Teemo Recipes are now live. Join us for a first look at TeamOhana’s newest AI capability, built to help Finance, HR, and Talent teams get faster insights from their workforce data. Recipes combine the flexibility of custom reporting with the intelligence of an AI Analyst.
Recipes help you create the reports your team actually wants to use, and then it reads those reports for you to surface the most important insights, patterns, and outliers. The result is a faster, easier way to move from workforce data to action without the manual digging, dashboard wrangling, or analyst dependency.
We'll cover how Recipes work and how to best utilize them, highlighting the following recipes:
- Hiring Slippage - See where hiring timelines are slipping so teams can quickly identify delays, understand where execution is falling behind plan, and take action before missed targets compound.
- Stalled Roles - Get an AI-generated summary of where roles are stuck, including which departments or recruiters have the most stalled headcount and which openings have been stalled the longest.
- Span Exceptions - Identifies divisions and managers whose span of control falls outside benchmark ranges, showing both current and forecasted status.
Key Highlights
What you’ll learn
- How Teemo Recipes work
- How Start Date Drift tracks the gap between a role's originally planned start date and its current target date
- How Stalled Roles identifies open headcount that hasn't seen meaningful recruiter activity, even when start dates haven't moved
- How Hiring Slippage quantifies the financial impact of headcount that has moved out of its originally planned quarter, using fully loaded compensation
- How to request custom Recipes and what's coming to the platform for self-serve recipe creation
About the speaker
Virginia Hyland is the Director of Solutions Engineering at TeamOhana. She works directly with customers to translate their workforce data challenges into practical solutions, and plays a central role in bringing new product capabilities to market through live demos and customer enablement. Her work sits at the intersection of product, customer success, and data fluency.
Takeaway #1: Why Recipes separate deterministic data from AI
Recipes aren't purely AI-generated because, as Virginia puts it, AI is bad at math. The architecture reflects that directly.
The foundation of every Recipe is a deterministic report. That means the same calculations run the same way every time. The columns don't change. The logic doesn't drift. When someone hits refresh, they get the same structure populated with the most current data from TeamOhana—whether that's from the hiring plan, the HRIS, or the ATS. That predictability is what makes Recipes trustworthy enough to share with a CFO or put in a board report.
AI layers on top of that foundation to do what it's actually good at: reading across variables, identifying patterns, and surfacing what's most worth your attention. The generative insights at the top of each Recipe distill what might take 30 minutes of spreadsheet scanning into a few headlines. For anything that warrants deeper exploration, the embedded Teemo agentic chat is available directly within the Recipe to answer follow-up questions in natural language.
The architecture is a deliberate design decision to make AI-driven workforce analysis something teams can trust and act on.
Takeaway #2: How a "Holy Grail prompt" becomes a reusable recipe
Recipes grew out of how teams were already using Teemo. Teams would work through a series of prompts—adjusting phrasing, narrowing scope, refining the output—until they landed on exactly the view they needed. Maybe it was a stacked bar chart the CHRO asked for every month, or a report that went into the board deck each quarter.
Once a team lands on the right output, they have everything they need to turn it into a Recipe. TeamOhana saves the underlying query so the report can be refreshed on demand with current data—no prompting required. The structure and calculations stay consistent, while a fresh layer of AI-generated insights surfaces what's changed.
The result is the difference between a one-time analysis and a reliable resource teams can count on for quarterly business reviews.
- Exploration phase: Use Teemo's agentic chat to iterate on the right question and the right format
- Graduation phase: Save that output as a Recipe for on-demand refresh, with AI insights layered on top
- Deeper analysis: Use Teemo—embedded inside the Recipe—to follow up on anything the insights surface
Takeaway #3: Recipes surface what standard headcount reports miss
Each recipe in this demo finds something a typical headcount report isn't designed to catch. A hiring plan might show 30 roles in flight—but won't tell you that some haven't moved in months, that others have quietly drifted a quarter behind schedule, or that a significant portion of projected hiring cost has shifted out of the current period.
Start Date Drift makes visible the gap between when a role was planned to start and when it's actually expected to. Stalled Roles surfaces open headcount that hasn't seen any activity, even if the dates look fine on paper. Hiring Slippage quantifies the dollar impact of that movement—showing Finance exactly how much cost has been deferred and which divisions are driving the variance.
Recipes also pull from forecasted headcount, giving teams a view into what's expected to happen, including projected budget impact, anticipated hiring timelines, and future span of control.
"When you think about the types of recipes or the types of queries you want to run, think about not only doing analysis on your current headcount, but also on what's projected to happen in the future." — Virginia Hyland
That forward-looking capability is what gives Recipes their planning value.
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