Systems Running in Production Right Now

MaxReach runs content production from end to end. The system takes a topic, researches it across the web, writes the article, runs it through fact-checking and AI detection, applies optimization, and publishes to 4 platforms simultaneously.

32 agents handle different stages. Trend scouts monitor industry signals. Query planners find keywords. Writers produce drafts. Editors polish. Quality gates catch errors before anything goes live. Each agent has a specific role, specific instructions, and specific quality criteria.

The system includes budget tracking per article, approval gates where a human reviews before publishing, and automatic social media derivatives from every article. One operator manages what traditionally requires a content team of 5 or more.

Stack: Node.js, Claude API, Supabase, n8n

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OpsForge replaced the operational overhead of a 25-person agency with AI agents organized into departments. HR handles onboarding checklists and document generation. Client delivery tracks projects, deadlines, and deliverables. QA runs automated quality checks on every output. Finance generates invoices and tracks payments.

Each department has its own agent team with defined responsibilities. Agents communicate through a shared database, escalate exceptions to humans, and log every action for audit. The dashboard shows real-time status across all 7 departments: HR, client delivery, QA, finance, marketing, operations, admin.

The system was built to answer one question: what happens when every repetitive operational task has an AI agent responsible for it, and a human only steps in for decisions that require judgment?

Stack: Next.js dashboard, Supabase, Claude API

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ContractorLicensePro lets homeowners verify a contractor's license status, check for complaints, and see a Trust Score before hiring. The system pulls data from state licensing boards, cross-references public complaint records, and generates a score based on license type, status, history, and consumer complaints.

The Trust Score algorithm weighs license validity, years active, complaint history, bond status, and insurance verification. Contractors with active licenses and clean records score higher. Red flags (expired license, unresolved complaints, missing bond) lower the score with specific explanations visible to consumers.

The platform includes city-level pages, state guides, and a cost calculator that estimates project costs by location and project type. Built in one week. Runs on Cloudflare Pages with D1 database at the edge. Monthly hosting cost: zero.

Stack: Astro 5, Cloudflare Pages, D1 SQLite, Workers

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DoctorLicensePro gives patients access to information that existing platforms skip. Rating sites show reviews. Booking sites show availability. Neither shows whether a doctor's license was restricted, whether board certification lapsed, or whether disciplinary actions exist on record.

The platform pulls from CMS NPPES (the federal provider database with 6 million records), cross-references with state medical board data, and verifies board certifications through ABMS. The Trust Score weighs active license status, board certification, disciplinary history, and practice duration.

Same architecture as ContractorLicensePro: proven pattern, proven economics ($0/month hosting), adapted for healthcare data sources and compliance requirements. When a model works, replicate it.

Stack: Astro 5, Cloudflare Pages, D1, NPPES + state board scrapers

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MCP (Model Context Protocol) is the standard that lets AI assistants use external tools. Adopted by the Linux Foundation in 2025. These 6 servers turn AI assistants from chatbots into operational tools that interact with real business systems.

Memory MCP gives AI persistent memory across sessions. Instead of starting from scratch every conversation, the AI remembers decisions, patterns, and project context. Worksection MCP lets AI manage tasks and projects directly. ServiceFusion MCP bridges field service operations. VisualDiff MCP automates design QA by comparing Figma mockups to live websites using Claude Vision.

Each server is production-ready: multi-tenant (serves multiple clients from one instance), OAuth2 authentication, encrypted API keys at rest, rate limiting per plan, Docker deployment with SSL. Not prototypes. Working infrastructure handling real data for real users.

Stack: Node.js, Supabase, Docker

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Standard research searches linearly: query, read results, done. This pipeline builds a knowledge graph from search results, then uses the graph structure to discover what to search next. Each iteration adds new nodes and edges. The loop continues until convergence: less than 20% of new searches produce new information.

Phase 1 casts a broad net (15 to 20 queries). Phase 2 derives new search queries from graph connections and repeats until saturated. Phase 3 analyzes citation networks: which papers cite each other, which clusters agree or disagree, which findings replicate. Phase 4 inverts the verdict and tries to break it from both directions. Phase 5 generates testable predictions from graph structure. Phase 6 assigns confidence scores weighted by citation strength.

Tested on a real scientific claim. Standard search: 23 studies, surface coverage. Full 6-phase pipeline: 65 studies, including 6 discoveries that were only possible through graph-derived iteration. The graph doesn't just organize knowledge. It reveals what you didn't know to look for.

Stack: Claude API, WebSearch, graph analysis, adversarial validation

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Real estate investors need data from dozens of sources: county records, tax assessor databases, property listings, auction schedules, title records. Each source has a different format, different login, different update schedule. Doing this manually for one property takes hours. For a portfolio, it becomes a full-time job.

This platform automates the entire research pipeline. 20 workflows orchestrate data collection from multiple county and state sources. Claude Vision reads and extracts data from scanned documents that traditional OCR misreads. VPN rotation prevents rate limiting across county websites that block automated access.

The system consolidates everything into searchable profiles per property: ownership history, tax status, liens, assessed value, comparable sales. An investor opens the dashboard and sees one screen instead of checking 8 different websites per property.

Stack: n8n, Claude Vision, Playwright, Supabase, VPN rotation

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The system scans markets continuously using 12 signal detectors. Each detector monitors different patterns: volume spikes, price breakouts, momentum shifts, divergence between indicators. When multiple detectors converge on the same signal, the system generates a trade recommendation.

3 strategies run with real money. 150 more run in paper mode (simulated trades against real data, no capital at risk). Every strategy has automated test coverage. 655 tests verify signal generation logic, position sizing rules, risk management thresholds, and order execution sequences.

The system doesn't replace trading judgment. It replaces the 14 hours of daily screen-watching that manual traders endure. Signals arrive with context. Positions execute within parameters. The human reviews results once a day, adjusts strategy weights, and lets the system continue.

Stack: Node.js, ccxt, Binance API, PostgreSQL, real-time data feeds

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Most AI chatbots live on one channel. A website widget, or a Telegram bot, or a phone line. Each is a separate system, separate context, separate conversation history. A customer starts on the website and switches to WhatsApp, and the AI forgets everything.

This system shares one reasoning layer across all 4 channels. Voice calls use Retell AI with real-time speech processing. The website widget embeds as a single script tag. Telegram and WhatsApp Business Cloud API handle messaging channels. All four feed into the same Claude API backend, the same knowledge base, the same conversation memory.

The voice component is fully built and production-tested (currently deployed on demand, not always-on). Text channels run continuously. The knowledge base updates from the business content automatically. Admin panel manages conversations, knowledge articles, and organization settings across all channels.

Stack: Node.js, Claude API, Retell AI, Supabase, Docker

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