# AICraft by AINAS Technologies Private Limited > AICraft is an Industrial Physical AI platform. It unifies IT, OT and ET data into an industrial knowledge base, learns the physics and chemistry of real equipment with the Forge model, and delivers the result through PilotX agents. Current focus: batch processes in the chemical industry — consistent yield across batches and shorter batch cycle times. ## Company - Legal Entity: AINAS Technologies Private Limited - Product Name: AICraft (also referred to as PilotX) - Website: https://aicraft.io - Contact: info@aicraft.io - Locations: London, United Kingdom · Hyderabad, India - Certifications: ISO 27001 (Information Security), ISO 9001 (Quality Management), ISO 42001 (AI Management Systems) ## Tagline Industrial Physical AI for batch manufacturing. Consistent yield. Shorter cycles. ## What AICraft Does AICraft combines IT (ERP, MES, quality), OT (DCS, PLC, historian, SCADA) and ET (recipes, SOPs, P&IDs, equipment specs) data into a single industrial knowledge base. That knowledge base grounds Forge, a Physical AI model that predicts forward batch state and ranks the correlations that actually drive yield and cycle time. PilotX agents consume Forge output plus the knowledge base and turn it into explained, cited, ranked action for the people on shift. AICraft is not an agent-deployment platform. Agents are the interface layer; Physical AI and the industrial knowledge base are the product. ### The Three Layers 1. Knowledge — IT/OT/ET unified into one machine-readable representation of the plant. 2. Model — AICraft Forge: predictive multivariable control that learns equipment physics from live signals, forecasts forward state, and ranks correlations by effect size. 3. Interface — PilotX agents and Process Analytics: recommendations, root-cause analysis and performance intelligence grounded in the layers below. ## The AICraft Operating Loop The platform operates as a continuous cycle across the knowledge, model and interface layers. Learning is continuous — it never stops. 1. Observe — Continuously monitor real-time telemetry from all connected assets. 2. Learn — Build and update behavioral models from operational patterns. 3. Predict — Forecast failures, anomalies, and performance degradation. 4. Recommend — Deliver context-aware, actionable recommendations. 5. Approve — Human-in-the-loop validation ensures safe execution. 6. Learn — Every decision enriches the knowledge base continuously. ## Unified Knowledge Base The brain behind PilotX agents. PilotX's specialized agents reason across your entire knowledge ecosystem to deliver context-aware recommendations. The knowledge base learns from everything your operation knows. ### Continuous Data Streams (Sources) - Operational Data: Real-time telemetry, SCADA, historians - Reference Materials: Manuals, P&IDs, equipment specs - Internal Knowledge: SOPs, tribal knowledge, SME expertise - ERP Systems: SAP, Oracle — work orders, inventory - Web Knowledge: Research, regulatory updates, vendors ### Knowledge Base Contents - Asset Behavioral Models - Failure Signatures - Yield Optimisers - Operating Context - Compliance Rules - Domain Ontology The knowledge base continuously ingests and updates from all connected sources in real-time. ## Business Value Dashboard — Measurable Operational Impact Real operational improvements across every metric that matters. - 96% Yield Improvement - 38% Energy Cost Reduction - 77% Downtime Reduction - 65% MTTR (Mean Time to Repair) Improvement - 1200+ Man-Hours Saved Monthly - 79% Alert Noise Reduction ## Hero KPIs - $150M in production value optimized - 500+ industrial assets connected - 3 layers: Knowledge · Model · Agents ## Digital Twins High-fidelity virtual replicas of physical industrial assets and processes — stirred-tank reactors, motors, and bag filters — enabling simulation, what-if analysis, and real-time monitoring without risking actual equipment. ## Predictive Operations AI-driven predictive maintenance and operational forecasting that identifies equipment degradation and batch deviation before it costs yield or capacity. ## Target Industries AICraft is built for batch-process