How to build an AI messaging assistant using Athena BDA data

AI messaging only works when it has the right structure around it.

For pharma outreach, that means the AI needs more than a prompt and a contact list. It needs access to well-structured contact, drug, signal and conference data. It also needs clear instructions on how to read that data, messaging rules that keep the output accurate, and an execution workflow that can turn the messaging into a multi-channel campaign that lands in an inbox.

The Athena BDA AI messaging assistant is built around four layers:

• Athena BDA Data Foundation
• AI Instruction Layer
• Conditional Messaging Layer
• Cowork Execution Layer

Together, these layers help the assistant move from raw data to personalised outreach that is grounded in deep context.

1. Athena BDA Data Foundation

The first layer is the Athena BDA Data Foundation.

This is the structured database that captures context for each contact, so the AI assistant can understand who a contact is, what they are connected to, and why they may be relevant.

The more useful contact context you can give the assistant, the better the outreach can become.

With Athena BDA you have access to several types of context:

Contact context

This includes fields such as therapy area, disease area, brand, role type, company, geography and seniority.

Signal data

This includes job changes, FDA approvals, pipeline news updates, new indication approvals and other relevant pharma news catalysts.

Drug context

This includes administration route, testing requirements, boxed warnings, device status, companion diagnostics and lifecycle stage.

Conference intelligence

This includes speaker data and speaker session topics, helping you understand where a contact will be and what they will be speaking about.

This foundation matters because richer contact context leads to better email personalisation.

Instead of asking the AI to write a generic message to a pharma contact, you are giving it structured information about the person, their company, the brand they may be connected to, and the commercial signal that makes them relevant now.

2. AI Instruction Layer

The assistant needs to be taught how to read the Athena BDA data.

This instruction layer explains what each Athena BDA field means and how the assistant should use it.

That includes:

Data definitions

The AI needs to understand what each data point represents. For example, what job titles are included in each role type and the definitions for each intent signal.

Signal interpretation

The AI needs guidance on how to interpret different signals. A job change, an FDA approval, a pipeline news update and a conference speaking slot all create different messaging opportunities.

Approved messaging only

The assistant is instructed to stay within approved messaging and approved proof points. It should not invent customer claims or commercial facts.

High-level messaging guidance

The AI also needs clear direction on tone, structure, message length and how direct or consultative the outreach should feel.

This layer is especially important in pharma, where accuracy matters.

The assistant needs to be trained to use the Athena BDA data and the company-approved messaging it has been given. It should not hallucinate drug details, exaggerate claims or create messaging that has not been approved.

3. Conditional Messaging Layer

This is where you map your company-approved messaging to different Athena BDA data contexts.

The aim is to help the AI understand which message should be used for which type of contact, brand, signal or drug attribute.

For example:

• If a drug has a testing requirement, the assistant might prioritise patient identification messaging.
• If a contact is linked to a particular therapy area, the assistant can use the most relevant therapy-area message.
• If there is a relevant case study, the assistant can use that example in the messaging.
• If a contact works at a company where there is an existing relationship, the assistant can adapt the opener or angle accordingly.

This is why the quality of the conditional messaging layer matters.

The more conditional logic you build, the better the personalisation becomes.

This gives the assistant a controlled set of rules so it can match the right approved message to the right data context.

4. Cowork Execution Layer

The final layer is execution.

For this workflow, Claude Cowork provides a practical way to turn the Athena BDA data, AI instructions and conditional messaging into real outreach.

This execution includes:

Inbox connection

Using Cowork, you can connect inboxes to send outreach through your own email infrastructure rather than through a cold email tool.

LinkedIn follow-up

Go multi-channel and use Cowork to create tailored LinkedIn connection messages and follow-up messages.

Inbox rotation

Sending should be done across multiple inboxes to spread the load of your cold outreach and protect your main email inbox.

Controlled sending

Set up Cowork to limit the number of daily sends and review steps before campaigns go live.

This matters because deliverability is a major part of outbound performance.

Using Cowork connected to your own inboxes helps keep outreach closer to your own email infrastructure, rather than relying entirely on cold email tools or shared sending systems.

The execution layer turns the framework into a working outreach motion.

Bringing the framework together

The Athena BDA AI messaging assistant is not just about generating better copy.

It is about connecting four things:

• Athena BDA data gives the assistant structured pharma context.
• AI instructions teach the assistant how to read and use that context.
• Conditional messaging maps approved messaging to the right data patterns.
• Cowork execution turns the output into real outreach across email and LinkedIn.

When these layers work together, the assistant can create outreach that is more specific, more relevant and more grounded in the commercial context behind each contact.