Data Architecture Checklist Before LLM Deployment

Disclaimer: The examples and patterns described in this article are generalized from industry observations and do not reveal internal technical stacks, specific implementation details, or proprietary information from any past employers or clients.

You've chosen your LLM. You've written your prompts. Your POC looks promising in the demo environment.

But before you deploy to production, ask yourself: Is your data architecture ready?

Most AI failures aren't caused by bad models—they're caused by bad data foundations. Here's the checklist we use at MetaFive One to assess enterprise readiness for LLM deployment.

1. Data Source Inventory

Question: Where does your data live?

Checklist:

• Structured data sources documented (databases, CRMs, ERPs)
• Unstructured data sources documented (logs, support tickets, documents, emails)
• Real-time vs. batch data identified (streaming APIs vs. daily exports)
• Data ownership mapped (which team owns which data source)
• Access permissions audited (who can read/write to each source)

Red Flag: If you can't list all your data sources in 15 minutes, you're not ready to deploy AI.

2. Data Quality Assessment

Question: Is your data clean, consistent, and complete?

Checklist:

• Missing data quantified (what % of records have null values for critical fields)
• Data consistency validated (same customer ID across systems, date formats standardized)
• Data freshness measured (how old is the data? updated daily, weekly, monthly?)
• Duplicate records identified (how many duplicate customers, products, transactions exist)
• Data accuracy spot-checked (sample 100 records manually, verify correctness)

Red Flag: If more than 10% of your data is missing, inconsistent, or duplicate, fix that before deploying AI.

3. Data Pipeline Design

Question: How will data flow from source systems to your LLM?

Checklist:

• ETL/ELT process defined (Extract, Transform, Load or Extract, Load, Transform)
• Data transformation rules documented (how raw data becomes AI-ready data)
• Pipeline orchestration chosen (Airflow, Prefect, dbt, or custom scripts)
• Error handling implemented (what happens when a data source is down or returns bad data)
• Pipeline monitoring configured (alerts for failed jobs, data quality issues)

Red Flag: If your data pipeline is "someone manually exports a CSV once a week," you're not ready for production AI.

4. Data Governance & Compliance

Question: Who owns the data, and are you allowed to use it for AI?

Checklist:

• Data ownership documented (which team/person is accountable for each dataset)
• Legal review completed (can we use this data for AI under GDPR, CCPA, HIPAA, etc.)
• PII identified and classified (names, emails, SSNs, credit cards, health records)
• Data retention policies defined (how long do we keep data? when do we delete it?)
• Consent mechanisms verified (did users consent to AI processing of their data?)

Red Flag: If you're in a regulated industry (healthcare, finance, government) and haven't consulted legal, stop immediately.

5. Data Security & Access Control

Question: How do you prevent unauthorized access and data leaks?

Checklist:

• Encryption at rest enabled (databases, file storage, backups)
• Encryption in transit enabled (TLS/SSL for all data transfers)
• Role-based access control (RBAC) implemented (least privilege principle)
• Audit logging enabled (who accessed what data, when, and why)
• PII masking/tokenization configured (sensitive data replaced with tokens before LLM processing)

Red Flag: If your LLM can access raw customer PII without masking, you're one prompt injection away from a breach.

6. Vector Database & Embeddings Strategy

Question: How will your LLM retrieve relevant context from your data?

Checklist:

• Vector database chosen (Pinecone, Weaviate, Qdrant, or Postgres with pgvector)
• Embedding model selected (OpenAI, Cohere, or open-source like Sentence Transformers)
• Chunking strategy defined (how to split large documents into searchable chunks)
• Metadata schema designed (what metadata to store with each embedding for filtering)
• Retrieval-Augmented Generation (RAG) pipeline tested (can the LLM find the right context?)

Red Flag: If you're planning to fine-tune an LLM instead of using RAG, reconsider—RAG is faster, cheaper, and more maintainable for most use cases.

7. Data Observability & Monitoring

Question: How will you know if your data pipeline breaks?

Checklist:

• Data quality metrics defined (completeness, accuracy, freshness, consistency)
• Monitoring dashboards built (Grafana, Datadog, or custom dashboards)
• Alerting rules configured (Slack/email alerts for pipeline failures, data quality issues)
• Data lineage tracked (can you trace data from source to LLM output?)
• Incident response playbook written (what to do when data quality degrades)

Red Flag: If you don't have automated alerts for data pipeline failures, you'll find out about problems when users complain—too late.

8. Cost & Scalability Planning

Question: Can your data architecture handle production load without breaking the bank?

Checklist:

• Data storage costs estimated (databases, vector databases, backups)
• Data transfer costs estimated (API calls, network egress, cross-region transfers)
• Embedding generation costs estimated (how much to embed all your data?)
• Query costs estimated (how much per LLM query, including vector search?)
• Scalability plan documented (what happens when data volume 10x? 100x?)

Red Flag: If you haven't calculated the cost of embedding your entire dataset, you might be in for a nasty surprise.

The Bottom Line

Data architecture is not optional. It's the foundation that determines whether your AI succeeds or fails.

If you can't check off at least 80% of the items on this list, pause your LLM deployment and fix your data foundation first. It's not glamorous, but it's the difference between a successful AI initiative and an expensive failure.

Need Help?

At MetaFive One, we conduct data architecture audits for enterprises planning LLM deployments. We'll assess your current state, identify gaps, and provide a prioritized roadmap to production readiness.

Book a free 30-minute AI Readiness Audit: Contact Us [blocked]