Does BI Backlog need access to our actual data?

No. BI Backlog analyzes metadata and usage information without requiring access to the underlying business data.

How long does a migration assessment take?

Assessment timelines vary based on the size and complexity of the BI environment, including the number of dashboards, reports, users, and data sources involved.

What does an assessment cost?

Assessment costs depend on the scope of the engagement, BI environment complexity, and migration requirements. KPI Partners provides pricing based on each organization’s needs.

Classification Deviation Accelerator

1 Deviation Received

Free-text description ingested directly from QMS (Veeva, SAP, or manual entry)

2 Knowledge Assistant

RAG engine retrieves taxonomy + SOPs · Cortex LLM predicts L1 → L2 → L3

3 QA Human Review

Reviewer approves, corrects, or escalates · Confidence score displayed · SSO-attributed

4 Resolution Recommender

Top resolution steps surfaced from historical CAPA library · SOP citations included

5 Feedback & Learning

All corrections written to Snowflake · Model improves with every QA decision

A production-ready, Snowflake-native AI pipeline that takes a deviation description and returns a governed three-level classification, confidence score, and resolution recommendations, in seconds, not days.

Ingests directly from your QMS, no file uploads or manual re-entry
Classifies into a GMP-aligned three-level taxonomy (L1 → L2 → L3)
Surfaces the top resolution steps from your organization's own CAPA history
Routes every classification through a human QA reviewer before finalization
Writes all decisions to an immutable audit trail with SSO user attribution

Classifies free-text deviation descriptions into a GMP-aligned L1 → L2 → L3 taxonomy driven from a live database table, not hardcoded. Version-controlled and auditable, with weighted composite confidence scoring at each level.

After classification, the approved category triggers a resolution agent that retrieves the top two relevant corrective action sequences from your historical CAPA library, with regulatory citations, SOP references, and root cause patterns.

A retrieval-augmented generation engine that ingests your SOPs, CAPA history, and regulatory guidelines into a vector store. The most relevant passages are retrieved and injected into the LLM prompt, grounding every prediction in your own knowledge.

A dedicated reporting tab generates a Cortex-powered narrative executive report on demand: situation summary, business impact by severity, root cause patterns, regulatory exposure, and recommended actions. Downloadable as print-ready HTML/PDF.

A governed QA review portal built on Stream lit where reviewers see the AI prediction, confidence score, and source evidence side-by-side. Approve, correct L1/L2/L3 independently, flag batch loss. Every action is SSO-attributed and timestamp-logged.

Every QA correction is written to a structured feedback table capturing the AI prediction alongside the human correction at all three levels. Corrections are recycled as few-shot examples and used to fine-tune the model, improving accuracy without manual retraining.