YOUR COMPETITORS ARE LEVERAGING AI. YOU'RE NOT.
A Pittsburgh AI lab, building since 2010 — 60+ open source repos, two shipped AI products, zero throwaway demos. Every quarter you wait, your competitors compound the lead.
We assess where AI fits across your business, ship the systems, and upskill the employees you already have — sales, ops, support, finance, legal, engineering — so every team operates at AI velocity. Private model deployment, Claude Code adoption, RAG, multi-agent workflows, and workforce training built for your security, compliance, and delivery constraints. Before next quarter slips.
MANIFESTO
WE BUILD. THEN WE SHIP.
Most companies are still in the slide-deck phase of AI. Their competitors aren't. Since 2010, we've operated like a lab — hands on the keyboard, not just the whiteboard. When we advise, it's because we've already built it ourselves.
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 — in weeks, not the next budget cycle. And every engagement leaves your existing team operating it — because the goal isn't a vendor dependency, it's YOUR people moving faster than the competition.
The companies that ship AI this year set the price. Everyone else pays it.
60+ open source repositories. 624 GitHub stars. 2 shipped AI products. No throwaway demos.
CAPABILITIES
LOCAL AI DEPLOYMENT
Run LLM workloads on infrastructure you control. We design and deploy Ollama or vLLM behind your firewall, with model selection, access policy, logging, monitoring, and handoff documentation built in. Best fit: healthcare, finance, legal, government contractors, and IP-sensitive teams that need AI capability without uncontrolled data movement.
> More on local AI deploymentMULTI-AGENT SYSTEMS
A chatbot answers one question. An agentic workflow finishes a process. We build AutoGen, CrewAI, and custom state-machine systems with role-specific agents, scoped tools, audit trails, budget limits, and human approval gates for actions that matter.
> More on multi-agent systemsSALES ENABLEMENT
Proposal generation, lead scoring, deal-desk risk reads, and pipeline hygiene built on your CRM, call transcripts, past wins, and sales methodology. The goal is not generic outreach volume; it is faster sales work with better context and tighter human review.
> More on sales enablement AIDOCUMENT INTELLIGENCE
RAG pipelines and document intelligence platforms for PDFs, Office files, email, Slack, SharePoint, contracts, filings, and internal knowledge bases. Answers are grounded in retrieved source material, with citations, permissions, evaluation sets, and admin analytics.
> More on RAG pipelinesCUSTOMER SUPPORT AI
Ticket triage, response drafting, knowledge-grounded answers, sentiment detection, and escalation summaries. We keep humans in the loop where risk is high and instrument deflection, accuracy, CSAT, handle time, and cost savings from day one.
> More on customer support AICUSTOM DEVELOPMENT
Internal tools, APIs, integrations, AI-assisted workflows, and data products that existing SaaS cannot cover. We scope tightly, ship the smallest useful system, instrument it, and leave your team with documentation, deployment automation, and a path to operate it.
CLAUDE CODE PRACTICE
Claude Code becomes useful at scale only when governance, context, skills, hooks, review rules, and team habits are designed together. We cover training, private/local workflows, enterprise setup, and managed retainers for teams that want AI-assisted development without chaos.
> See the full Claude Code practiceAI ADOPTION & WORKFORCE UPSKILLING
Most of your competitive advantage isn't a model — it's whether your existing employees use AI every day. We audit your business processes, pick the right tools per role (Claude, ChatGPT, Copilot, internal agents), set the governance rails, and run hands-on workshops for sales, ops, support, finance, legal, and PM teams so they ship work 3–10× faster — safely, with policy that holds up under audit.
> Start with a free readiness assessmentTHE ACRE METHOD
HOW WE WORK — IN FIVE PHASES
Most consultancies hand you a deck. We hand you a working path. The same five phases run every engagement — from a free readiness assessment to a production build. You always know what comes next, what risk is being retired, and what evidence decides the next step.
ASSESS
Free 30-minute readiness call. We map your stack, your data, your highest-ROI AI or Claude Code use cases, and the governance gaps that would block production rollout. You leave with a written one-page summary either way — whether or not we work together.
> Book free assessmentPLAN
Two-week strategy sprint. Stakeholder interviews, data and infrastructure audit, model and hardware sizing, implementation roadmap with cost, risk, and timeline. Fee credits toward your build if you move forward.
> See planning tiersBUILD
Six to twelve weeks of milestone-based delivery. Real integrations, real data, real production hardening: auth, audit logs, CI/CD, observability, load and adversarial testing. Thirty days of post-launch support included.
> See build engagementsTRAIN
Your existing employees take over. Two-day workshops ($15K) for engineering teams learning Claude Code, role-specific AI workshops for sales, ops, support, and operations teams, multi-week org-wide enablement ($35K–$75K), and coaching retainers ($3K–$5K/month) for governance and ongoing skill development. We don't replace your people — we make them faster than your competitors'.