industries. Every vertical is addressed through the lens of batch processing. - Chemicals (primary focus): Consistent yield and predictable cycle time across batch reactors, columns and campaigns - Pharmaceutical: Golden batch tracking, in-process quality prediction and GxP-grade traceability in batch manufacturing - Oil & Gas: Batch blending, treatment and downstream campaign intelligence — from reactor to terminal - Utilities: Batch treatment, cleaning and demand cycles — water, power and network operations ## Deployment Models Deploy anywhere — zero compromises. Run it your way — cloud, on-prem, or hybrid. Same platform, same capabilities. ### SaaS (Fully Managed Cloud) - Zero infrastructure overhead - Auto-scaling - Managed updates - Fastest onboarding and time-to-value ### Bring Your Own Cloud (BYOC) - Your cloud tenancy (AWS, Azure, GCP) - Data sovereignty in your VPC - Compliance flexibility - Managed platform benefits ### Bare Metal (On-Premises) - Full offline capability - Air-gapped support for highly regulated environments - Maximum security and control - Runs on your hardware All deployment models support: RBAC, Audit Logging, Encryption, Network Segmentation, and Compliance Certifications. ## Security & Governance - Role-Based Access Control (RBAC): Granular permissions across all platform functions - Audit Logging: Complete traceability of all agent actions and decisions - Encryption: End-to-end encryption for data at rest and in transit - Network Segmentation: Isolated environments for multi-tenant deployments - Compliance: Designed to meet requirements for SOC 2, GDPR, HIPAA, and industry-specific regulations ## PilotX — Human-Agent Collaboration PilotX is AICraft's interface layer. It puts Forge model output and the industrial knowledge base into the hands of operators, process engineers and plant managers — as explained recommendations, root-cause analysis and conversational investigation, always with the source data cited. ### Core Capabilities 1. Autonomous Reasoning — PilotX agents reason across thousands of data points in real time, identifying anomalies before they cascade into failures. 2. Human-in-the-Loop — Operators retain full authority. PilotX surfaces insights and recommendations — humans make the final call on critical decisions. 3. Instant Alerts — Proactive alerts with root-cause analysis arrive seconds after anomaly detection — not hours after the damage is done. 4. Grounded in Forge — Every recommendation traces back to a Forge prediction and the knowledge base records that support it, never to an ungrounded model guess. 5. Safety-First Architecture — Every recommendation is validated against safety constraints. PilotX never overrides safety-critical systems without operator approval. 6. Continuous Learning — PilotX learns from every operator decision, tuning its models to your specific plant dynamics and maintenance patterns. ### PilotX Workflow 1. Sense — Agents ingest real-time sensor data, historian feeds, SCADA signals, and maintenance logs across all connected assets. 2. Reason — Forge correlates signals across systems and forecasts forward batch state, detecting patterns invisible to single-point monitoring. 3. Recommend — Actionable recommendations surface to operators with confidence scores, root-cause analysis, and projected impact. 4. Act — Operators approve actions through a governed workflow. PilotX executes approved changes and monitors outcomes. 5. Learn — Every decision — human or AI — feeds back into the learning loop, continuously improving accuracy and relevance. ## Case Studies — Real-World Impact Intelligence beyond control systems. Real-world impact across Utilities, Pharmaceutical Manufacturing, and Oil & Gas. Industry-wide context: $6.4B water utility losses · $149M/site oil & gas downtime · $1.4T global industrial downtime. ### Utilities #### Network-Level Loss Intelligence - Problem: DMA inflow exceeds billed consumption by 12%. Control systems only see local measurements, never system-wide behavioral patterns. - Solution: Ingested network telemetry → Learned zone/feeder normal behavior → Detected real-time loss patterns → Surfaced