The AI work harness for everyone else.
Chat. Agents. Workflows. Knowledge. Apps. Connectors. One customer-owned platform your team uses instead of stitching together Perplexity, n8n, Lovable, and half a dozen other tools. Every agent, workflow, and app you build is yours.
The AI revolution arrived at work. It arrived unevenly.
67%
of employees now use AI at work
Only 18% of organisations have a formal governance policy.
Salesforce, 2026
70%
of enterprise AI runs outside IT oversight
Shadow AI is now the default, not the exception.
Lenovo, 2026
92%
plan to increase AI investment
Only 1% consider themselves mature in deployment.
McKinsey, 2025
The barrier isn't employee readiness. It's the absence of a single platform that gives every team the same AI capability developers already have. Workers are ready. Odokai is the one-stop harness.
Reduce tool spend. Move faster. Keep control.
Operator-led companies buy software to move faster, then get slowed down by vendor sprawl. A CRM here. A compliance tool there. An agency for content, a consultant for analysis, a freelancer for every gap. Odokai Team and Enterprise plans consolidate those costs into one platform, so your internal teams ship workflows, apps, and agents in-house instead of renewing another external contract.
SaaS subscriptions
CRM, project management, compliance tracking, content planning, reporting tools. Five, ten, fifteen seats each. Renewal every year. Integration between them costs more than the tools.
Service retainers
Content agencies, compliance consultants, research firms, marketing freelancers. Instead of renewing external retainers, your team builds and runs the capability inside the harness.
Manual processes
The spreadsheet that gets emailed around every week. The compliance report someone assembles by hand. The proposal that starts from a blank page every time. These are not people problems. They are tool problems.
One Odokai Team plan
£99/user/month. Chat, agents, workflows, knowledge, apps, connectors. Governed and owned by you. Lower recurring software spend, ship internal tools faster, and keep your operating logic in-house.
Start with TeamThe model is the engine. The harness is everything else.
In software, a harness is the system that wraps a model and makes it useful at work. Context loading. Tool orchestration. Error handling. Output routing. Integration with the systems where decisions get made. The chassis, the steering, the dashboard, the brakes. Cursor proved it for developers: the harness matters more than the model.
Odokai applies the same principle to office work. Not a chatbot. Not an API wrapper with an enterprise login. A full work harness that consolidates the tools your team would otherwise stitch together from separate subscriptions — chat, agents, workflows, knowledge, studio apps, and connectors — into one customer-owned platform.
Inside the harness
Multi-model conversational AI, grounded in your context
Persistent threads, file and image input, citations from your knowledge base, and visible model reasoning. Replaces the ChatGPT tabs scattered across your company. One surface, your policy, your data.
Role-specific agents that can read, write, and act
Build custom agents with their own prompts, models, tool access, and memory. Long-running autonomous jobs with execution history and human approval gates.
Turn natural-language goals into running automations
Visual orchestration for multi-step, branching, scheduled work. Structured inputs and outputs, queue-backed reliability, and a full run timeline for every execution.
RAG over the documents your team relies on
Index your knowledge bases, contracts, tickets, and SOPs. Cite sources in every answer. Refresh continuously. Keep the data inside your environment.
Studio builds the UI for any agent or workflow
Build a CRM, compliance tracker, client portal, or reporting dashboard inside Odokai Studio. Wrap any agent or workflow in a branded app, then replace the separate SaaS tool it used to require.
MCP-native, with first-party integrations and custom tools
Connect Notion, Slack, GitHub, Jira, databases, and anything with an API. Write custom tools in JavaScript. Run any MCP server. Trigger downstream systems via webhook.
What makes a harness yours, not someone else's
A harness you own, govern with built-in controls, run on any model, and extend without limits. Four pillars in one platform. The full case lives on the Why Odokai page.
You own the platform
Self-host inside your cloud, your private network, or fully air-gapped. Your data stays where it has to live. Your roadmap is not held hostage by a vendor.
Read the ownership case →Built in, not bolted on
RBAC, action-level audit, policy guardrails, and approval gates. SMCR-ready and aligned with the EU AI Act from day one.
Read the governance case →Best-of-breed, never locked in
Frontier, open-source, and self-hosted models behind one gateway. Route by team, workflow, or data sensitivity. Swap providers without rebuilding the work on top.
Read the model-choice case →Build anything. The Lovable principle, for office work.
MCP-native. Custom tools in JavaScript. Studio app builder with live preview. Describe what your team needs. Build it inside the harness. Ship it.
Read the extensibility case →Hosted assistant, or a harness you own?
ChatGPT Enterprise, Microsoft Copilot, and Claude for Enterprise are good consumer-grade AI products with an enterprise login on top. A harness inverts the relationship. Three lines that capture it. The full matrix lives on the Why Odokai page.
| Hosted assistants | Odokai | |
|---|---|---|
| Who owns the platform | The vendor | You |
| Where your data lives | Vendor infrastructure | Your cloud, private network, or air-gapped |
| Model choice | One vendor's models | Any frontier or open model, swappable |
Want the full eight-row breakdown across ChatGPT Enterprise, Microsoft Copilot, and Claude for Enterprise? See the full comparison on Why Odokai.
Describe it. Build it. Govern it. Ship it.
Book a 30-minute walk-through. We will show you Odokai running, build a workflow live, talk through how it compares to whatever you are using today, and map a path to your first production deployment.