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What do a farm, mine, refrigerated warehouse and smart home have in common? They all produce continuous operational signals—and every signal can lead to a decision. Irrigate now or wait? Take a haul truck out of service? Is a refrigeration temperature rise a real food-safety risk? Is a water sensor indicating an actual leak? AI agents operating in the physical world need training environments where sensor changes lead to decisions and measurable consequences. Our Agriculture, Mining & Buildings telemetry datasets are designed to support exactly these types of prediction, diagnosis and reasoning tasks. Add work orders, operator actions and what-if scenarios, and telemetry becomes an agent-training environment rather than simply a time series. Interested in samples? DM me or contact [email protected].

Reading a W-2 or 1099 is easy compared with understanding why every number appears where it does and whether the document is internally correct. Our W-2 data follows the chain payroll state → tax logic → W-2 fields → rendered document → coordinates → verified ground truth, while our 1099 datasets preserve economic-event lineage from invoices, payments, tax lots, distributions, merchant transactions and other source activity through to the annual form. This enables training and evaluation for models and agents that must extract, reconcile, reason, validate and identify errors—not merely perform OCR. Both factories are designed for scalable generation and machine-checkable ground truth. Building financial-document AI? DM me or write to [email protected] for sample packs, custom datasets and benchmark/evaluation programs.

Imagine training a healthcare AI model on thousands—or far more—of realistic lab, microbiology and pathology reports where every value on every page already has a verified answer. Our synthetic report engines create clinical laboratory PDFs plus complex culture/sensitivity and surgical pathology reports, with ground truth covering locations, values, MIC interpretations, resistance mechanisms, diagnoses, staging, biomarkers and other clinical details. The microbiology/pathology corpus independently re-derives its medical logic during validation, and its tested 2,000-report corpus achieved a 100/100 A+ quality gate. This creates scalable data for model training, OCR, extraction benchmarks, clinical reasoning and AI-agent evaluation without using PHI. Interested in healthcare AI training data with ground truth? DM me or write to [email protected].

Would a real city or county government sell five years of its complete connected operating data to an AI lab? Property records. Budgets. 911 calls. Utility accounts. Public works requests. Financial transactions. Internal emails. Government data may include PII, sensitive operational information, protected records, security concerns and regulatory restrictions. Even where individual records are public, obtaining the entire organization as one coherent, connected AI-training environment is a very different proposition. So how do we teach AI agents how a city and county actually operate? Xpert Systems Inc. created: #16: Calder Metro Calder Metro is a connected enterprise corpus for city and county government. It represents: 14 linked process layers 60 months One invented consolidated city-county government Rendered end to end And the scope is deliberately broad: From the parcel roll and budget ordinance → to the 911 call → the water bill → the pothole → the ledger → and the email threads employees sent each other about all of it. That is what makes the concept interesting for AI. A city isn't a collection of unrelated departments. A resident reports a problem. A government process responds. Resources are consumed. Transactions occur. Records are created. Employees communicate. Eventually, activity can flow into financial and administrative systems. An Enterprise AI agent may need to understand all of those connections. Imagine testing an AI agent with questions such as: Can it trace an event across multiple government processes? Can it reconcile structured records with internal communications? Can it reason about events that happened months or years apart? Can multiple specialized agents collaborate across departments while remaining grounded in the same underlying government world? Calder Metro creates an environment for Government AI agents, Enterprise RAG, public-sector workflow agents, long-horizon reasoning, cross-process reasoning, multi-agent systems and rigorous AI evaluations. Instead of giving an AI model another folder of government PDFs, give it a city and county that operates continuously for years. Then see whether the AI can understand how government actually fits together. Calder Metro is one of 18 Continuous Connected Enterprises from Xpert Systems Inc. We are looking for partnerships with frontier AI labs, well-funded AI startups, GovTech companies, Enterprise AI developers, data companies and research organizations interested in training and evaluating AI against complete connected enterprise environments. Want to put your AI model or AI agent inside a five-year city and county government? DM Xpert Systems Inc. or write to [email protected] for samples, specifications, licensing and partnership opportunities.

General-purpose LLMs can explain ERP; we want LLAMA Xpert ERP to reason through it. Starting with a Llama foundation and training on connected enterprise data, we are building toward specialized intelligence for ERP copilots, investigative agents, workflow agents, enterprise evaluation and increasingly autonomous operations. Our focus is cross-functional reasoning across Finance, Procurement, Supply Chain, Sales, HR and Operations—with traceable cause and effect across the enterprise. We are seeking AI labs and enterprise partners for early access, pilots, licensing and strategic partnerships—DM me or write to [email protected].

