---
title: "About · ARC"
description: "ARC (Agentic Research Communications) is led by Rachel Carroll and Antonis Karamitros."
canonical: "https://arc.silba.ai/about/"
markdown: "https://arc.silba.ai/about/index.md"
author: "ARC"
published: "2026-10-04"
modified: "2026-10-20"
language: "en"
---

# Being well‑run is no longer sufficient. You also have to be well‑read.

## Why ARC

The quality of a company's investor communications is not a soft variable. **It is a direct input to how a company is owned and valued.** Companies that state their investment case with precision and consistency attract well-informed investors, build more stable shareholder bases and earn valuations that reflect the business they have built.

What has changed is who reads those communications first. AI research tools now read a company's filings, releases and website before any human analyst does. They are faster than any analyst and less forgiving of ambiguity. Investor relations was designed for a human reader. **Today, the first reader is an AI research tool.**

**Being well read now determines how much of the market can find you.** Institutions, family offices and retail investors increasingly begin their research with AI tools, and autonomous systems are starting to act on what those tools conclude. Index classification and thematic screens also depend on how a company describes its business. A company that is read accurately, classified correctly and described on the right terms is visible to every pool of capital screening for it. A company that is misread, misclassified or left out loses capital it never knew was looking.

ARC brings two careers together to influence how a company is read, by the AI tools investors now use first and by the investors themselves. Rachel leads the client work and the investor relations practice. Antonis leads the research and builds the systems that show how a company is read today. What ARC tells a company about its materials rests on evidence, not opinion.

That is the discipline: read how your company is understood, engineer the investment case so it is understood correctly, and bring it to the investors and voices that shape demand. **Traditional IR reports your story. ARC engineers it.**

## Who leads ARC.

### Rachel Carroll

Principal · capital markets and IR

Leads ARC's client engagements and **investor relations practice**.

**25 years in capital markets** in the US and Europe: on the sell side at Cowen and Société Générale, in-house leading an investor program through a Nasdaq listing, and building Edison Group's IR advisory practice.

[LinkedIn](https://www.linkedin.com/in/rmncarroll/)

### Antonis Karamitros

Principal · research and engineering

Antonis leads ARC's **research program** and builds the systems that measure how AI agents read a listed company. What they find, what they miss, and what changes when the material is engineered.

He has spent 14 years **building complex software systems**, with projects for Bloomberg, Deutsche Bank and Google.

Since 2024 he has built an AI research engine that has analyzed >100 listed companies from their primary sources.

[LinkedIn](https://www.linkedin.com/in/antonis-karamitros-6a550a55/)

## Partner with ARC

If you advise companies, ARC adds the agentic layer to the advice you already give: how AI research tools read your clients, what they get wrong, and what changes it. We collaborate with IR, legal, accounting and investment firms. Request a call to talk about how that fits with your clients.

[Request a call](https://arc.silba.ai/contact/#partner-form)

## Our standards

ARC helps companies be understood accurately. Everything we help a company publish is its own information, clearly attributed and consistent with its filings. Disclosure decisions remain with the company, and our work follows the rules that govern company communications in each market we serve. Sponsored content and paid placements are always identified as such, and we do not engage in undisclosed promotion. We make accurate information easier for AI systems to find and verify; we never use hidden content or techniques designed to mislead them. Client relationships do not shape our research findings, we disclose when a client is part of a study, and we publish our methods with our research so others can test them.