Learning tools, simulation and deployment labs, add-on studios and engineering digital twins — all in one desktop software for Windows and macOS. Prime unlocks 22 core labs. Add-on studios are ₹150/month each.
Interactive dashboards that teach the core ideas of AI, ML, fuzzy logic and Python, step by step.
Learn regression, classification and K-nearest neighbours by working with interactive datasets. Fit models, adjust their settings and watch decision boundaries and error curves update live. Compare algorithms side by side to see which one suits each problem and why.
Explore unsupervised and associative-memory neural networks through visual experiments. Watch a self-organising map arrange itself to cluster data, step by step. Store patterns in a Hopfield network and see it recall them from noisy or incomplete inputs.
Understand the foundations of artificial intelligence before machine learning. Step through search algorithms, game trees with minimax, constraint satisfaction and logical inference. Each algorithm is animated so you can follow every decision it makes.
Learn fuzzy logic from first principles, starting with fuzzy sets and membership functions. Build a fuzzy inference system, write its rules and compare defuzzification methods. Finish by watching your fuzzy controller drive a simple system in real time.
An interactive dashboard that explains how AI agents work. See how an agent plans a goal, chooses tools, remembers context and checks its own results. Follow multi-agent examples to learn how agents divide and coordinate work.
A complete Python course, from your first line of code to working programs. Each topic has a clear lesson, runnable examples, hands-on challenges and quick quizzes. Track your progress through the course and earn a completion certificate.
Build a neural network layer by layer and see exactly how it learns. Follow the forward pass, the error and every backpropagation step with the maths shown. Change weights, learning rate or activations and watch the training curves respond.
Full working labs to build, train, explain and deploy models — from data acquisition to edge devices.
Collect live data from sensors and hardware over WiFi, USB serial or Bluetooth Low Energy. Log, plot and label signals in real time, ready for training models. Then deploy a trained model to an edge device and monitor it as it runs.
Design, train and deploy AI agents in a guided, stage-by-stage workflow. Define the task, configure agents and train them, then compare how they perform. Connect agents to tools and data through the Model Context Protocol (MCP) to automate real work.
A complete machine-learning teaching platform covering the full pipeline. Move from data and exploratory analysis through preprocessing, modelling, training and evaluation. Make predictions on new data and export the code and reports for your project.
Configure and train deep neural networks without writing setup code. Choose an architecture, prepare your data and follow training with live loss and accuracy plots. Visualise layers, features and results to understand what the network has learned.
A full platform for designing fuzzy logic controllers for engineering systems. Edit membership functions and rule bases visually, then test the controller with live inputs. Study control surfaces and rule activations, and export the controller for use elsewhere.
Run metaheuristic optimisation algorithms on benchmark and custom problems. Watch the search move across the solution space and follow convergence as it happens. Compare optimisers head to head on a leaderboard to choose the best one for your task.
Explain why a machine-learning model makes the predictions it does. Apply SHAP, LIME and feature-attribution methods to your own models and data. Produce clear visual explanations that build trust and support reports and reviews.
A Jupyter-style Python notebook that runs inside VeriAI Studio. It runs on WebAssembly, so there is nothing extra to install and no internet connection needed. Write code, run cells and plot results right next to your labs.
Simplify high-dimensional data and reveal its structure. Compare 17 reduction methods, including PCA, t-SNE, SVD and UMAP, on the same dataset. See clusters and patterns side by side to choose the right method for your data.
Studios for engineering agentic workflows, surveying literature, writing papers, building Simulink models, designing PCBs and design thinking.
A studio for engineering complete agentic AI workflows. Design multi-step workflows, connect agents to tools through MCP and test them end to end. A built-in user guide walks you through each stage of the build.
Run a systematic literature review from search to summary. Find papers through CrossRef, screen them and document every step with a PRISMA flow diagram. Organise your references and export them as BibTeX for your manuscript.
Prepare research manuscripts in one place, from first draft to final file. Structure sections, figures, tables and references in a consistent format. Export the finished manuscript to Word, LaTeX or PDF.
Build MATLAB / Simulink models without assembling every block by hand. Create models from blocks, equations or a written requirement, and lay them out automatically. Validate the model and export a ready-to-open .slx file.
Take a project through the five stages of design thinking. Empathise with users, define the problem, generate ideas, prototype and test them. Templates and guided steps keep the whole team's work organised in one place.
Turn a circuit drawing into a manufacturable board. Upload a schematic image or PDF, size the components and simulate it on a 4-channel scope with FFT and cursors. Real parts are picked from the simulated stresses, the board is placed and routed to IPC-2221 with DRC, and exported as a KiCad project with Gerbers, drill and BOM.
Interactive digital twins of real engineering systems — motors, batteries, smart grids, vehicle networks and solar PV.
A digital twin for electromagnetic and thermal design of electrical machines. Run finite-element analysis to see magnetic fields, flux and losses. Combine thermal models with efficiency maps to evaluate a design across its operating range.
A digital twin of an electric-vehicle battery and its management system. Simulate cell balancing and estimate state of charge (SOC) and state of health (SOH). Generate remaining-useful-life (RUL) datasets for battery analytics and research.
A 3D digital twin of the brushless DC motor used in electric vehicles. Drive the motor from a 3D cockpit and follow Hall-sensor commutation step by step. Inspect phase currents and voltages on a built-in oscilloscope.
A digital twin of the standard IEEE 33-bus radial distribution network. Add distributed energy resources such as solar and storage, and run power-flow studies. Analyse voltage profiles, losses and harmonic distortion (THD) across the grid.
A digital twin for load testing a three-phase induction motor. Apply loads and follow torque-slip, speed and current dynamics as they change. A thermal model shows how temperature rises under each operating condition.
A digital twin of an in-vehicle CAN and CAN-FD network. Simulate CAN 2.0B and CAN-FD traffic between ECUs and measure bus load. Inject faults and test with hardware-in-the-loop (HIL) to check how the network responds.
A digital twin for resilient, agent-based control of power grids. Multiple AI agents manage power flow and coordinate across the network. Test islanding and black-start scenarios to see how the grid recovers from outages.
A digital twin for comparing intelligent control strategies. Apply neuro-fuzzy (ANFIS) and sliding-mode controllers to the same system. Tune controller parameters automatically with particle swarm optimisation (PSO).
A digital twin of a solar PV array with maximum power point tracking (MPPT). Simulate partial shading and the multiple power peaks it creates. Track the true maximum power point with a hybrid PSO-GA algorithm and compare results.