Prepare Your Platform Data for Production AI

Modern AI initiatives stall when foundations are brittle. At Digiteria Labs we start every engagement with a ruthless assessment of data readiness so the first sprint doesn't disappear into firefighting. Use this checklist to baseline your platform before we wire up models, copilots, and orchestration.

1. Contracts and Lineage Are Non-Negotiable

You cannot automate knowledge or deploy copilots if upstream semantics are a mystery. Every critical feed must ship with:

• Source of truth ownership and escalation path.
• Schema contracts with versioning and backward-compatibility guarantees.
• Column-level lineage and quality SLAs published to stakeholders.

If lineage tooling is missing, we bootstrap metadata using dbt docs, OpenLineage, or even lightweight spreadsheets so people can trust transformations on day one.

2. Freshness, Volume, and Criticality Tiers

Different workloads tolerate different levels of staleness. We tier datasets by business impact:

• Tier 0: Customer-facing metrics, real-time decisions, fraud detection. Requires automation, dual-writes, and sub-hour freshness alerts.
• Tier 1: Operational reporting, marketing automation. Needs hourly checks, replay tooling, and backlog tracking.
• Tier 2: Exploratory analytics. Document retention periods but avoid over-investing in SLOs.

With this segmentation we design monitoring rules that trigger when the business would actually feel pain.

3. Compliance and Security by Default

AI products increase surface area for sensitive information. Confirm:

• PII discovery scans run on every ingestion path.
• Access is gated through role-based policies.
• Audit trails for reads and writes flow into centralized logging.
• Data retention policies match jurisdictional requirements.

Digiteria Labs hardens environments with zero-trust defaults so security review does not block launch.

4. Feature Store and Embeddings Governance

Whether serving ML features or semantic search embeddings, teams need shared infrastructure. Baseline questions:

• Where do features live and how are they versioned?
• Can downstream teams discover and reuse them without Slack archeology?
• Do embeddings align with data residency rules?

We deploy catalog patterns aligned to tools like Feast, Databricks Feature Store, or custom registries backed by relational stores.

5. Runbooks for Incidents and Drift

AI systems fail in new ways. Prepare before the pager rings:

• Document on-call rotations with clear escalation to data leadership.
• Automate anomaly detection for drift, bias, and cost anomalies.
• Store golden datasets for replay and offline evaluation.

Digiteria Labs deliveries always end with battle-tested runbooks. This is how we protect velocity after launch.

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Need a partner to accelerate production AI safely? Schedule time with Digiteria Labs and we will map a delivery plan tailored to your platform.