Solution
Adopt AI on top of your data — not beside it.
Move from pilots to production by giving AI the three things pilots lack: governed data, real tools and a record of what happened.
The problem
What teams are dealing with.
- Pilots impress in a demo and stall when they need access to real data and real systems.
- Each team picks its own model and its own way of connecting it, and nobody has the inventory.
- Leaders are asked to approve AI use without being able to see what it does.
Why it stays unsolved
Existing approaches fragment the workflow.
- AI tooling is bought separately from the data platform, so every use case starts with an integration project.
- Governance is added last, by a different team, in a different tool.
The DooHorizon solution
DooHorizon treats AI as one more layer of a platform you already govern.
FlowDoo
Status: AvailablePrepares the data AI depends on and carries out the actions it decides on.
About FlowDooMLDoo
Status: PreviewProvides explainable predictive models for the decisions that should not be left to a language model.
About MLDooAIDoo
Status: Coming SoonIs designed to add retrieval, agents and approvals on top of both.
About AIDooGovernanceDoo
Status: Coming SoonIs designed to keep the inventory of models and agents and the policy for each.
About GovernanceDoo
Reference architecture
How the pieces are arranged.
- Governed data
- FlowDoo
- MLDoo modelsAIDoo agents
- GovernanceDoo
- Automated action
Example business workflow
Start with one use case. Expand across the ecosystem.
- Pick one use case
- Prepare its data
- Build and explain
- Review and approve
- Automate
- Expand
Business outcomes
What this lets you do.
- Start with a use case that is useful on its own, on products available today.
- Add AI capabilities without re-platforming the data underneath.
- Show leadership what each model and agent does and who owns it.
- Avoid a second, ungoverned stack for AI.
Governance and security
How it stays under control.
- Models carry a Model Passport with their data, performance and limitations.
- Agents are designed to act only through approved tools, with human approval where it matters.
- One audit trail is the design goal across data, models and AI.
Start with this use case.
Start with one use case. Expand across the ecosystem.