{ "organization": { "name": "2 Acre Studios", "founded": "2010", "location": { "city": "Pittsburgh", "state": "PA", "country": "US" }, "tagline": "We build AI that actually works.", "philosophy": "AI should be private, local, and owned by the people who use it.", "website": "https://2acrestudios.com", "contact": { "email": "marc@2acrestudios.com", "phone": "(412) 407-6170" }, "social": { "github": "https://github.com/marc-shade", "linkedin": "https://linkedin.com/company/2-acre-studios", "discord": "https://discord.gg/9DvzfsrYFA" } }, "team": [ { "name": "Marc Shade", "role": "Agentic AI Chief Engineer", "bio": "Building software since the 1980s. 20 years shipping for corporate clients — Kellogg's, Hertz, Stryker, ConAgra — with teams from Leo Burnett and Arc Worldwide. Founded 2 Acre Studios in 2010. Pivoted to AI in 2023. 60+ open source repos. ARC-AGI-3 competitor.", "expertise": ["Private AI", "Multi-Agent Systems", "RAG Pipelines", "Full-Stack Development", "Open Source"], "links": { "github": "https://github.com/marc-shade", "linkedin": "https://www.linkedin.com/in/marcshade" } }, { "name": "Scott Frederick Laughlin", "role": "Lead AI/ML Engineer / Cloud Architect", "bio": "Tech entrepreneur and cloud architect with a decade of pioneering AI SaaS, IoT solutions, and multi-cloud infrastructure. Founder of TechRamp. Led high-impact projects for Fortune 500 enterprises.", "expertise": ["Generative AI", "AI-Assisted Consulting", "IoT", "Cloud Systems", "AI Agents"], "links": { "linkedin": "https://www.linkedin.com/in/scott-engineer-inventor" } } ], "services": [ { "id": "local-ai", "name": "Local AI Deployment", "description": "Private, on-premise AI systems running on your hardware. We deploy Ollama-based large language models behind your firewall — no data leaves your building, ever. Your IT team gets full control over model selection, fine-tuning, and access policies. We handle GPU allocation, model optimization, load balancing, and monitoring. Most deployments are production-ready within two weeks. Ideal for healthcare, finance, legal, and any industry where data sovereignty is non-negotiable. Supports models from 7B to 70B+ parameters on consumer GPUs or enterprise hardware.", "tech_stack": ["Ollama", "vLLM", "Python"], "ideal_for": ["Healthcare", "Finance", "Legal", "Government"], "timeline": "2-4 weeks" }, { "id": "multi-agent", "name": "Multi-Agent Systems", "description": "Autonomous agent workflows that actually complete tasks. Not chatbots — workers. We build multi-agent systems on AutoGen and CrewAI where specialized AI agents collaborate to solve complex business problems: research agents gather information, analyst agents process it, and execution agents take action. Each agent has defined roles, tools, and guardrails. We've shipped agent teams for competitive intelligence, automated code review, and content production pipelines. Work gets done while you sleep — with audit trails, error handling, and human-in-the-loop checkpoints.", "tech_stack": ["AutoGen", "CrewAI", "Python"] }, { "id": "sales-enablement", "name": "Sales Enablement", "description": "AI-powered proposal generation, lead scoring, and pipeline intelligence that delivers real revenue impact. Our systems cut proposal creation time by 40% by automating research, competitive positioning, and document assembly. Lead scoring models analyze behavioral signals, firmographic data, and engagement patterns to surface highest-probability deals. Pipeline intelligence dashboards give sales leaders real-time visibility into deal health, risk factors, and forecast accuracy. Built on RAG pipelines that learn from your winning proposals.", "tech_stack": ["Python", "LLMs", "RAG"], "metrics": { "proposal_time_reduction": 0.40, "hours_saved_per_proposal": 4 } }, { "id": "document-intelligence", "name": "Document Intelligence", "description": "Turn your document chaos into a searchable, queryable knowledge base. We build RAG pipelines that process thousands of documents in hours — PDFs, Word files, spreadsheets, emails, Slack threads, whatever you have. Your