Protect the data. Prove you can recover it. Make it usable by AI.
Three disciplines, engineered together: protection that survives a compromise, recovery you have measured rather than estimated, and data in a condition intelligent systems can actually work with.
One governed layer, protected and recoverable.
Business systems feed one governed layer. Protection keeps it safe, recovery gets it back on a proven timeline, and the same modelling that makes it restorable is what makes it usable by AI.
Three disciplines, engineered as one.
Protection, recovery and AI readiness share the same foundations — modelled data, known ownership and proven restores.
Protection an attacker cannot undo.
Backup, replication and immutable copies engineered against a stated recovery point and held outside the credential boundary that runs production — plus retention that meets your obligations rather than keeping everything forever.
Recovery that is rehearsed, not assumed.
Failover engineered to a measured recovery time, restores tested on a schedule, and a separate path for recovering from a compromise rather than a hardware failure.
The prerequisite AI actually has.
Retrieval quality is a data engineering result. AI-ready data is modelled, documented, access-controlled and refreshed — before any model is chosen.
Everything in this practice, in detail.
Protection, analytics and the foundations AI depends on — each with its own page rather than a summary line.
Data Protection
Backup
Backup engineered to a stated recovery point objective, with immutable copies and scheduled restore testing.
Read the detail →Replication & Snapshots
Snapshot and replication design that narrows recovery point to minutes, with consistency and lag monitoring.
Read the detail →Ransomware Protection
Immutable and isolated backup copies, separated credentials and anomaly detection, so recovery survives a compromise.
Read the detail →Archiving & Retention
Retention schedules mapped to obligations, cold data tiered, and defensible automated deletion.
Read the detail →Data Recovery
Disaster Recovery
Failover and recovery engineered to a stated recovery time objective, with rehearsed and documented runbooks.
Read the detail →Restore Testing
Automated, application-validated restore testing on a schedule, producing measured timings and audit evidence.
Read the detail →Cyber Recovery
Recovery designed for compromise: known-good point identification, scanning before restore and clean-room rebuild.
Read the detail →Business Continuity
Process-level continuity: roles, manual fallbacks, communications and rehearsal, alongside technical recovery.
Read the detail →Data for AI
AI-ready data
Preparing operational data for AI: modelled, documented, permissioned and refreshed on a known cadence.
Read the detail →Vector Search
Embedding, indexing and hybrid retrieval infrastructure sized to your corpus and refresh needs.
Read the detail →Knowledge Bases
Structured, owned knowledge with review cycles and freshness signals, for people and AI systems alike.
Read the detail →Governance
Data ownership, classification, access and retention implemented as enforceable technical controls.
Read the detail →Is your data ready for what you want to build on it?
Bring one question the business keeps asking. We will trace what answering it reliably would take.