Company AI · On-prem · Self-evolving

Make your company's legacy evolve.

Your company's second brain — a private AI that runs inside your firm, learns how your experts work, and keeps their judgment evolving.

Outer loop — expert corrections set the target Inner loop — improves itself on every trace
01

Your company's second brain

One memory of how the whole firm works — every channel, expert, and year.

02

Inside the firm, on-prem

Runs on your own hardware. Client and patient data never leave the building.

03

Your legacy, evolving

Senior judgment becomes a company asset that improves with every correction.

100%
On-prem · private
1–2 mo
Free pilot, one workflow
2.1×
Productivity · internal pilot
+56%
Small beats frontier · ACL 2026

01 · Why now

AI today is a personal tool, not a company asset.

Know-how goes into a prompt, an answer comes out, and tomorrow it starts over. The firm learns nothing — and the know-how still walks out the door.

Know-howin one person's head
Promptre-typed every time
Black boxhidden reasoning
Outputtake it or leave it
repeat tomorrow

No memory

Every session starts from zero.

No lineage

No record of why an output came out that way.

No company-level gain

Individual use; the firm's workflow doesn't move.

02 · Deploy first

We built a small law firm first. Now we deploy inside real ones.

In LawFlow (COLM 2025), trained law students ran business-formation matters in a simulated small firm — and we recorded every step of their decisions.

Drafting an LLC operating agreement — every step recorded and mapped to a workflow graph.
LawFlow setup: law students and clients in small-business formation scenarios; legal processing tasks such as issue identification, note taking, template selection, research, drafting, and client conversation lead to an operating agreement draft reviewed by peers.
The simulated firm: clients, law students as counsel, peer review.

What we found

Experts are selective. AI is exhaustive.

People plan in layers, follow the branch that matters, and loop back when something changes. LLM agents plan flat and run straight through. The expert's value is in what they don't do.

The two maps below are the same matter — one walked by a person, one by an LLM agent.

Human workflow: a selective, depth-oriented path that follows the relevant branches and loops back when needed.
Human — selective, loops back
LLM agent workflow: a wide, exhaustive, breadth-first path through the task.
LLM agent — exhaustive, breadth-first
Then · research

A simulated firm

Judgment lives in the process — and the process lives inside the firm.

so this time
Now · deployment

We go to the company.

Engineers on-site, a light recorder on your machines, a model that grows inside your walls.

  1. 01 · Record

    Capture the work itself

    Screen, keystrokes, and traces — with consent. No change to how experts work.

  2. 02 · Structure

    Map how the firm works

    Traces become a map of your real workflows in week one.

  3. 03 · Optimize

    Improve on every trace

    Experts' corrections set the target. A human always signs off.

  4. 04 · Evolve

    Grow your own model

    Your model, on your hardware — better every month.

03 · Technology

Recursive self-improvement, with your experts, on-prem.

One research question runs through our work: how does a small model keep improving on expert work that no benchmark can grade? Three threads answer it — expert work as data, learning to discover, and recursive self-improvement — and together they form the dual loop below.

Outer loop — your experts correct what comes out. Those corrections become the target, and a human signs off. Inner loop — between corrections the local model keeps improving itself on your traces.

Research behind it

Five peer-reviewed papers at COLM, ACL and NeurIPS, plus one open-source platform, from the founding team's lab.

Recursive self-improvement inside a firm starts with the work itself, not a benchmark. We record how experts actually work — lawyers planning a business-formation matter LawFlow, COLM 2025, researchers writing papers keystroke by keystroke ScholaWrite, ACL 2026, people at work on video SERUM, COLM 2026 — and turn it into traces a model can learn from.

Between corrections, the local model improves itself: it learns to discover from evolutionary search EFT, NeurIPS 2026; Open Galapagos, open source, and one meta-operation keeps adding layers until the gains stop Metan, NeurIPS 2026. Experts correct what comes out, and a person signs off. All of it runs on your hardware; nothing leaves the building.

04 · On-prem scaling law

The bigger problem isn’t intelligence. It’s missing context.

Your context is scattered across channels, people, and the work itself — and locked inside the firm. Deployment connects the dots; connected dots reveal the workflow.

Your domain

0 task traces

Connecting the firm’s channels, people and process data on-prem. The first suggestion arrives when the system has seen enough of the work.

General model, no context Emergent behavior

Discover → Optimize → Coach: capability on your own work compounds as context accumulates on-prem.

Illustrative. Capability on your own work compounds as context accumulates on-prem.

Every card is a suggestion the system raises on its own — you approve, edit or reject it. Drawn from the workflow patterns we catalog across verticals; firms anonymized, figures illustrative.

05 · How we start

One workflow. One person. Free for 1–2 months.

Engineers on-site, a lightweight recorder, and approval gates — a human signs off before anything goes out.

Weeks 1–2

On-site discovery

Pick the workflow; install the recorder with consent.

Months 1–2

Free pilot

One workflow, one person, measured against your rubric.

~Day 60–90

Production

On your hardware, benchmarked against your baseline.

Ongoing

Compounding

Better every month. Month 12 is not month 1.

Finance & M&ALegal & IPHospitals & clinicsInsuranceAccounting & taxWealth management

In the field

Design partner, pilots, and firms in conversation

  • M&A advisory5-person boutique · US
  • Patent & IP consulting~100 people · Korea
  • Dental clinic3 dentists · Korea
  • Patent law firmMulti-partner · Korea
  • Eye clinicPrivate practice · US
  • Medical devicesGlobal enterprise · US
  • IP law firmNational · US
  • Information servicesGlobal enterprise
  • Law firmLarge · Korea

06 · Team

Team

Dongyeop Kang

Dongyeop Kang

Founder & CEO

  • CS faculty, University of Minnesota
  • Director, Minnesota NLP Group · 75+ papers
  • PhD CMU · Postdoc UC Berkeley
  • Research at Facebook AI, AI2, Microsoft
James Mooney

James Mooney

FDE Lead

  • CS PhD student, University of Minnesota
  • Ex-Grammarly · leads on-site deployment
Shady Ahmed

Shady Ahmed

ML Engineer

  • BS, University of Minnesota
  • Forward-deployed ML engineering

Tech Lead

  • FAANG · 5+ years
  • FinTech · 5+ years
  • Statistics PhD
  • UC Berkeley · KAIST alum
Stealth

Director of Research

  • FAANG applied scientist
  • CS PhD
  • KAIST alum
Stealth

AI Researchers

Part-time

Stealth

Advisory board

Legal & IP · infra · AI startups

Stealth

We’re hiring

Product Owner / Senior Product Manager

Own what we ship into live deployments: turn what our engineers see inside a firm into a product the next firm can run. On-site with design partners, close to the loop.

  • Full-time · Minneapolis, or on-site with partners
  • B2B or enterprise software, 5+ years
  • At home in regulated work — legal, medical, finance
Apply — admin@cogmenta.biz

Supported by

  • NVIDIA Inception Program member

Build your company's second brain.

A free 1–2 month pilot. Our engineers come to you. Nothing leaves the building.