Operating in stealth Classification: Confidential Ref: CGM·LOOP·SEED Access: By invitation
Cogmenta · LoOp Framework

Make your company AI‑native.

Cogmenta builds LoOp — on-premise AI that learns from your experts and compounds with every correction. Built for work that cannot be outsourced.

Deployment
100%
On-prem · private
Time to go-live
60–90 d
Discovery → production
Delivery
Full-stack
AI framework
Correction loop
Compounding weekly

01 Vision

Wherever an expert corrects a machine, there is a workflow to optimize.

Record · annotate · build ontology · close the loop. Same architecture, every domain where judgment is the bottleneck.

Now · Short-Term Wave 01 01

Workflow optimization for regulated expert firms.

Per-firm, on-prem, compounding — finance, legal, medtech.

Midterm · 2–4 Yrs Wave 02 02

Vertical AI agents across regulated industries.

Consent-bound ontology becomes training fuel that warm-starts every new firm.

Long-Term · Moonshot Wave 03 03

From knowledge work to physical expert work.

Same loop, new sensors — labs, factories, clinics, kitchens.

Internal Memo · Thesis

02 The Thesis

Agents only compound where experts correct them.

Horizontal LLMs plateau. Workflow-specific systems — tied to your data, ontology, and corrections — get measurably better every week.

01 / VERTICAL

The agent era is vertical.

Generic models plateau on domain reasoning. Winners know one company's workflows, ontology, and corrections deeply.

02 / MOAT

Private data is the moat.

The work that matters can't go to a public service. Private deployment, behind the firewall, fully auditable.

03 / FLYWHEEL

Corrections are the flywheel.

Every expert edit is a training signal. After 12 months, the system embeds judgment no competitor can replicate.

03 How LoOp Works

Complex workflows become ontology. Ontology compounds into efficiency.

LoOp watches expert work, structures it as ontology, and lets agents optimize against it — edit by edit.

LoOp · ontology pipeline
M1M3 M6M9 M12
Step 01
Complex workflow
Step 02
Structured ontology
Step 03
Compounding efficiency

04 Our Product

LoOp

The workflow optimization system, on your own stack.

Full-stack on-premise AI with a workflow optimization layer. Learns your decisions, your judgment — without your data ever leaving the building.

F·01

Workflow optimization system

Agentic AI on your real workflows. Experts only touch what needs judgment.

F·02

On-prem full-stack AI framework

Hardware, inference, models, pipelines, governance — one private system. Air-gap optional. Zero public-cloud dependency.

F·03

Compounding correction loop

Weekly prompt optimization, monthly retrain on your corrections. Month 12 ≠ month 1.

F·04

Audit-ready by default

Every decision traceable. Clears disclosure and explainability standards in regulated, safety-critical work.

05 Who It's For

Built for the workflows that can't leave the firm.

Wherever expert work runs on private data or private machines, the same loop applies.

Sector 01

Finance

IB, M&A, asset management, equity research — deal data never leaves the firm.

Sector 02

Legal

Contracts, diligence, IP, litigation — privilege preserved.

Sector 03

Healthcare & Medtech

Clinical docs, regulatory, R&D — HIPAA-grade by design.

Sector 04

Audit & Consulting

Fieldwork, synthesis, decks — the audit trail is the deliverable.

Sector 05

Physical Ops & Labs

Factories, labs, clinics — same loop, new sensors.

Engagement Protocol

06 Engagement

A phased partnership built on trust and results.

Fixed scope, fixed deliverables, no surprises. The loop keeps compounding long after go-live.

Phase 01
Discovery & Trust
~3 weeks · on-site

Embed with experts. Map workflows, audit data, design architecture.

DeliverableOpportunity report + blueprint.
Phase 02
Build
~5 weeks

Ontology, pipeline, expert-tuned models — trained on your sessions, not the public internet.

DeliverableOntology, models, pilot-ready system.
Phase 03
Deploy
~4 weeks

Production cutover. Expert-in-the-loop on. Benchmarks vs. baseline.

DeliverableProduction system + 30-day hypercare.
Phase 04
Compound
Ongoing retainer

Weekly prompt opt · monthly retrain · quarterly architecture review.

DeliverableA system that gets better every month.

07 Team

A small team. The right one.

Frontier AI research, applied ML at scale, and operating experience inside regulated industries.

Co-Founder & CEO

AI Research · Architecture
CMU · Berkeley · UMN

CS Ph.D. Postdoc in human-centered AI; publishes on systems that augment expert reasoning.

Co-Founder & CSO

Quant Modeling · GTM
Amazon · Finance

Statistics Ph.D. Research scientist · applied ML at scale · prior ops in regulated finance.

Head of ML Engineering

Model Training · Eval
UMN · KAIST · Amazon

AI Ph.D. Fine-tuning, eval harnesses, private inference at scale.

Embedded Client Lead

Workflow Capture · Delivery
UMN · Prior AI startup

Applied AI + prior founder. Embeds on-site to translate workflow signal into the layer.

Hires by referral. Deliberately small. Get in touch.

Request Access

We work with a small number of companies at a time.

Stealth · by invitation. If your team is rethinking how expert work gets done where data can't leave the firm — we'd like to hear from you.

Request a private briefing