Glossary
Enterprise AI, defined without the fog.
Every term we use, explained in plain language. If a vendor can't define it this simply, ask why.
- Agentic AI
- AI systems that autonomously execute multi-step business workflows — reading documents, calling tools and APIs, updating systems of record, and escalating exceptions to humans — rather than only generating text responses.
- Enterprise AI solutions
- Computer Vision
- AI that interprets images and video — detecting product defects at line speed, recognizing faces for attendance, or monitoring safety compliance — typically running on cameras already installed in a facility.
- Attendance AI
- Court-First Lawyer Discovery
- Finding legal representation by starting from the specific court where a case will be heard — rather than searching by city — since advocates practice in specific courts. The model pioneered by VakilBarak in India.
- VakilBarak
- Data Sovereignty
- The legal and regulatory requirement that data remains within specified jurisdictions and controlled infrastructure — driven by laws like India's DPDP Act and the EU GDPR. For AI, it means the model must come to the data, not the reverse.
- Private LLMs and data sovereignty
- Demand Forecasting
- Machine-learning prediction of future demand at SKU, store, or service level, trained on seasonality, promotions, and market signals — replacing spreadsheet extrapolation and reducing both stockouts and overstock.
- Supply Chain Intelligence
- Digital Twin
- A live virtual replica of a physical operation — a warehouse, production line, or pipeline — continuously synchronized from sensor and system data, used to simulate changes risk-free before applying them in the real world.
- Warehouse Digital Twin
- Edge AI
- Running AI model inference directly on devices at the point of action — factory floors, pipeline compressor stations, buses, campuses — instead of in a distant cloud. Essential where connectivity is intermittent or latency is safety-critical.
- Pipeline Leak Detection
- MLOps
- The engineering discipline of running machine-learning models in production: versioning, evaluation, drift monitoring, retraining pipelines, and rollback — what makes AI behave like reliable infrastructure instead of an experiment.
- Custom AI Models
- Multi-Agent System
- An architecture where several specialized AI agents coordinate on a task — for example a triage agent handing work to a domain specialist with different tools. Justified only when workflows genuinely branch into specialties.
- AI Research
- No-Show Prediction
- Machine-learning models that estimate the probability a patient will miss an appointment, enabling smart reminders, standby lists, and intelligent overbooking that recover otherwise-lost clinical capacity.
- Healthcare CRM
- OEE (Overall Equipment Effectiveness)
- The standard manufacturing metric combining machine availability, performance, and quality into one percentage. Unplanned downtime is its biggest destroyer — and the primary target of predictive maintenance.
- Manufacturing
- Offline AI
- AI software that runs entirely on a local device with no internet connection, cloud storage, or external API calls — keeping sensitive data (like privileged legal documents) on the machine it was created on.
- LocalDraft AI
- Pipeline Leak Detection
- Continuous monitoring of pipeline networks using pressure-transient analysis, acoustic sensors, and machine learning to detect leaks within minutes of onset — versus hours or days with manual SCADA review.
- Pipeline Leak Detection
- Predictive Maintenance
- Using sensor data (vibration, temperature, current) and machine-learning models to predict equipment failures days or weeks before they happen, so maintenance is scheduled inside planned windows instead of reacting to breakdowns.
- Predictive Intelligence
- Private LLM
- A large language model that runs entirely inside an organization's own infrastructure (VPC, data center, or edge device), so prompts, outputs, and training data never leave controlled systems — required for regulated and data-sovereign environments.
- Custom AI Models
- RAG (Retrieval-Augmented Generation)
- A technique where a language model retrieves relevant passages from a governed knowledge base before answering, grounding its responses in an organization's actual documents rather than only its training data.
- Enterprise AI solutions
- Supply Chain Control Tower
- A single operational view that unifies procurement, inventory, logistics, and demand data in real time, layered with AI that forecasts demand, flags disruption risk, and automates replenishment decisions.
- Supply Chain Intelligence
- Vector Database
- A database that stores text, images, and other content as numerical embeddings so semantically similar items can be found instantly — the retrieval backbone of RAG systems and enterprise AI search.
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