One platform. Every AI tool your team needs. Chat with context. Automate with visual workflows. Build with Studio. Search your knowledge. Connect to everything. Odokai consolidates the tools you would otherwise assemble from five separate subscriptions — all in one customer-owned harness.
The layers any organisation needs to move from experimentation to production AI. Governed models, agents that act across your systems, and experiences your teams adopt. Built-in RAG, hosted or private deployment depending on your plan, full API, and MCP, without hiring a platform team for every initiative.
Underneath the chat, agents, workflows, knowledge, apps, and connectors sits the architecture that makes them real in production. Model access governed by your policy. An execution engine built for long-running work. Team-facing surfaces your people will use.
One gateway across every provider, with per-team policy, routing, and spend control. Swap models without rebuilding the workflows on top. OpenAI-compatible API so existing clients keep working.
Long-running agentic jobs with queue-backed reliability, structured I/O, approval gates, scheduling, full execution history, and observability ready for Datadog and friends.
Studio app builder, embeddable agent surfaces, MCP-native extensibility, declarative connector packs for Notion, Slack, GitHub, Jira, databases, and the long tail of internal systems.
The capabilities under those layers that reclaim hours. The manual work your team should not have to repeat every time.
Describe the task in plain English. An agent builds the execution plan and runs it. Pause, resume, or cancel at any point. You get the output; the agent handles the steps in between.
Design multi-step processes as directed acyclic graphs on an interactive canvas. Chain agents, tools, approvals, and conditions into a workflow that is visible, auditable, and runs reliably every time, not just when a human remembers to trigger it.
Set it once. Run it on schedule. Weekly reports, daily briefings, monthly compliance outputs: generated and routed automatically without anyone manually starting the process.
Index your organisation's documents into a retrieval-augmented generation pipeline that agents query automatically before they act. The right context, surfaced from your own knowledge base, without anyone manually hunting through folders or copy-pasting into a prompt. See how organisations use this for due diligence, compliance, and clinical documentation.
AI that reads, writes, and operates on files inside a cloud workspace you control. Agents create, edit, and share documents as part of a workflow. Everything stays inside your governed environment, not in a chat window you cannot trace or retrieve.
Point agents at your indexed knowledge base, external sources, or both. They gather, synthesise, and return a structured brief, not a raw dump for a human to make sense of.
Cut subscription spend and launch internal tools faster. Most multi-team operators carry too many SaaS tools: sales has one, ops has one, marketing has one, legal has one. With Studio, you build those apps inside Odokai - your data, your fields, your controls. A CRM, compliance tracker, client portal, or reporting dashboard becomes part of the same governed platform instead of another renewal.
Conversations that remember where they left off and what documents they have seen. No re-explaining the context every time. Ongoing work, not one-off interactions.
The system recommends the right model for the task based on capability and cost. Your team spends time on the work, not on evaluating which model to use.
Operational AI needs control as much as capability. Governance and private deployment are what let you scale these layers beyond experiments.
Run Odokai on your own servers, in your private cloud, in a customer VPC, or fully air-gapped with no external connectivity. Your data does not move. Your infrastructure team stays in control of where it runs. Talk to us about your deployment model.
Every model call, every document the AI touches, every step in every workflow: logged with timestamps and exportable in full. Your compliance team can see exactly what happened and when.
Flexible RBAC controls that determine who can access which agents, which models, and which workflows. Clinical staff see clinical tools. Finance sees finance workflows. No one sees what they should not.
A centrally managed list of the models your organisation has approved for use. No staff member can route sensitive data to an unapproved model. The catalogue is yours to control and update.
Use OpenAI, Anthropic, Google Gemini, or open-weight models running locally on your own hardware. Mix cloud and local models in the same workflow: proprietary models where quality demands it, open-weight where data sensitivity or volume calls for it. Swap providers without rebuilding. Onboard a model released today in minutes.
