The Ember Difference: 2 Years of training before GTM
Nov 25, 2025
Why Ember Spent 2 Years in Stealth Before Going to Market
The healthcare staffing industry has entered a new era — one where speed, accuracy, and clean data determine who wins and who falls behind. As agencies search for tools that can help them move faster in a competitive market, AI has quickly become the hottest topic of conversation.
But here’s the truth most people aren’t talking about:
Not all AI is created equal.
And accuracy doesn’t happen overnight.
This is exactly why Ember spent nearly two years in stealth mode before launching publicly in the summer of 2025.
We weren’t waiting.
We were building.
The Problem With Most AI Tools in Staffing
The rise of AI has created a rush of new tools entering the market — some exciting, many unproven. The challenge? Most of these platforms rely on generic AI models that were never trained for the complexity, urgency, and workflow-specific challenges of healthcare staffing.
Here’s what that means:
They don’t understand healthcare staffing terminology
They can’t interpret recruiter notes correctly
They misclassify jobs, specialties, and candidate attributes
They surface weak or irrelevant matches
They lack the precision needed to actually improve recruiter speed
And it’s not because the founders aren’t smart — it’s because AI models become accurate only with time, volume, and industry-specific training.
Which brings us back to Ember.
The Ember Difference: 2 Years of Training Before Day One
When we began building Ember, we made a deliberate choice:
Don’t launch until the model is extremely accurate.
Don’t release a product until it’s ready for real recruiter workflows.
So instead of rushing to market, we spent two years:
Training the model on healthcare staffing-specific data
Reading thousands of recruiter notes, text messages, and call logs
Learning credentialing terms, pay language, location preferences, and shift patterns
Building deeply accurate field extractions
Testing matches against real open jobs
Validating outcomes with real recruiters, daily
This wasn’t theoretical work.
This wasn’t “AI for staffing” in name only.
This was real, hands-on, applied training with our alpha and beta clients — agencies who trusted us early and helped us refine Ember into the model it is today.
And those same early partners?
Today they’re thriving on Ember.
Because mature, trained AI makes recruiters faster.
Immature AI slows them down even more.
A Word of Caution as You Evaluate AI Tools
As more AI tools enter healthcare staffing, here are a few things to watch for:
1. Has the model been trained on healthcare staffing data specifically?
If not, the tool will struggle with accuracy and relevance.
2. How long has the model been training?
If the answer is “a few months,” expect imprecision.
3. Are they offering free trials to everyone?
That’s often a sign they’re using your agency to train their model.
4. Does the AI understand recruiter notes, pay details, and job specifics?
Generic models rarely do.
5. Are they claiming massive AI capabilities without real client outcomes?
Results matter more than hype.
These aren’t warnings — they’re lessons learned from our own journey.
Before we ever launched Ember publicly, we were doing free trials, hands-on testing, and co-building with early clients because that’s what it takes to build an accurate model.
We’ve done the hard part already.
Why Accuracy Matters More Than Anything
AI in healthcare staffing affects:
Submittal speed
Data quality
Candidate experience
Recruiter experience
Fill rates
Job board spend
Revenue
If your AI doesn’t understand your data, doesn’t interpret notes correctly, and doesn’t match candidates accurately — it becomes noise, not support.
Accuracy isn’t optional.
It’s the difference between winning and losing a submission.
And that’s why Ember took two full years before launching.
Where Ember Is Today
After years of training and refining, Ember is now:
🔥 Cleaning agency databases historically
🔥 Keeping data clean in real time
🔥 Surfacing instant, accurate matches when jobs hit
🔥 Notifying recruiters in Teams, Slack, or on-screen
🔥 Allowing instant SMS/email job sharing from the match
🔥 Elevating recruiter speed and reducing admin work
🔥 Supporting some of the industry’s top-performing agencies
We didn’t build a toy. We built an engine — powered by accuracy and trained by the industry itself.
Ready to See the Difference?
If you’re evaluating AI tools for 2026, here’s the most important thing to ask:
How long has this product been training on healthcare staffing data?
If the answer isn’t “years,” the accuracy won’t be there.
With Ember, you get a model built through years of training, real-world testing, and hands-on refinement with high-performing agencies.
If you’re ready to see it in action: