The DataS Sovereign AI Framework
Discover. Control. Deploy. Operate.
A repeatable path from a business bottleneck to a governed AI workflow in production, with the control, evidence, and portability decisions made up front instead of retrofitted later.
- 1
Discover
Map where the value is, where the data lives, who owns it, and what could go wrong before anything is built.
What happens
- Interview operators, finance, and engineering on the bottlenecks that cost real hours
- Inventory systems, data sources, exports, and the spreadsheets that glue them together
- Assess data sensitivity, ownership, and contractual or jurisdictional constraints
- Score candidate workflows on value, data readiness, and risk
Deliverable
An AI and data opportunity map, a 90-day roadmap, one priority use case, and a fixed-scope pilot plan.
Delivered via
- 2
Control
Set the boundaries first: which data, which people, which models, under what policy, with what evidence.
What happens
- Define data boundaries and classification for the workflow in scope
- Set identity and access policies: who and which agents may read or act on what
- Choose model access and deployment options, including self-hosted where warranted
- Specify retention, deletion, logging, and audit requirements
Deliverable
A DataS control architecture and an implementable governance baseline your team can own.
Delivered via
- 3
Deploy
Build the selected workflow end to end, on real data, with the approvals and monitoring it needs to run unattended.
What happens
- Integrate source systems through APIs, exports, warehouses, or custom connectors
- Build the structured data context and retrieval or knowledge layer
- Implement model routing, agent logic, and the user-facing surface
- Wire human approval steps, monitoring, and evaluation into the release
Deliverable
A deployed workflow solving one measurable operational problem, with its success metric instrumented.
Delivered via
- 4
Operate
Keep the system honest: measure quality, cost, safety, latency, and adoption, and adjust before drift becomes damage.
What happens
- Run scheduled evaluations against real traffic and edge cases
- Tune models, prompts, policies, data mappings, and workflow steps
- Report on business outcomes, cost, and governance posture
- Maintain an improvement backlog tied to the metric that matters
Deliverable
Managed AI operations, periodic evaluation reports, and a prioritized improvement backlog.
Delivered via
An operating partner, not an hourly consultant.
Each phase has a fixed scope, a named deliverable, and a clear hand-off to the next. You can stop after any phase and keep everything we built.