team asks natural language questions and gets accurate, sourced answers in seconds. Vector database backends ensure fast retrieval across millions of document chunks. We handle OCR for scanned documents, table extraction, and multi-language support. Reduce document processing time by 70%.", "tech_stack": ["RAG", "Vector DBs", "Python"], "metrics": { "processing_time_reduction": 0.70 } }, { "id": "customer-support", "name": "Customer Support AI", "description": "Intelligent ticket triage, response generation, and escalation at $0.50 per interaction versus $15+ for human support. Our systems automatically classify incoming tickets by urgency, topic, and sentiment, then route them to the right team or generate draft responses for agent review. We achieve 65% automation rates on Tier 1 support while maintaining customer satisfaction scores. Integrates with Zendesk, Freshdesk, Intercom, and custom helpdesk systems. Built-in analytics track resolution times, automation rates, and cost savings in real time.", "tech_stack": ["LLMs", "Python", "APIs"], "integrations": ["Zendesk", "Freshdesk", "Intercom"], "metrics": { "ai_cost_per_interaction": 0.50, "human_cost_per_interaction": 15.00, "automation_rate": 0.65 } }, { "id": "custom-dev", "name": "Custom Development", "description": "Full-stack web applications, APIs, and integrations — built in Python, JavaScript, or whatever the problem needs. We've shipped production systems for Fortune 500 companies and startups alike. We specialize in AI-integrated applications where traditional development meets machine learning — internal tools with natural language interfaces, automated workflows with human oversight, and data pipelines that feed both dashboards and AI models. Every project includes proper error handling, monitoring, documentation, and deployment automation.", "tech_stack": ["Python", "JavaScript", "APIs"] } ], "faq": [ { "question": "What is local AI deployment and why does it matter?", "answer": "Local AI deployment means running large language models and other AI systems on hardware you own and control — inside your data center, office, or private cloud. Unlike cloud AI services where your data is sent to third-party servers, local deployment keeps everything behind your firewall. This matters for industries with strict data regulations (healthcare, finance, legal, government) and for any organization that considers its data a competitive advantage." }, { "question": "How much does AI customer support cost compared to human support?", "answer": "Our AI customer support systems cost approximately $0.50 per interaction compared to $15 or more for human-handled support tickets. At a 65% automation rate for Tier 1 inquiries, a company handling 1,000 tickets per month saves roughly $113,000 annually. Most companies see full ROI within 3-6 months." }, { "question": "What is a multi-agent system and how is it different from a chatbot?", "answer": "A multi-agent system uses multiple specialized AI agents that collaborate to complete complex tasks autonomously. Unlike a chatbot that responds to one query at a time, a multi-agent system assigns roles — researcher, analyst, writer, reviewer — and agents work together through defined workflows. The result is AI that does work, not just answers questions." }, { "question": "What is RAG and why do you use it for document intelligence?", "answer": "RAG (Retrieval-Augmented Generation) connects a large language model to your specific documents and data. Instead of relying on the model's training data alone, RAG retrieves relevant passages from your knowledge base and generates accurate, sourced answers. It dramatically reduces hallucinations and provides citations for every answer." }, { "question": "How long does it take to deploy a private AI system?", "answer": "Most local AI deployments are production-ready within two to four weeks. More complex projects like multi-agent systems or enterprise-wide document intelligence platforms typically take four to eight weeks. We start building in week one." }, { "question": "What hardware