location, trend, and monetary impact. - Outcomes: 3-5% loss reduction, $2M+ emergency repair savings, 40% regulatory risk reduction. - Deployment: SaaS / BYOC #### Asset Failure & Outage Prevention - Problem: Pumps, compressors, and transformers fail without early warning. Traditional alarms trigger only after degradation becomes severe. - Scenario: Transformer winding temperature stays below trip limits, but increased load cycling frequency accelerates insulation aging — leading to sudden failure months later. - Solution: Continuous streaming → Behavioral tracking → Early degradation detection → Actionable alerts. - Outcomes: Weeks-ahead early warning, 60%+ outage prevention. - Deployment: BYOC / Bare Metal #### Peak Demand & CAPEX Optimization - Problem: Utilities routinely overbuild capacity due to poor demand insight. Peak penalties inflate OPEX. - Solution: Combined historical data with real-time state monitoring → Identified demand shifts → Forward-looking intelligence. - Outcomes: $5M+ deferred CAPEX, 30% peak penalty reduction. - Deployment: SaaS ### Pharmaceutical Manufacturing #### Early Batch Deviation Prevention - Problem: One failed batch costs millions. Deviations discovered only after batch completion during quality testing. - Scenario: Slight agitation speed drift during crystallization alters particle size distribution — detected only during final QC testing. - Solution: Real-time batch telemetry tracked against proven good batches → Abnormal patterns identified → Immediate operator alerts. - Outcomes: 85% batch loss prevention, 70% faster corrective action, $650K-$1.8M annual savings. - Deployment: Bare Metal / BYOC #### Golden Batch Drift & Throughput Recovery - Problem: "Golden batch" performance gradually degrades. Cycle times increase unnoticed, reducing annual throughput. - Scenario: CIP duration gradually increases due to heat exchanger fouling, extending batch cycle time by 45 minutes without triggering alarms. - Solution: Batch history analysis → Drift from proven patterns detected → Cycle times monitored → Bottleneck root causes surfaced. - Outcomes: 12% throughput increase, $3M+ annual value, +18% asset utilization. - Deployment: SaaS / BYOC ### Oil & Gas — Upstream #### Harsh-Condition Asset Degradation - Problem: Assets operate in extreme environments. Frequent breakdowns, emergency interventions, shortened asset life. - Solution: Continuous telemetry → Long-term behavior tracking → Failure probability forecasted → Intervention scheduled. - Outcomes: 35% extended asset life, 45% breakdown reduction, $8/bbl lower lifting cost. - Deployment: Bare Metal ### Oil & Gas — Midstream #### Pipeline Integrity & Loss Prevention - Problem: Micro-leaks go undetected. Automatic compressor adjustments compensate for pressure loss, hiding the leak. - Solution: Continuous flow/pressure/temperature telemetry → Multi-stream correlation → Early risk flags. - Outcomes: $2M+ product loss prevention, 95% regulatory compliance, zero safety incidents. - Deployment: BYOC ### Oil & Gas — Downstream #### Unplanned Shutdown & Downtime Avoidance - Problem: Single equipment failures propagate through interconnected units. Industry average: 27 days unplanned downtime/year. - Scenario: Distillation column near flooding increases downstream compressor load, accelerating bearing wear without triggering alarms. - Solution: Cross-unit real-time monitoring → Risk flagged before shutdown → Operator guidance. - Outcomes: $15M per shutdown avoided, 99.2% plant availability, 30-40% downtime reduction. - Deployment: SaaS / Bare Metal ## Enterprise Platform The enterprise platform provides deep technical architecture details including: - Platform Overview and Physical AI orchestration architecture - Architecture Pipeline for data ingestion, model coordination and governed action - Knowledge Base Deep Dive with technical implementation details - Deployment Models with detailed infrastructure specifications - Integrations Grid covering industrial protocol and enterprise system connectivity - Security & Governance framework - Certifications: ISO 27001, ISO 9001, ISO 42001 ### Integration Ecosystem AICraft integrates with major