Robotics AI needs to learn more than pick-and-place. ROBO-TRAJ-004 provides simulated robot trajectories for four contact-rich skills: pressing buttons, opening hinged doors, placing items on shelves and inserting pegs, with full state, multiple action representations, labels and camera data at every step. Critically, the dataset includes mistakes, recoveries and failures marked at the exact step, giving teams data for training recovery policies, failure detectors, reward models and robotic agents. Every episode can be exactly replayed in MuJoCo, and the validated sample pack contains 260 episodes across 16 task × variant combinations. AI Labs, Neo Labs and funded robotics startups: DM me or write to [email protected] for the sample pack, custom production runs, licensing or partnerships.

CONTINUOUS CONNECTED ENTERPRISE #13 OF 100 — U.S. HEALTH SYSTEM AI labs need data showing how a hospital system actually operates. But which hospital will hand over 15 years of patient encounters, diagnoses, medications, imaging, lab results, claims, denials, physician activity, emails and financial data? HIPAA, patient privacy, PII/PHI, cybersecurity, clinical confidentiality, payer contracts and regulatory requirements make this data extraordinarily difficult to obtain. That is why we built Bellhaven Health System. 8,900+ employees. 15 years of interconnected history — and it never stops running. Patients arrive. Physicians diagnose. Labs and imaging are ordered. Medications are administered. Surgeries occur. Claims are submitted. Denials arrive. Appeals are filed. Supplies are consumed. Employees communicate. Revenue and costs change. Tomorrow, Bellhaven continues from where it finished today. It connects 10 business processes: Patient Access → Clinical Encounters → Orders & Diagnostics → Pharmacy & Medication → Surgery & Procedures → Revenue Cycle → Claims & Denials → Credentialing & Workforce → Supply Chain → Quality, Finance & Compliance. Underneath are interconnected tables covering patients, providers, encounters, diagnoses, procedures, medications, labs, imaging, claims, denials, payments, employees, supplies and financial activity. But a hospital is more than tables. Bellhaven can extend into clinical documents, reports, CRM data, emails, Slack-style messages, PowerPoints, spreadsheets, policies, SOPs, quality reports, staffing plans, meeting artifacts and strategic roadmaps—connected to the underlying activity. Where facilities and medical infrastructure are involved, CAD/BIM drawings and engineering artifacts can also be linked. A patient encounter creates orders. Orders create results. Treatment consumes medications and supplies. Procedures create charges. Charges become claims. Denials trigger appeals. Clinical outcomes influence quality metrics. Everything ultimately affects operations and financial performance. That is what enterprise AI needs: A living healthcare enterprise where actions have consequences. Not a static dataset. 15 years + tomorrow + the next day + continuously. Xpert Systems Inc. has built 100 Continuous Connected Enterprises, developed with valuable input from 100+ contracted industry specialists. We are looking for partnerships with well-funded AI startups, neo labs, frontier AI labs, enterprise AI companies and strategic technology partners for model training, co-development, data partnerships or joint ventures. DM Xpert Systems Inc. or write to [email protected] for samples, specifications and partnership discussions.

Do you have hands-on experience in Retail or E-commerce operations? We would like to work with you. Xpert Systems Inc. is expanding our Human Data Network to create detailed, human-validated Standard Operating Procedures (SOPs) for AI model and AI agent training. We are looking for experienced professionals across areas such as: • Store Operations • E-commerce Operations • Order Management & Fulfillment • Inventory Management • Warehouse Operations • Receiving & Replenishment • Returns, Refunds & Exchanges • Customer Service / Contact Centers • Payments & Payment Exceptions • Fraud Prevention • Pricing & Promotions • Merchandising • Product Catalog Management • Marketplace Operations • Shipping & Delivery Exceptions • Loss Prevention • Supplier / Vendor Management • Omnichannel / Buy Online, Pick Up in Store • Store Opening & Closing Procedures • Retail Operations Management You do not need to write the SOP from scratch. We want specialists who understand how these processes actually work in stores, e-commerce businesses, distribution operations and omnichannel environments. Based on your expertise, Xpert Systems will draft the SOP — including roles and authority, systems of record, step-by-step workflows, decision points, exception handling, escalation paths, service levels, documentation requirements and quality controls. You will then work with us to: Review → Correct → Validate → Sign Off Our objective is to transform real human retail and e-commerce expertise into structured, specialist-validated operational knowledge for training and evaluating AI models and AI agents. If you have meaningful hands-on experience in Retail or E-commerce operations, we would like to hear from you. DM Xpert Systems Inc. or write to [email protected] with your area of expertise, current/previous role, industry experience and years of experience. Help us capture how retail and e-commerce operations actually work — and turn that human expertise into training data for the next generation of Enterprise AI.