> See training optionsOPERATE
Optional managed retainer. We stay on as your Claude Code agency: 1–3 automations per month, queue-based intake, support, strategy syncs, and continuous optimization. $4,500–$22,000/month depending on scope.
> See retainer tiersTHE LAB
OPEN SOURCE PROJECTS
Ollama-Workbench
Comprehensive platform for managing and testing local Ollama models
TeamForgeAI
AI agent framework for managing teams of agents with common goals
ai-persona-lab
Create and manage dynamic AI personas for interactive group chats
Reddit-Marketing
n8n workflow for identifying marketing leads from Reddit posts
TEAM
THE HUMANS BEHIND THE MACHINES.
Marc Shade
AGENTIC AI CHIEF ENGINEER> Building software since the 1980s. Spent 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. Now builds private, local AI systems — multi-agent workflows, RAG pipelines, and full-stack applications.
> 60+ open source repos. ARC-AGI-3 competitor. Zero tolerance for avoid unnecessary single-vendor dependency.
Scott Frederick Laughlin
LEAD AI/ML ENGINEER / CLOUD ARCHITECT> 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 — advanced AI agents, large-scale data processing, and cloud-connected product architectures.
> Expertise: generative AI, AI-assisted consulting, IoT, and next-generation cloud systems.
TERMINAL
THE BEST INTERFACE IS NO INTERFACE.
AI ROI CALCULATOR
ESTIMATE YOUR RETURN ON AI INVESTMENT
ENGAGE
FOUR WAYS IN. START FREE. SHIP THIS QUARTER.
AI READINESS ASSESSMENT
30 minutes that tells you exactly where your competitors are eating your lunch with AI — the two or three interventions that close the gap fastest, and which of your existing teams need upskilling first. No pitch. We'll tell you honestly whether you need us yet.
- Business-process + stack review
- AI opportunity shortlist (per team)
- Workforce upskilling read
- Data sovereignty + risk check
- Written 1-page summary
DEEP DISCOVERY
Full technical + operational discovery. We interview stakeholders, audit data + infrastructure, benchmark candidate models, and deliver an implementation roadmap you can hand to any vendor — including us.
- Stakeholder interviews (up to 8)
- Data + infra audit
- Model + hardware sizing
- Roadmap with cost / risk / timeline
- Fee credited toward Tier 03 or 04
POC BUILD
A working proof-of-concept on a single well-defined use case. Real data, real integrations, runs in the target environment, and produces evidence for a go/no-go decision.
- One focused use case, shipped
- Runs on your hardware or cloud
- Evaluation harness + metrics
- Handoff docs + recorded walkthroughs
- Go / no-go decision framework
PRODUCTION BUILD
Production-grade hardening of the POC: monitoring, access control, scaling, SLAs, and training for your team. This is the version you put in front of real users and real data.
- Hardening, auth, audit logging
- CI/CD + observability
- Load + adversarial testing
- 30-day post-launch support
- Optional maintenance retainer
AI AGENCY RETAINER
Claude Code collapsed the cost of custom software. We install it inside your stack, wire it into your APIs, load it with your institutional context — then run it as the dev engine that ships automations, agents, and internal tools on request.
You don't learn Claude Code. You don't hire AI engineers. You get us.
One-time AIOS install. We set up Claude Code against your live stack, integrate your APIs and data sources, load your institutional context, and ship your #1 pain-point automation — live, in front of you. Proof of value before the retainer clock starts.
For teams who know what they want.
- 1 automation / month
- Queue-based request intake
- Email + Discord support
- Quarterly strategy sync
For teams scaling fast.
- 2–3 automations / month
- Priority Slack / Discord access
- Monthly strategy + review call
- Maintenance on everything shipped
- Rapid iteration on feedback
// HOW THIS COMPOUNDS: every system we ship for you makes the next one faster. Claude Code learns your stack, your naming conventions, your data shapes, your people. The 10th automation costs a fraction of the 1st. That's the flywheel.
// FIXED-FEE WHEN WE CAN. T&M ONLY WHEN SCOPE CAN'T BE FROZEN. RETAINERS BY REQUEST — NEVER BY DEFAULT.
> REQUEST FREE AI READINESS ASSESSMENT
30 minutes. Remote. We'll reply within one business day.
FAQ
COMMON QUESTIONS ABOUT PRIVATE AI
What is local AI deployment and why does it matter?
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. We use Ollama-based infrastructure that runs models from 7B to 70B+ parameters on standard GPU hardware, with controlled data boundaries, predictable operating costs, and no lock-in to one inference vendor.
How much does AI customer support cost compared to human support?
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. The AI handles ticket classification, response drafting, sentiment analysis, and routing. Complex issues are escalated to human agents with full context summaries. Implementation costs range from $5,000 for small teams to $200,000+ for enterprise deployments, with most companies seeing full ROI within 3-6 months. Assumptions use conservative industry benchmarks and should be validated against your ticket mix before procurement.
