Founder story

Two Stories That Built BringThisIn

Brian Glen, founder. The WHO pipeline. The medical care failure. And what happened when one met the other.

Act 1

The pipeline

“I had developed a system — a harness — that takes multiple AI agents and teaches them how to think more critically, how to push back on each other’s conclusions, how to annotate their own uncertainty, and how to work together as a team. I then built the infrastructure for these agents to communicate and collaborate.”

— Brian Glen, founder

“My brother works at MUSC, and he called me to ask about integrating AI into some of what his team was doing. I couldn’t explain it all very well, but I said I could do it myself. So I started putting my AI team to the task of analyzing diseases for a WHO cancer genomics consortium wiki. There wasn’t any money in it, but it was rewarding — and I learned a tremendous amount about harnessing a multi-agent AI team to find, sort through, and verify tens of thousands of research articles.”

— Brian Glen, founder

“Developed for a WHO cancer genomics project covering 634 disease entities — same rigor, now applied to your condition.”

Act 2

The care failure

Throughout my life I have had terrible experiences with doctors. When I was first diagnosed with diabetes, my doctor never followed up with me on any actual blood sugar numbers. I noticed they were high, and I pushed for changes in my medicine — and he just accepted whatever I asked for. I switched doctors, and it was the same story all over. I researched doctors near me and found one that I liked. He had availability for new patients, but he worked in the same practice as my current doctor. I requested to switch but was denied that opportunity because it might “hurt my original doctor’s feelings.”

Time and time again I felt like I was dealing with people that were more interested in going to the country club and hanging out with their friends than actually taking care of me.

Even worse, it wasn’t just me. When my daughter was a toddler, she had a regular issue where her elbow would make a popping noise and she would be in terrible pain for a few hours. We went to see a specialist, and while we waited the nurse whispered in our ear that she thinks it’s probably just a case of nursemaid’s elbow, and it will likely resolve on its own as she gets older.

Then the doctor came in. My daughter was on Medicaid at the time — I was an elementary school teacher and due to our income my daughters qualified for that. The doctor walked in. He didn’t ask questions. He didn’t take an x-ray. He didn’t inspect her elbow. He immediately said, “I have been writing books and studying this condition for several years…” He went on to diagnose my daughter with some rare bone disorder and said we would need to put her in a cast for one year, but not to worry because he is the country’s leading expert on this issue.

I told him we would be leaving. We got a second opinion, and it was in fact nursemaid’s elbow. The nerve on the elbow would sometimes pop out of place, and there was a simple wrist movement that could pop it back in. She grew out of it.

I could go on, but let me focus on the most immediate experience.

I recently, in the last year, suffered an injury that led to a diagnosis of Peyronie’s disease — a condition that affects the penis. At first I was confused. I thought maybe I had an infection, but then the pain became unbearable. As it got worse I went to a specialist. The specialist had me meet his nurse first, who scheduled a meeting with him — three months out.

I finally met with him. He spoke to me for all of two minutes and scheduled a Doppler ultrasound. He said he anticipated we would need to use an injection to fix the issue, but he needed the Doppler first. However, they couldn’t schedule the Doppler for three months. I found another location able to do the same Doppler two weeks later, but the doctor’s office told me he refuses to use any Doppler performed by any other office. I would have to wait.

Sadly, I did. My condition worsened — the pain died back but the deformity became significantly worse over that time period. I finally got the Doppler. Again, it was all of two minutes with the doctor.

Two weeks later I went back for the injection, and he said, “In three months we will see how the disease has progressed and do another shot if needed.”

I tried to set up the next appointment but was told it would be difficult to schedule the staff needed, and to put a tentative date three months out. They’d let me know if they could make it work. I haven’t heard back about it yet. As the weeks passed, there was no improvement.

Act 3

Turning the pipeline

“So here I was: no money, no profits, the last eight months of my life spent learning how to harness AI, my personal body breaking in ways that were terrifying, and no improvements to show for it.

And so I pointed my AI team at my own disease.”

— Brian Glen, founder

“What I found was upsetting. The injection I’d been given — verapamil — had no controlled studies showing it improves curvature in Peyronie’s disease. The only double-blind randomized controlled trial showed exactly zero curvature change.

There is also an FDA-approved injection specifically indicated for Peyronie’s disease — collagenase clostridium histolyticum (Xiaflex) — supported by two large double-blind RCTs with over 800 patients. It was never mentioned to me. Not once, in months of visits.”

— Brian Glen, founder

“Five built-in layers of trust: pair collaboration, formal uncertainty tracking, manager oversight, independent editorial verification, and an evidence-to-disk rule. Nothing gets presented as fact until it’s been independently confirmed.”

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The Concord Method

Multiple independent AI agents work in coordinated teams. A research pair analyzes published medical literature and compares interpretations. A writing team translates findings into patient-accessible language. An editorial review verifies citation accuracy. A verification swarm traces every claim back to its PubMed source.

Cooperative AI agents working in pairs, using a notation system that forces uncertainty to be visible — they can’t hide what they don’t know from each other.