Enterprise AI agents need to understand how transactions move across an organization—not just isolated ERP tables. Xpert Systems has synthetic ERP datasets covering Intercompany Transactions, Audit Trails, Purchase Requisitions, Purchase Orders, Supplier Quotations, Contracts & Sourcing, Goods Receipts, Invoice Verification, Inventory Movements, Stock Levels, Warehouse Operations, Supplier Performance, Demand Planning, Supply Planning, Logistics and Returns. These datasets capture workflows such as approvals, budget checks, three-way matching, MRP, inventory traceability, sourcing, shipping and reverse logistics. Together they can support AI model training, post-training, ERP reasoning, agent development, benchmarking and evaluation across Finance, Procurement, Supply Chain and Warehouse Operations. AI Labs, Neo Labs and funded startups building Enterprise AI: DM me or write to [email protected] for samples, licensing or partnerships.

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What would an AI model learn from 28,310 ERP documents spanning approximately 283,000 pages? Xpert Systems is building an ERP Documentation Corpus covering 2,831 ERP codes × 10 documents per code, with each document family generated from a common specification so interfaces, fields, objects and test cases remain connected. The pipeline includes eight automated quality gates covering length, required sections, consistency, object traceability, thin content, assumptions, release contamination and duplication. This creates a large structured corpus for ERP model training, post-training, enterprise reasoning, coding agents, implementation copilots and evaluation. AI Labs, Neo Labs and funded startups interested in ERP AI: DM me or write to [email protected] for samples, licensing or partnership opportunities.

AI Labs: Need human-authored CAD data for Additive Manufacturing and 3D Printing AI? Xpert Systems Inc. is building a collection of 10,000 human-drawn / human-authored Additive Manufacturing CAD parts, created individually by human CAD designers. Every part will be delivered in two formats: STEP (.STEP/.STP) Native SolidWorks Part (.SLDPRT) The 10,000-part collection can span: • Lattice-based components • Topology-optimized parts • Conformal-cooling designs • Complex additive-manufacturing geometries The important distinction: These are human-authored CAD designs—not 10,000 files automatically generated by changing parameters in a script. Additive manufacturing is especially interesting for AI because it enables geometries that can be difficult or impossible to manufacture using conventional machining. That makes it a valuable domain for teaching AI systems to understand the relationship between geometry, design intent and manufacturing approach. Potential applications include: CAD Foundation Models | Text-to-CAD | Generative Design | CAD Agents | Engineering Copilots | 3D Reasoning | Feature Recognition | Design Understanding | Manufacturability Reasoning | Multimodal Engineering AI And unlike many CAD categories, additive manufacturing can cut across aerospace, automotive, medical devices, robotics, energy and industrial engineering. Our approach: Commission purpose-built human-authored CAD specifically for AI Model and AI Agent Training. 10,000 human-authored Additive Manufacturing CAD parts. 10,000 STEP files + 10,000 native SolidWorks Part files. Additive Manufacturing / 3D Printing is Category 11 of 54 in our human-authored CAD program. DM Xpert Systems Inc. or write to [email protected] for samples, specifications, the CAD catalog, custom human-authored CAD programs, and licensing options.

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AI models and agents learning engineering need more than text—they need to understand real CAD geometry, design intent and engineering structures. Xpert Systems can provide CAD datasets that include both synthetically generated files and CAD models authored by human designers, across areas such as mechanical engineering, aerospace, automotive, industrial equipment, construction and other engineering domains. Datasets can be created at scale and structured for model training, multimodal reasoning, CAD generation, design understanding, evaluation and engineering-agent development. Human-authored CAD is particularly valuable for teaching models the patterns, decisions and variations created by actual designers. AI labs building engineering or CAD intelligence: DM me or write to [email protected] for samples, custom datasets and partnerships.

I would not trust an LLM for any ERP task. Our results here... None of the top 4 Frontier Models can be trusted as of now docs.google.com/document/d/1…

