About Sensemaking

Sensemaking turns collected data during Fact Finding into understanding. It's ELLA's contextual AI layer, designed to help you move from intake to insight without breaking your flow.

Advisors spend countless hours making sense of fragmented data, such as spreadsheets, notes, and (sometimes half-remembered) conversations. Sensemaking closes that gap by giving you fast, contextual intelligence that’s:

• Accurate & grounded: Pulls directly from verified fact-finding data.
• Secure: Sensitive details are redacted before reaching any model.
• Malleable: Prompts adapt to your workflow; no “prompt engineering” required.

Understanding Sensemaking

Fact Finding helps you gather details. Sensemaking helps you use them. It surfaces patterns, risks, and opportunities inside your client’s business using the data already stored in ELLA.

You can think of it as your AI-powered research partner. It knows your client’s business as well as you do, and never confuses details about one client with another or forgets what it's learned after a few weeks or months without talking to that client. It's also connected to the rest of ELLA, so it can use the data you've collected in other parts of the workbench to provide even more context.

The result: you can prepare for a meeting, analyze risks, or clarify value drivers in seconds.

Using Sensemaking

1. Choose a business Open any client workspace and select the Sensemaking tab.

2. Select or enter a prompt Use one of the built-in prompts, for example: "Prepare for client meeting" or "Break down risk factors" to get started, or enter your own prompt. Our built-in prompts are tuned to the specific needs of exit planning advisors and handle the "prompt engineering" for you, but you can always enter your own prompt to go your own direction.

3. Process and review ELLA analyzes the relevant fact-finding data, enriches it with context from across your client workspace, and routes it through the best model for that type of question.

4. Get actionable insights You’ll see a structured output with clear calls to action. When information is missing, ELLA flags it instead of guessing, so you can trust what you see.

Behind the Scenes

Sensemaking uses a secure AI pipeline built in-house. This allows multiple LLMs (like OpenAI, xAI, or Gemini) to be used safely through a shared redacted pipeline. No client data is ever retained or used for training by any third-party models.

Every query runs through three layers of protection:

• PII redaction before model calls
• Model routing to select the right provider
• Response filtering to prevent hallucinations or irrelevant output

Common Use Cases

Sensemaking works best when the Fact Finding data is rich and current. Advisors often use it to:

• Prepare for an upcoming meeting
• Summarize progress since the last session
• Identify risks or gaps in readiness
• Prioritize next steps for increasing value
• Generate conversation prompts for exit-team discussions

That said, due to the way we redact PII and protect sensitive data, Sensemaking may not be able to pull in all the data you need to make sense of the business. This is particularly relevant for financial information or personal information about the business owner(s).

Best Practices

• Keep Fact Finding updated — Sensemaking’s accuracy improves as your data does.
• Use built-in prompts as starting points; refine from there.
• Review insights before sharing them with clients to ensure they align with your tone and judgment.
• Remember that while we strive to make sense of the data, it's still important to use your expertise to interpret the insights and make decisions. Even with the guardrails we have in place, all LLMs can hallucinate, so it's important to always review the insights before sharing them with clients.

Treat Sensemaking as augmentation, rather than automation. It helps you think faster and pull in more data, but doesn't replace your expertise.