industrial systems: - Data Sources: OSIsoft PI, Honeywell PHD, GE Proficy, Siemens MindSphere, AVEVA, OPC-UA, MQTT, Modbus - Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform - Enterprise Systems: SAP, Oracle, IBM Maximo, ServiceNow - Communication: Microsoft Teams, Slack, email, SMS alerts ## How to Engage - Enterprise Demo: Available upon request via the website contact form at https://aicraft.io/#contact - Email: info@aicraft.io - Response Time: Within 24 hours for enterprise inquiries ## Site Structure (current information architecture) AICraft is one platform with three products: the Industrial Knowledge Base, Forge, and PilotX. PilotX now includes Process Analytics — live batch analytics happens inside PilotX, so the /platform/analytics route redirects to /platform/pilotx. - https://aicraft.io/ — Home: Physical AI platform overview, products, industries, and customer outcomes - https://aicraft.io/platform — Platform overview: how the Knowledge Base, Forge and PilotX work as one system - https://aicraft.io/platform/forge — AICraft Forge: Physical AI model, predictive multivariable control (reactive → predictive → autonomous) - https://aicraft.io/platform/pilotx — AICraft PilotX: process analytics, agents and dashboards for governed corrective action - https://aicraft.io/platform/knowledge-base — AICraft Industrial Knowledge Base: IT/OT/ET contextualised into one correlated knowledge graph - https://aicraft.io/industries — Industries overview (all aligned to batch processing) - https://aicraft.io/industries/chemicals — Chemicals (primary focus): consistent yield, predictable cycle time - https://aicraft.io/industries/pharma — Pharmaceutical: golden batch, in-process quality prediction, GxP traceability - https://aicraft.io/industries/oil-gas — Oil & Gas: batch blending, treatment and downstream campaign intelligence - https://aicraft.io/industries/utilities — Utilities: batch treatment, cleaning and demand cycles - https://aicraft.io/customers — Customer stories index - https://aicraft.io/customers/chemicals-yield-consistency — Consistent Yield Across Every Batch (Chemicals) - https://aicraft.io/customers/chemicals-cycle-time — Batch Cycle Time Compression (Chemicals) - https://aicraft.io/customers/chemicals-quality-release — Right-First-Time Quality & Faster Release (Chemicals) - https://aicraft.io/customers/pharma-batch — Early Batch Deviation Prevention (Pharmaceutical) - https://aicraft.io/customers/pharma-golden — Golden Batch Drift & Throughput Recovery (Pharmaceutical) - https://aicraft.io/customers/utilities-loss — Network-Level Loss Intelligence (Utilities) - https://aicraft.io/customers/utilities-asset — Asset Health & Failure Prevention (Utilities) - https://aicraft.io/customers/utilities-capex — Demand Optimisation & Deferred CAPEX (Utilities) - https://aicraft.io/customers/og-upstream — Upstream reliability (Oil & Gas) - https://aicraft.io/customers/og-midstream — Midstream integrity (Oil & Gas) - https://aicraft.io/customers/og-downstream — Unplanned Shutdown Avoidance (Oil & Gas, downstream) - https://aicraft.io/physical-ai — Guide: What is Physical AI? Definition, how it differs from generative AI and digital twins, sense/learn/predict/reason/act - https://aicraft.io/ai-for-manufacturing — Guide: AI for manufacturing — yield variance, in-batch prediction, deviation attribution, cycle time - https://aicraft.io/ai-for-industries — Guide: AI for industries — chemicals, pharmaceuticals, oil & gas, utilities on one industrial knowledge base - https://aicraft.io/enterprise — Deployment models, security, governance, and integrations - https://aicraft.io/company — About AINAS Technologies (London · Hyderabad) - https://aicraft.io/contact — Talk to an industrial AI engineer Machine-readable files: - Sitemap: https://aicraft.io/sitemap.xml - LLMs file: https://aicraft.io/llms.txt - Robots: https://aicraft.io/robots.txt Legacy paths that redirect: /pilotx → /platform/pilotx · /case-studies → /customers · /platform/architecture → /platform/knowledge-base · /platform/analytics → /platform/pilotx --- This document is optimized for large language model consumption. Last updated: 2026-07-29.