100,000 Hand-Drawn Energy & Power Systems CAD STEP Files — Available for AI Training Energy infrastructure combines mechanical, electrical, structural, thermal, and fluid-system engineering across some of the world's most complex physical assets. AI models and engineering agents need to understand the actual 3D components engineers design, manufacture, install, and maintain. That requires real CAD geometry created by humans. Xpert Systems is building 100,000 human-created / hand-drawn Energy & Power Systems CAD models delivered as native STEP files for AI model, multimodal model, CAD copilot, and engineering agent training. The collection can include: • Turbine components • Generator components • Pumps & compressors • Valves & actuators • Pipes, fittings & flanges • Heat exchangers • Pressure vessel components • Boiler components • Cooling system components • Electrical enclosures • Transformer components • Switchgear components • Busbar & conductor assemblies • Battery enclosures & racks • Battery Energy Storage System components • Solar mounting structures • Wind turbine components • Equipment skids • Structural supports & brackets • Maintenance fixtures & tooling The dataset can represent variations in geometry, dimensions, materials, interfaces, pressure classes, mounting configurations, thermal requirements, tolerances, and manufacturing processes. Why Human-Created Energy CAD? Human-created CAD can support training and evaluation for: CAD generation → geometry understanding → component recognition → design reasoning → engineering copilots → multimodal models → maintenance agents → autonomous engineering agents Models can learn how components connect across larger power generation, transmission, renewable energy, battery storage, and industrial energy systems. 100K CAD STEP Files Available for Energy & Power Xpert Systems can work with AI labs, utilities, energy companies, equipment manufacturers, and Enterprise AI teams to define: Component families → design variations → complexity → materials → manufacturing processes → engineering metadata → STEP deliverables We can also develop custom human-created CAD datasets for specific energy systems, equipment families, or AI training objectives. Mechanical Engineering. Construction. Semiconductors. Data Centers. Automotive. Aerospace. Energy. Robotics. Industrial Equipment — and many more CAD-intensive domains. Interested in licensing 100K human-created Energy & Power CAD STEP files or commissioning a custom CAD dataset? DM Xpert Systems Inc. or write to [email protected] for the catalog, samples, specifications, and licensing options.

Unless you are a software company like us who are producing CCE (100 Continuous Connected Enterprises) and 1 million CAD files - to just name a few. Shamelessly plugging our full catalog here xpertsystems.ai/catalogs/Syn…

AI models learning engineering need specialized CAD data that reflects the structures engineers actually design. Xpert Systems has CAD datasets covering Floor Plans, Lattice Towers, LGS Frames, Moment Frames, Pallet Racks, Pipe Racks, Portal Sheds, Precast Frames, Pressure Vessels, Roof Trusses, Scaffolds, Sheet-Metal Flat Patterns & Nesting, Solar Mounts, Stair Platforms, Steel Frames and Timber Frames. These datasets can support CAD understanding, design generation, structural reasoning, drawing interpretation, design modification and engineering-agent training. They can also become the foundation for benchmarks and increasingly difficult tasks where AI must understand requirements, create or modify designs and validate its outputs. AI labs, Neo Labs and funded engineering-AI startups: DM me or write to [email protected] for samples, licensing or partnerships.

Would a real oil & gas company sell five years of its complete connected operating data to an AI lab? Leases. Wells. Production. Operations. Contracts. Financial records. Internal communications. For a real energy company, this information can contain enormous commercial value. Proprietary operating information, contractual restrictions, employee data, financial information, competitive intelligence and internal communications make complete enterprise-level data extremely difficult to obtain for AI training. Yet how will AI agents learn how an energy company actually operates if all they see are isolated documents and disconnected datasets? Xpert Systems Inc. created: #15: Saltfork Energy Corporation Saltfork is a connected enterprise corpus for upstream oil & gas. It contains: 15 linked process layers 60 months One invented company rendered end to end And the connection extends all the way: From the leases under the ground → through the enterprise → to the audited ledger → and even the email threads employees sent each other about both. That last piece is particularly important for Enterprise AI. Real enterprises don't operate exclusively through structured databases. People communicate. They discuss problems. They make decisions. Those decisions influence operations. Operations create transactions. Transactions eventually reach the financial statements. An AI agent operating inside an enterprise may need to reason across structured data, business processes, financial records and human communications simultaneously. That is what makes connected enterprise environments interesting for: AI model training AI agent training Enterprise RAG Oil & gas AI Operational reasoning Financial reasoning Cross-process reasoning Long-horizon reasoning Multi-agent systems Enterprise AI evaluations Instead of giving an AI model thousands of unrelated energy documents, imagine giving it an energy company with five years of history. Then ask it to understand what happened—and why. That is the idea behind our Continuous Connected Enterprises. Saltfork Energy Corporation is one of 18 Continuous Connected Enterprises from Xpert Systems Inc. We are looking for partnerships with frontier AI labs, well-funded AI startups, EnergyTech companies, Enterprise AI developers, data companies and research organizations interested in training and evaluating AI against complete connected enterprise environments. Want to put your AI model or AI agent inside a five-year upstream oil & gas company? DM Xpert Systems Inc. or write to [email protected] for samples, specifications, licensing and partnership opportunities.

How good is your AI model or agent when it must make an actual enterprise decision—not answer a general knowledge question? Xpert Systems has developed 16 benchmark/evaluation tasks (Tasks 3–18) covering vendor payments, material master, sales orders, journal entries, work orders, procurement, billing, quality, CRM, production, logistics, warehouse operations and healthcare claims. Across 4,752 decisions, none of the four models tested achieved 100% strict accuracy on any test. We can build similar custom benchmarks around your industry, business process, policies and model—and score not only the final decision but the reasoning, evidence and unsafe actions. AI labs and enterprise AI teams: DM me or write to [email protected] to benchmark your model or agent.