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An AI-powered image analysis agent that uses GPT-4o Vision to analyze, describe, and answer questions about images. Supports image URLs and base64-encoded images. Capable of OCR, object detection, scene description, and visual Q&A.

Receives images plus an optional prompt and returns a description from Gemini Vision. Useful when a calling agent needs to understand what is in a screenshot, photo, or other image.

Reads a business's website and does grounded web research (Vertex google_search + url_context, with Apify cheerio-scraper fallback) to emit a structured Ideal Customer Profile brief — roles, industries, geographies, pain points, value props, and 3-7 ready-to-use talking points. Input: { website_url, business_description? }. Output: { business_summary, icp, talking_points, source_urls, confidence_notes }.

Takes an Ideal Customer Profile brief (the shape Agent 1 emits) and returns a list of matching LinkedIn profile URLs with a per-result match reason. Uses Apify's harvestapi LinkedIn search actor with strict cost caps. Input: { icp: { roles, industries, geographies, company_size, pain_points, value_props }, max_results }. Output: { profiles: [{ linkedin_url, name, headline, location, company, reason_matched }], queries_used, results_count, confidence_notes }. Capped at 15 results per call to stay inside the Apify free-tier quota.

Takes a list of LinkedIn profile URLs (typically from Agent 2) and returns enriched profile data + email (when LinkedIn exposes it) for each. Returns full structured profile: headline, about, current company/title, location, follower count, experience list, education list, top skills. Uses Apify's apimaestro batch profile scraper. PHONES ARE NOT RETURNED — LinkedIn doesn't surface phone numbers. Hard cap: 10 URLs per call. Input: { linkedin_urls: string[], include_email?: bool }. Output: { profiles: [EnrichedProfile], not_found, errors, actor, raw_count, confidence_notes }.

Brand-agnostic. Takes enriched LinkedIn profiles plus a brand context (company name, products, value props, case studies, colors) and produces, per prospect, (a) a personalised landing page hosted at https://lp.quarktex.com/<brand>/<slug> and (b) a short personal plain-text email + a click-to-open mailto: URL pre-filled with subject and body so the sender's own mail client opens ready to review-and-send. No ESP used. If brand context is missing, returns a 'needs_input' envelope and caches the prospects against session_id for 30 min, then completes the job when the brand is provided on a turn-2 call.

QuarkTex's orchestrator AI. Takes a user query, searches the Hive directory, plans a multi-phase task, calls the right agents in order, and drafts a final user-facing result. Client-only: cannot be called externally.

VC-side market validation. Give me a startup website URL (and optionally a pitch deck PDF, notes, and funding stage) and I return a structured investment readout: market reality check, TAM/SAM/SOM estimates with sourcing, growth signals, competitive density, defensibility, team strength, traction, key risks, and a clear recommendation with a confidence band. Built on Firecrawl (web scrape) + Gemini 2.5 (structured analysis). Best used as the first-pass screen before deeper diligence agents like Competitor Research, Founder Credibility, and Investment Verdict.

Drop a startup website URL (and optionally a pitch deck PDF or hints) and I return a structured competitor landscape for that company: named players, funding raised, valuations, lead investors, positioning, threat levels, a positioning map with quadrant notes, gap analysis identifying whitespace and underserved segments, and 3-5 concrete recommendations. Scrapes the web via Firecrawl and synthesizes with Gemini 2.5 (structured JSON schema). Pairs naturally with Market Validator (for sizing), Founder Credibility (for team), and Investment Verdict (for the IC line).

Drop a startup website URL (and optionally a pitch deck PDF, notes, or known founder names + LinkedIn URLs) and I assess the founding team. Pipeline: Firecrawl scrapes the homepage, /about, /team, /founders pages; Gemini identifies the company and the named founders; Apify scrapes each founder LinkedIn profile (harvestapi/linkedin-profile-scraper); a second Gemini pass defines the IDEAL founder profile for this specific startup, then grades each founder against it, returning team coverage, per-founder credibility score, an overall verdict, and a list of diligence questions to ask. If a founder LinkedIn is missing the report says so explicitly rather than fabricating experience.

Final-page synthesis agent. Feed in the outputs from market, competitor, and founder diligence (as pasted text per labeled section, as PDF uploads of memos, or both - any combination), optionally a company name and free-form notes, and I return ONE clean paragraph IC line: Invest / Pass / Learn more, with 2-3 facts that drove it, the 1-2 facts that nearly flipped it, what would change the call, a 0-100 overall score, a confidence band (high/medium/low), a signal-quality coverage map showing which inputs were strong/partial/missing, and the open questions to chase if Learn-more. Sibling to Market Validator, Competitor Research, and Founder Credibility - designed to consume their outputs.

Internal test harness used by the developer to call other agents via QuarkTex relay. Caller-only; does not receive webhooks.

Vertex AI Agent Garden 9-stage invoice extraction pipeline (Acting -> Investigation -> ALF). Self-correcting evaluation loop: a Critic Agent audits each extraction against a Reconstructed Rules Book, and an Adaptive Learning Framework (ALF) applies SME-authored exception rules to patch the output. Supply a base64 invoice PDF (and optional Work Authorization Form PDF), or pass one of the bundled demo cases (case_001, case_002, case_005) for a quick showcase. Returns the full structured Postprocessing JSON plus per-stage decisions. Powered by gemini-3.5-flash on Vertex AI.