Arcten

What do they actually do

Arcten provides an embeddable agent component and SDK to add an AI agent inside existing applications. The product focuses on deep in‑app integrations (tool calling against customer functions), retrieval over large document sets, guardrails, observability, and edge deployment so teams can ship production‑ready agents with less custom glue code (arcten.com).

They are applying this agent infrastructure to financial research use cases, helping teams screen and analyze filings, transcripts, and related datasets to surface opportunities and monitor competitors with concise, verifiable outputs (YC profile, arcten.com).

Who are their target customer(s)

• Buy‑side research analysts (hedge funds / asset managers): They spend most of their time reading SEC filings, transcripts, and reports and struggle to turn volume into timely, defensible insights; manual screening makes misses and delays more likely (YC profile).
• VC / PE scouts and deal teams: They need to detect early signals across large public filings and adjacent datasets; slow, manual review creates blind spots that lead to missed or late opportunities (YC profile).
• Fintech and product teams building investor‑facing apps: Adding reliable agents that integrate deeply with an app takes weeks of engineering and brittle orchestration; teams want a faster, safer way to ship embedded agents (arcten.com).
• Investor relations / corporate strategy teams at public companies: They need continuous, accurate monitoring of competitor filings, 8‑Ks/10‑Ks, and insider activity, with contextual alerts and concise impact summaries that fit their workflow (YC profile).
• Compliance and research‑ops teams at financial firms: Ingesting and normalizing changing document formats is brittle; they need guardrails, auditability, and integration choices to embed agents into regulated workflows (arcten.com).

How would they acquire their first 10, 50, and 100 customers

• First 10: Run high‑touch, scoped pilots with buy‑side and deal teams on their own document sets, plus one rapid embed for a fintech product team; use YC/warm intros, define one success metric per pilot, and convert wins with a light migration/onboarding plan (YC profile, arcten.com).
• First 50: Productize the pilot into a repeatable package with templates (filings monitoring, insider alerts, transcript summaries) and a sandbox tier; pair targeted outbound to hedge funds with webinars, short case notes, connectors, and a small set of partners to scale trials.
• First 100: Standardize onboarding/support and compliance packs, launch a partner program and connector portal, add clear pricing tiers, and run ABM to mid‑market asset managers/PE while expanding sales, success, and implementation capacity to support concurrent pilots at predictable SLAs.

What is the rough total addressable market

Top-down context:

Arcten sits at the intersection of regtech/compliance and enterprise AI‑assistant software. Published 2024 estimates put regtech at roughly $15.8B and AI‑assistant software near $8.5B, implying a combined pool in the mid‑$20B range that Arcten’s platform can compete within (Fortune Business Insights, Grand View Research). Broader finance context underscores buyer scale and data flow: asset managers oversee ~$128T and hedge funds ~$4.5T AUM, and there are ~58k listed companies generating continuous filings (BCG, Reuters, World Bank/WFE).

Bottom-up calculation:

As a practical near‑term SAM, assume 3,000 buy‑side/PE/VC teams adopting at a $50k average ACV (~$150M) plus 2,000 fintech/IR/compliance teams at $25k average (~$50M), yielding roughly ~$200M in obtainable spend to start; this sits inside the larger regtech + AI‑assistant pools and can expand with broader use cases (World Bank/WFE for scale).

Assumptions:

• Arcten targets teams willing to replace manual screening or brittle in‑house scripts with a managed agent + integrations; average ACVs reflect early production deployments.
• Seat counts and team budgets vary widely; estimates reflect blended small/mid‑market buyers more than top‑tier enterprises.
• Expansion paths (more seats, more data sources, adjacent workflows) can lift ACVs beyond initial averages as trust and integration deepen.

Who are some of their notable competitors

• AlphaSense: Widely used research platform for filings, transcripts, and premium content with AI‑assisted search and monitoring; overlaps with Arcten’s goal of surfacing investment signals from large document sets.
• BamSEC: SEC filings and transcripts platform focused on fast document access, screening, and monitoring; relevant where teams want timely filing insights and alerts.
• Tegus: Research platform centered on expert transcripts, financials, and workflows; competes for analysts’ time and attention across diligence and monitoring.
• LangChain: Open‑source framework for building retrieval and agent workflows; an alternative for teams building and maintaining agent infrastructure in‑house.
• OpenAI Assistants API: A general‑purpose assistant/agent runtime with tool calling and retrieval features; a base layer some teams use instead of a specialized, embeddable agent platform.