The questionOur data is not clean enough for AI. Where would we even start?
A four-phase methodology that establishes trust before expanding scope. It starts from read-only connectivity, deliberately surfaces the data-quality problems most vendors conceal, deploys grounded agents with known caveats built in from day one, then expands domain by domain — each expansion gated on the previous one having proved itself.
What we can evidence
Start without cleaning your data first.
Phase two is a deliberate data-quality audit: the platform surfaces stale feeds, default-coordinate records and orphaned assets as actionable findings rather than concealing them. Known caveats are then baked into agent instructions from day one, so answers are trustworthy immediately rather than after a long calibration period.
Expand only after the previous domain has earned it.
Each new domain — root-cause analysis, planning, optimisation, closed-loop automation — is added after the previous one has demonstrated value. The sequence is designed so a stalled phase costs a phase, not a programme.
The reality most AI vendors obscure
Production network data is rarely clean. Feeds go stale, coordinates default to the centre of the country, records orphan when equipment is decommissioned. An AI platform that hides these problems rather than surfacing them creates more risk than it resolves, because it produces confident answers built on quietly broken inputs. The honest alternative is to treat data quality as the first deliverable rather than a precondition.
The four phases
Connect read-only
Scoped credentials and schema mapping, with purpose-built query tools per data source. No free-form SQL against production systems.
Data-quality reality check
The platform surfaces stale feeds, default coordinates, orphaned records and other anomalies — presented as actionable intelligence, not as failures of the implementation.
Grounded agents on a verified picture
Known data caveats are written into agent instructions from the first day, so the agent flags a suspect figure rather than quoting it confidently.
Expand by evidence
New domains are added one at a time, each gated on demonstrated value from the last.
Governance without sacrificing velocity
Digitata Networks delivers and operates the platform in the early phases; Studio's self-service layer lets the operator's team take over as much ownership as their capability supports. Versioning and rollback on both assistants and skills, integrated tracing, and a built-in promotion pipeline for controlled capability releases mean governance and speed are not traded against each other.