Apply rules that control which teams can run which workflows, how much they can spend, and which execution paths are available to them. Governance is not a policy document. It is enforced at the platform level.
Embed AI capabilities into your own applications with token authentication, origin allowlists, and key rotation. AI that surfaces in your products, on your terms, with full security controls intact.
Upload, list, rename, delete, save, monitor, and export files, all inside your governed environment. Nothing leaves the platform without an explicit action that is logged and attributable.
Built to help procurement, security, and compliance teams say yes faster, without compromising control.
Odokai is engineered around control frameworks aligned with SOC 2 and ISO/IEC 27001 principles, with customer data protection and platform integrity treated as first-order requirements, not afterthoughts.
Current controls include authenticated access and role-based permissions, API token lifecycle management, secure defaults (CSRF, strict CORS, rate limiting, and security headers), sensitive-data-aware logging, audit trails for security-relevant actions, runtime guardrails, and monitoring designed for incident detection and investigation.
Our governance, evidence collection, and operational processes continue to mature in support of formal compliance programs.
Oversight and visibility built into the platform from the start, so the teams responsible for governance can say yes to more AI, faster.
Insert approval checkpoints at any point in a workflow. Agents do the work and surface the decision; a human approves before anything consequential is acted on. The judgment stays with your people.
Watch workflows execute live, node by node, with progress updates at every step. You are never waiting on a black box. You can see what is happening and intervene if something is wrong.
Spend and usage data across every user, team, model, and workflow, in one dashboard. No hidden AI costs. No surprises at the end of the month. Budget controls enforced before they are exceeded.
Manage users, roles, groups, model registry, feature flags, and operational settings from a single control plane. Your IT and operations teams run the platform; they do not need to delegate that to vendors.
Test model and workflow performance before you rely on it operationally. Built-in evaluation tools let you validate outputs, compare models, and iterate before anything goes live.
Generate and manage API tokens for secure programmatic access. Connect Odokai to your existing systems without giving up the security controls that govern everything else on the platform.
Integrations, scheduling, and orchestration that turn one-off AI tasks into repeatable, automated operations your team can rely on.
The relationship between Odokai and your stack works in both directions. Use the full Odokai API to trigger workflows, manage agents, and pull results into your own systems programmatically. Or connect your external tools into Odokai using MCP (Model Context Protocol), so agents can act on your systems directly. Both directions work. Neither requires replacing what you already have. Read more about delegating work to AI agents.
Browse and activate pre-built connectors for Gmail, Notion, databases, and custom APIs through the connector marketplace. Agents that work alongside your existing tools rather than demanding your team learn something new from scratch.
Extend what agents can do by connecting external tool servers. Custom capabilities built for your specific processes, without rebuilding the underlying platform infrastructure.
Build and operate complex automated workflows from one control plane. Sequential pipelines, parallel processing, conditional routing, and error handling: the kind of reliability your operations team needs to trust AI with real work.
Share agents, conversations, and workspaces across users and groups. The expertise built into one agent is available to the whole team, not siloed in a single person's account.
Surface AI capabilities inside your own products and internal tools using straightforward code snippets. Governed, audited AI, not a public API embedded without oversight.
Lovable did this for developers building apps. Odokai does it for office workers building workflows and applications. You don't get a SaaS subscription with someone else's logo on it. You get the workflow and the app your team needs, running inside your governed environment, owned by you. For a small team, this means bringing capability in-house that used to require a service contract. Here is what that looks like in the first month.
Custom fields, your workflows, your access controls. Running on your infrastructure. Not a SaaS subscription with a five-figure annual seat charge. The CRM your team would have asked engineering to build, without the engineering backlog.
Describe the campaign. Odokai drafts the content, segments the audience, schedules the sends, and tracks the results. All inside the harness. All logged. All on-brand. Marketing keeps the strategy. The assembly disappears.
A research agent. A drafting agent. A review agent that checks against your brand and tone guidelines. A publish step into your CMS. Governed. Consistent. On-brand. The agent produces the first ten drafts. Your team approves four.
