AI for PBM Consulting

You spend 80% of RFP time on data cleaning. AI handles the normalization so you can focus on analysis and client delivery.

Use Cases

What AI can do for you

Use Case

How it works

Ingests claims data in any format (Excel, CSV, PDF extracts)
Standardizes NDCs to 11-digit format, maps to drug names
Adds therapeutic class, GPI, and category data
Flags anomalies for human review

Before → After

Before

NDC: 2143380 / Drug: (blank)

After

NDC: 00002-1433-80 / Lipitor 40mg / Statin

Use Case

Configured for how you work. Your benchmarks, your categorization rules, your report templates. Not a generic tool—built around your methodology.

How It Works

From kickoff to live in 3-4 weeks

Week 1

Discovery

Map your current workflow, data sources, and deliverable formats. Define the rules and benchmarks to configure.

Week 2

Build

Build the data pipeline and normalization engine. Configure to your specific methodology.

Week 3

Test

Run against historical RFPs. Validate output against your previous analysis. Refine rules.

Week 4

Launch

Go live on new RFPs. Train your team. Establish feedback loop for continuous improvement.

Questions

What consultants ask

What data formats can this handle?

Any format you receive from PBMs—Excel, CSV, PDF extracts, fixed-width files. The system normalizes regardless of input format.

How accurate is the NDC mapping?

High-confidence matches are auto-processed. Low-confidence matches are flagged for human review. You maintain control over the final output.

Can this use our existing benchmarks?

Yes. Configured with your proprietary benchmarks and pricing data. This isn't a generic tool—it's built around how you work.

What about client confidentiality?

Data stays within your environment. No client data is used to train models or shared across clients. Standard enterprise security practices.

Let's see if this fits your workflow

30 minutes. Walk through your current RFP process and see where automation would save the most time. No pitch deck.