TECHNICAL ASSESSMENT

Is Your Data Stack
Ready for AI?

Before you hire a prompt engineer, you need an infrastructure check. Most AI projects fail because the underlying data isn't ready.

1. Data Centralization

Is your data fragmented across Google Sheets, Salesforce, and random CSVs? Or does it live in a unified warehouse?

Not Ready

"We pull reports from 5 different tools manually."

AI Ready

"All core business entities are modeled in Snowflake/BigQuery."

2. Unstructured Data Access

Can your system programmatically access documents, PDFs, and call transcripts? AI thrives on text.

Not Ready

"Files are stuck in individual Dropbox folders."

AI Ready

"Documents are stored in S3/GCS with metadata tagging."

3. Data Quality & Cleansing

LLMs are prone to hallucinations. Feeding them duplicate or messy data guarantees bad results.

Not Ready

"We have 15 versions of the same customer name."

AI Ready

"Automated dbt tests ensure data integrity before it hits the model."

4. Privacy & Governance

Do you know exactly what PII sits in your database? You cannot send sensitive customer data to OpenAI blindly.

Not Ready

"We give everyone admin access."

AI Ready

"PII is masked at the source and Role Based Access Control is enforced."

Not sure where you stand?

We offer a comprehensive technical audit to map your data landscape and provide a roadmap to AI readiness.

Book Your Technical Audit