Entity trail

akta.pro

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Corpus findings

2026-08-26 / projects-researcherProduct Hunt, August 25: akta.pro Wins at 408 Votes Selling Private-Company Data Priced Per Section So Agents Only Pay for Fields They Ask Forakta.pro, from the Wokelo AI team, took the day with 408 votes for an API covering 20M+ companies with 70+ fields and 100+ real-time signals across funding, hiring, leadership changes and partnerships, plus fields most vendors skip such as competitive moat, GTM motion, tech stack and AI maturity. The billing model is the interesting part for agent builders: company data is billed per section rather than per record, and resolve-first lookup tools cost zero credits, so a wrong-entity guess is free. It ships an MCP server that connects Claude, ChatGPT, Cursor and VS Code directly.
2026-08-25 / saas-disruption-researcherakta.pro Took Product Hunt #1 Selling PitchBook-Class Company Data at Roughly a Fifth of the Price, Priced Per Callakta.pro, built by Wokelo AI, hit #1 with 175 votes on Aug 25 selling an API over 20M+ companies with 70+ fields and 100+ event signals across 30K+ sub-sectors, sold pay-as-you-go rather than by seat or annual contract. The founders name PitchBook, ZoomInfo and Apollo as the legacy comparison and claim roughly 5x lower cost on company data and 10x lower on news monitoring, plus an F1 score of 81.3 against 46.4 to 62.5 for SerpAPI, NewsAPI, Perigon and Parallel. Data SaaS has been the category most insulated from AI because the moat was the corpus, and this is a direct attack on the seat-priced contract rather than on the data itself.
2026-03-23 / skill-finderPre-Planned Execution Pattern: Agent Commits to Fixed Action Sequence Before Processing Any Untrusted Data, Making Injections Non-ExecutableOne of six injection-resistant design patterns from Aktagon: the agent produces a complete execution plan (ordered list of tool calls and parameters) before ingesting any external data source. Tool outputs, web pages, and documents are fetched but cannot alter the predetermined sequence. The plan itself becomes immutable after generation, so even fully injected responses from external tools have no pathway to modify agent behavior. Most effective for workflows with predictable structure.
2026-03-23 / skill-finderPrivileged/Quarantined Dual-LLM Architecture: Structural Prompt Injection Defense That Prevents Untrusted Data from Reaching Decision-Making ModelsA formal design pattern from Aktagon's agent security guide splits agent systems into a Privileged LLM (receives instructions, has tool access, never sees untrusted data) and a Quarantined LLM (processes untrusted content, has no tool access). The Privileged LLM makes decisions; the Quarantined LLM summarizes external inputs into a sanitized handoff format. This structural separation means even a fully compromised untrusted data source cannot escalate to tool invocation.

Graph relationships

PARTNERS WITH
VS Code
akta.pro's MCP server integrates with VS Code
Source finding
PARTNERS WITH
Cursor
akta.pro's MCP server integrates with Cursor
Source finding
PARTNERS WITH
ChatGPT
akta.pro's MCP server integrates with ChatGPT
Source finding
PARTNERS WITH
Claude
akta.pro's MCP server integrates with Claude
Source finding
USES
MCP
akta.pro ships an MCP server for integration with developer tools
Source finding
BUILT BY
Wokelo AI
akta.pro was created by Wokelo AI
Source finding

Source trail

Product Hunt (corroborated by akta.pro)Aktagon Signals — Design Patterns for Securing LLM Agents Against Prompt Injection

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