What is a multi-agent system and how is it different from a chatbot?
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. For example, one agent might gather competitive intelligence, another analyzes it, and a third drafts a report. We build these on AutoGen and CrewAI frameworks with guardrails, error handling, and human-in-the-loop checkpoints. The result is AI that does work, not just answers questions.
What is RAG and why do you use it for document intelligence?
RAG (Retrieval-Augmented Generation) is a technique that 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 uses them to generate accurate, sourced answers. We use RAG because it dramatically reduces hallucinations, provides citations for every answer, and works with any document format — PDFs, Word files, spreadsheets, emails, Slack threads. Our RAG pipelines process thousands of documents in hours and store them in vector databases for sub-second retrieval across millions of chunks.
How long does it take to deploy a private AI system?
Most local AI deployments are production-ready within two to four weeks. Week one covers infrastructure assessment, model selection, and environment setup. Week two handles model deployment, fine-tuning, and integration with your existing systems. Weeks three and four focus on testing, optimization, and team training. More complex projects — like multi-agent systems or enterprise-wide document intelligence platforms — typically take four to eight weeks. We don't do six-month discovery phases. We start building in week one and iterate based on real usage.
What hardware do I need to run local AI models?
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), you'll need enterprise GPUs like the A100 or H100, or multiple consumer GPUs. We also support Apple Silicon deployments (M2/M3/M4 Ultra) for organizations using Mac infrastructure. We handle GPU allocation, model quantization, and memory optimization to get the best performance from your existing hardware. Many clients start with a single GPU server and scale up based on actual usage patterns.
Do you offer ongoing support after deployment?
Yes. Every deployment includes 30 days of post-launch support covering monitoring, optimization, and issue resolution. After that, we offer ongoing maintenance retainers that include model updates, performance tuning, security patches, and scaling support. We also provide training for your technical team so they can manage day-to-day operations independently. Our goal is to make you self-sufficient, not dependent on us — but we're here when you need expert help for new features, model upgrades, or scaling challenges.
How is 2 Acre Studios different from other AI consulting firms?
Three things set us apart. First, we build and ship — 60+ open source repositories, 624 GitHub stars, and two shipped AI products. We're not advisors who've never written production code. Second, we prioritize private, local AI over cloud dependencies. Your data stays on infrastructure you control, with no lock-in to one AI vendor. Third, we've been building software since the 1980s and shipping for Fortune 500 clients (Kellogg's, Hertz, Stryker, ConAgra) since 2006. We bring enterprise-grade engineering discipline to AI projects, not just AI research credentials.
What industries do you work with?
We work across technology, healthcare, finance, manufacturing, retail, and professional services. Our AI ROI calculator applies industry-specific multipliers: finance and healthcare see higher returns due to the premium on data privacy and compliance automation. Manufacturing and retail benefit most from document intelligence and supply chain optimization. Technology companies typically engage us for multi-agent systems and developer tooling. We've delivered projects for organizations ranging from 10-person startups to Fortune 500 enterprises. The common thread: organizations that want production AI systems, not disposable demos.
What is the AI Agency Retainer and who is it for?
The AI Agency Retainer (Tier 05) is an ongoing engagement where we install Claude Code inside your stack, wire it into your APIs and data sources, and use it as the dev engine to ship automations, agents, and internal tools on request. It starts with a one-week Kickoff ($7,500 remote or $12,500 on-site) where we set up the AI operating system and ship your first automation live — proof of value before the retainer clock starts. Monthly retainers start at $4,500 (Operator) for one build per month, $9,500 (Partner) for two to three builds per month with priority support and maintenance included, and $22,000 (Embedded) for unlimited queue with weekly syncs and optimization. Designed for small and medium businesses that want production AI work without hiring AI engineers or learning Claude Code themselves.
How is the retainer different from a traditional project?
Traditional project engagements (Tiers 02–04) are scoped, fixed-fee, time-boxed: a discovery, a POC, or a production build. You know what you want; we ship it; we're done. The retainer is the opposite: you don't know what you'll need next quarter, but you know you'll need something. We stay embedded, the AIOS stays installed and contextualized, and every system we build compounds. The 10th automation costs a fraction of the 1st because Claude Code has learned your stack, your naming, your people, and your patterns. That's the flywheel.
Do we have to learn Claude Code to use the retainer?
No. That's the whole point. We install and operate Claude Code inside your stack; you submit requests in plain English and receive working systems. If you do want your team to learn Claude Code as a daily driver, we offer a two-day Team Enablement Workshop ($15,000, up to 10 people) as an add-on on any retainer tier. The Embedded tier includes one workshop per year.
STILL READING? YOUR COMPETITORS AREN'T.
They're shipping. Their teams are already faster. Bring the business process, the team that needs upskilled, and the decision you've been putting off — we turn it into a production path and a workforce that operates it. Before the next board meeting, not after the next funding round.
> PHONE: (412) 407-6170
> LOCATION: Pittsburgh, PA, USA