Introducing Iris
Modern sales teams are full of highly capable people doing work they should not have to do.
Instead of building relationships, earning trust, and closing deals, they spend their days digging through spreadsheets, clicking through public records, cross-referencing LinkedIn, and trying to figure out who actually makes the decision.
Too often, they are doing all of that work on top of the exact same stale database their competitors bought yesterday.
That model is broken.
At the same time, the market is flooded with AI tools. But AI alone does not solve this problem. Reasoning is no longer rare. Everyone has access to powerful models. What most teams do not have is proprietary, timely, decision-ready data.
That is the real bottleneck.
An LLM does not automatically know who filed a building permit yesterday, which company just changed ownership, or which operator is actively looking for a new vendor. That information still lives across fragmented websites, filings, public records, and the hidden corners of the web.
In a world where the models are commoditized, the advantage is no longer the AI itself.
The advantage is the data you can find, the signals you can interpret, and the speed at which you can turn them into action.
That is why we built Iris.
Iris is an intelligence system that finds the revenue hiding in plain sight so your team can stop hunting through noise and start talking to the right people at the right time.
Core idea
The advantage is no longer the model. The advantage is finding the right signal before everyone else does.
Most outbound systems start with a list.
You buy 10,000 contacts, load them into a sequencer, and hope a few happen to be in the market. It is inefficient, expensive, and brutal on good salespeople.
Iris flips that model.
Instead of starting with a static list, Iris starts with a live signal. Here’s a few examples:
A company expands into a new office.
A property changes hands.
A permit gets filed.
A fund launches.
A management team turns over.
A regulatory event happens.
These are the moments when a buyer is most likely to act.
When you start with the signal, you do not need to spam 10,000 people. You need to find the 10 who actually have a reason to care right now.
Finding a signal is only the first step.
Take a common example in commercial real estate. A property record shows ownership under an LLC. The public filing lists a registered agent. A human rep clicks around for 45 minutes, reaches a law firm, and hits a dead end.
Iris does not stop there.
It investigates.
It traces the filing, evaluates whether the registered agent is just legal cover, pivots to other public records, cross-references addresses, resolves identity through additional sources, and works outward until it finds the real person behind the asset.
The result is not just a name.
It is a verified buyer, real context, and a clear reason to reach out.
What reps get
Iris does not hand sales teams a list. It hands them a reason, a contact, and the timing that makes outreach relevant.
Most enrichment workflows waste money by enriching everything and filtering later.
Iris does the opposite.
It qualifies while it hunts.
As soon as the system sees a red flag, it drops the prospect. If it sees a strong fit, it keeps going deeper. That means less wasted spend, better lead quality, and a much more efficient pipeline.
You are not paying to deeply enrich junk.
You are paying to surface the few opportunities that are actually worth attention.
Human researchers learn over time. They remember which sources are useful, which paths are dead ends, and which signals actually predict outcomes.
Most AI systems do not. They start fresh every time.
Iris was built with memory.
Every search, every source, and every successful path teaches the system something. Over time, Iris gets sharper about where to look, what to ignore, and how to resolve the kinds of problems your market produces again and again.
That means month six is smarter than month one.
We did not build Iris to create another dashboard.
We built it to remove friction from the work that matters.
Your best people should not spend their day cleaning bad data, chasing dead ends, or stitching together context by hand. They should spend their time talking to real buyers, understanding real needs, and closing real business.
Iris handles the heavy lifting in the background:
- finding live signals
- investigating who is behind them
- qualifying what matters
- delivering deal-ready intelligence into your workflow
So your team can do what humans do best.