What we build
Code, prompts, procedures, and documentation belong to you.
Agents, harnesses & AI governance
Useful AI agents, supervised like colleagues.
We deploy AI where it holds up in your operations: assistants, agents connected to your tools, and control harnesses that limit what each agent can do. Engineers deployed within your organization, not one more subscription.
- For
- Executive leadership, operations, IT departments, and document-heavy business teams
- What changes
- Useful AI agents, supervised and audited
- First step
- An operational diagnostic of your use cases
The problem
AI is easy to buy and poorly deployed. Without a scope, a log, and an owner, an agent is a risk. Without a proven use case, it stays a demo.
What changes
Before, after.
- TodayAI tools adopted without rules.With Enterprise AIA defined usage framework, controls, and human review.
- TodayPilots that never reach production.With Enterprise AIAgents integrated into your tools and real workflows.
- TodayDependence on the provider.With Enterprise AITrained teams and transferred know-how.
Features
What Enterprise AI does.
Scoping and proof
One use case, one hypothesis, one protocol: we test before investing.
Agents and harnesses
Agents connected to your systems through standard interfaces (MCP, API), with identity, scope, and limits.
Deployed engineering
DOSOBE engineers work within your teams until go-live.
Training and handover
Masterclasses and workshops: your teams operate and govern the solution.
Public sector
AI assistants operating within the rules of the administration: identity, scope, revocation.
Ministries, agencies, public operators: supervised assistants, sovereign use of data.
Private sector
Agents connected to your tools, with every action logged.
Enterprises, banks, industries: business agents and governed automation.
Implementation
Start small, decide on facts.
Each step ends with a deliverable and a decision: continue, adjust, or stop.
- 01
Diagnose
Map the work, the constraints, and the expected value.
- 02
Validate
Test the options under your real conditions, with stopping criteria.
- 03
Design and deploy
Integrations, controls, harnesses: going into production within the workflow.
- 04
Train and govern
Training by role, procedures, monitoring, and continuous improvement.
What you keep
Choice of models
We stay neutral: the model is chosen for the use case.
Control
Each agent has bounded rights and a trace of its actions.
Questions
The questions we are asked.
Which AI model do you use?
The one that fits the use case, your data, and your hosting constraints. We remain independent of vendors.
Is our data used to train models?
Data flows are documented and decided with you. No use of your data for training is implied.
How long before a first result?
The diagnostic and validation are time-boxed and set at the start, so you can decide quickly based on facts.
What happens after go-live?
We stay on for monitoring and improvement until your teams run it on their own.