, with cost-optimized routing (GPT-4o for reasoning, Gemini Flash for bulk work), per-tenant spend caps, encrypted credentials, and automatic failover — so you never write a provider SDK integration again. Read more →
AeionClaw — The Assistant Engine
The conversational layer your users actually talk to, backed by 47 module-specific skills (commerce, support, finance, fleet, and more), 8 auto-routing personas, persistent memory, 89 MCP tools, and full RBAC — so it reasons over your real business, not a generic chatbot script. Read more →
Neural Bus — The Autonomy Layer
Don't wait for prompts. Neural Bus watches what's happening across the platform — a new order, a new support ticket, a failed login attempt — spots the patterns that matter, and dispatches AeionClaw skills to draft replies, flag anomalies, or kick off remediations, with a human approving anything that needs one. Read more →
WebMCP — The Browser Bridge
Tools run inside the user's own authenticated browser session — no server-side API key ever sits in the LLM prompt, no vulnerable Chrome extension to install. Same RBAC as the admin UI: if the user can't do it, the agent can't either. Read more →
Forge — The Agent Project-Manager
The layer your assistant uses to track its own work — project tracking, issue triage, knowledge capture, and session handoff — so agents pick up where they left off across sessions instead of starting cold every time. Read more →
Blueprint — The Workflow Layer
When a reaction needs more than one step, Blueprint hosts the multi-step workflow — conditional logic, approval gates, retries. Claw kicks off a Blueprint; the Blueprint calls Claw back when it needs reasoning partway through. Read more →
The Layering — What Calls What
Every business AI assistant needs to do four things: reason, act, react, and report. The four native layers map cleanly onto those needs.
Worked Example — A Compliance-Aware Support Assistant in 4 Days
Priya is CTO at a 40-person fintech building an AI assistant that triages customer-support tickets while staying HIPAA-aware (some of her customers are healthcare-adjacent). Here's the full build.
What You Avoid by Going Native
No API key in the browser
WebMCP tools execute in the browser session under the user's JWT — the LLM never sees a service-account key.
No prompt-injection path into your DB
Same RBAC checks the admin UI uses gate every tool call. A "Viewer" role can't be tricked into running "Delete."
No vendor lock-in
Aeion AI's 57-provider gateway means swapping OpenAI for Anthropic for Groq is a one-line config change. Skills stay the same.
No external SaaS holding your conversations
Conversation history, memory, audit log = rows in your tenant DB. Aegis backs them up on the same schedule as your business data.
No per-message billing
The platform is free; you pay your AI provider directly with zero markup. Per-tenant cost caps prevent runaway spend.
No 6-week integration project
Reaction Engine + 47 pre-built skills + 89 MCP tools cover ~80% of typical assistant use cases out of the box.
How It Compares
The honest landscape of "build a business AI assistant" options in 2026.
CTO / AI-buyer questions
No. Each one stands alone. You can use Aeion AI as a pure provider gateway behind an existing LangChain stack. You can use AeionClaw without Neural Bus if you don't want autonomy. You can use WebMCP without Claw if you want raw browser-native MCP. The four-layer stack is the reference architecture — pick what you need.
Every AI call — whether triggered by a user, a Claw skill, a Neural Bus reaction, or a WebMCP tool — flows through Aeion AI's usage-tracking layer. Per-tenant cost caps enforce at 80% (warn admin), 95% (warn user), 100% (hard block). One dashboard, one bill, one audit log across all four layers.
Yes — the Custom Provider Adapter pattern lets you plug in any OpenAI-compatible endpoint (vLLM, HuggingFace TGI, internal model server) via a `baseUrl` config. Air-gapped Enterprise deployments use this for Ollama / on-prem inference.
Aeion doesn't fine-tune anything. Your data stays in your tenant. If you want a fine-tuned model, do it directly with the provider (OpenAI, Anthropic) and bring the fine-tuned model ID via BYOM. Neural Bus + Claw will route to it like any other model.
Configurable per-reaction. Low-risk actions (drafting a reply, suggesting an assignee) auto-execute. Higher-risk actions (mass-updates, deletions, external comms) require admin approval — surfaced via Inbox + Slack with the proposed action plan. Approval queues live in the same notification pipeline as everything else.
Three guardrails. First, RBAC: the AI can only call tools the user could call. Second, audit log: every action is logged with prompt + response + tool calls + diff. Third, Aegis: undo any AI-initiated change via per-doc Version History, or bulk-undo a batch via the Temporal DB. The AI is never trusted with raw SQL — only with named tools that have schema-validated inputs.
Yes. Every layer is tenant-scoped. AeionClaw conversations, Neural Bus events, WebMCP tool calls, Aeion AI usage logs — all scope automatically to your tenant at the framework layer. Cross-tenant data leakage requires an explicit super-admin operation.