A workflow pulls data from your systems, runs it against the regulatory requirements, flags the exceptions, and routes them to the right Senior Manager. Full audit trail. SMCR accountability built in. The thing the team does manually every month, running on a Sunday night instead.
You aren't limited to what Odokai ships with. Build custom agents for your processes. Plug into the systems you already run: CRM, document management, case management, ERP. Ship domain-specific workflows for regulated industries. Add new models, new tools, and new integrations as your needs change.
The Odokai platform is the same wherever you deploy it. These are the kinds of agents and workflows teams put live first, across every function. All built on the same chat, agent, workflow, knowledge, and app surface.
Legal and Risk
Use agents to run first-pass review against your policies and precedent, then route the output to a human approver with the right context already assembled.
Fewer hours on first-pass analysis. Humans keep the final call.
Finance
Pull data from connected systems, apply checks, and generate the draft report automatically. Analysts see only exceptions and sign-off steps, not a blank page every cycle.
Less manual assembly. More time on the numbers that matter.
Sales and Business Development
Research prospects, summarise signals from your CRM and external sources, and generate first-draft proposals grounded in your previous wins. Reps focus on the relationship.
Faster pipeline progression. Less time on preparation, more on selling.
Operations
Automate procurement communications, supplier follow-ups, and weekly status updates. Agents gather context from connected systems and draft outputs your team reviews once before they go out.
Fewer coordination hours. Handoffs that happen on time without chasing.
Customer Success
Ground agents in your product documentation, past tickets, and operating procedures. Teams draft accurate responses faster, escalate only what needs a human, and keep a clear record of what was said.
Faster first response. Consistent answers. Less time searching for the right document.
Nobody gets replaced. The repetitive assembly work disappears. The team spends its time on review, judgement, and the decisions only a human can make.
Swipe or scroll horizontally to compare all columns.
| Workflow | Before | With Odokai |
|---|---|---|
| Recurring document review | Subject-matter experts read everything from scratch | Agents handle first-pass analysis and route the exceptions for review |
| Scheduled reporting | Analysts collect, format, and check data manually | Data is pulled and drafted automatically, with humans focused on sign-off |
| Account and prospect research | Reps spend hours researching each account before outreach | Agents compile relevant signals from your CRM and external sources into a ready-to-use brief |
| Proposal and pitch creation | Sales teams start from a blank template, pulling in details by hand | First-draft proposals are generated from past wins and account data, with reps refining before they go |
| Customer support response drafting | Support teams search docs, past tickets, and knowledge bases for every response | Agents draft grounded responses instantly, with the team reviewing and sending |
| Contract or policy redlining | Every edit starts from a blank page or last version | First-pass changes are proposed automatically against known standards |
| Weekly or monthly updates | Managers spend hours collecting updates and writing summaries | Updates are compiled and drafted automatically, then reviewed once |
| Operational approvals | People coordinate decisions across email, chat, and spreadsheets | Approval steps are explicit, auditable, and built into the workflow |
Managed when speed wins. Private cloud when the data has gravity. Air-gapped when the regulator says so. The agents, workflows, knowledge bases, and apps are identical across all three. Changing deployment mode is a deployment decision, not an engineering project.
Notes on governed models, operational automation, and how teams move from AI-assisted pilots to broader adoption.
1 June 2026
UK SMEs spend about £23K a year on AI tools. The true cost is closer to £57K–£71K once overlap, waste, and compliance gaps are counted.
Read more30 May 2026
Where AI helps, built for you, live in weeks. Odokai advises, delivers tooling on your data, and runs it on one governed platform, from one partner.
Read moreChoose the process that costs your team the most time. Due diligence, compliance reporting, clinical documentation, or content production. Start on a hosted plan or deploy Enterprise to your infrastructure, align the registry and execution layers to your policy, and prove value in weeks. The pattern is repeatable, not a frozen one-off.