do I need to run local AI models?", "answer": "For small to medium deployments (7B-13B parameter models), a single workstation with an NVIDIA RTX 3090 or 4090 GPU (24GB VRAM) is sufficient. For larger models (30B-70B parameters), enterprise GPUs like the A100 or H100 are recommended. We also support Apple Silicon deployments (M2/M3/M4 Ultra)." }, { "question": "Do you offer ongoing support after deployment?", "answer": "Yes. Every deployment includes 30 days of post-launch support. After that, we offer ongoing maintenance retainers covering model updates, performance tuning, security patches, and scaling support. We also train your team for independent operation." }, { "question": "How is 2 Acre Studios different from other AI consulting firms?", "answer": "Three things: we build and ship (60+ open source repos, 624 GitHub stars, two shipped products), we prioritize private local AI over cloud dependencies, and we bring decades of Fortune 500 engineering discipline to AI projects." }, { "question": "What industries do you work with?", "answer": "We work across technology, healthcare, finance, manufacturing, retail, and professional services. Finance and healthcare see higher returns due to data privacy premiums. Technology companies engage us for multi-agent systems and developer tooling." } ], "projects": [ { "name": "Ollama-Workbench", "url": "https://github.com/marc-shade/Ollama-Workbench", "stars": 47, "language": "Python", "description": "Comprehensive platform for managing and testing local Ollama models" }, { "name": "TeamForgeAI", "url": "https://github.com/marc-shade/TeamForgeAI", "stars": 27, "language": "Python", "description": "AI agent framework for managing teams of agents with common goals" }, { "name": "ai-persona-lab", "url": "https://github.com/marc-shade/ai-persona-lab", "stars": 8, "language": "Python", "description": "Create and manage dynamic AI personas for interactive group chats" }, { "name": "Reddit-Marketing", "url": "https://github.com/marc-shade/Reddit-Marketing-Assistant-Workflow", "stars": 7, "language": "n8n", "description": "n8n workflow for identifying marketing leads from Reddit posts" } ], "calculator": { "metrics": { "support_automation_rate": 0.65, "support_cost_human": 15.00, "support_cost_ai": 0.50, "doc_processing_reduction": 0.70, "sales_speed_improvement": 0.40, "sales_hours_per_proposal": 4, "productivity_boost": 0.40, "cost_reduction": 0.25, "roi_per_dollar": 3.70 }, "implementation_costs": { "1-10": { "low": 5000, "high": 15000 }, "11-50": { "low": 15000, "high": 50000 }, "51-200": { "low": 50000, "high": 100000 }, "201-500": { "low": 100000, "high": 200000 }, "500+": { "low": 200000, "high": 500000 } }, "industry_multipliers": { "technology": 1.0, "healthcare": 1.15, "finance": 1.20, "manufacturing": 0.95, "retail": 0.90, "professional_services": 1.05, "other": 1.0 }, "formulas": { "support_savings": "tickets * 0.65 * ($15 - $0.50) * 12 * industry_multiplier", "document_savings": "hours_per_week * 0.70 * hourly_cost * 52 * industry_multiplier", "sales_savings": "proposals * 0.40 * 4 * hourly_cost * 12 * industry_multiplier", "roi": "(total_savings - implementation_cost) / implementation_cost * 100", "payback_months": "implementation_cost / (total_savings / 12)" }, "data_sources": ["McKinsey", "IBM", "Gartner", "Industry Averages"] }, "manifesto": [ "We build. Then we ship.", "Since 2010, we've operated like a lab — hands on the keyboard, not just the whiteboard.", "Every line of code we write ships. Every model we train solves a real problem. Every system we deploy runs on YOUR hardware, with YOUR data, under YOUR control.", "We believe AI should be private, local, and owned by the people who use it.", "60+ open source repositories. 624 GitHub stars. 2 shipped AI products. Zero bullshit." ], "stats": { "repos": "60+", "stars": 624, "years": "14+", "products_shipped": 2, "vendor_lock_in": 0 }, "meta": { "generated": "2026-02-22", "version": "2.0", "spec": "llmstxt.org", "agent_files": { "summary": "/llms.txt", "full": "/llms-full.txt", "structured": "/agent.json" } } }