Eight representative scenarios across technical animation, AR/XR, exploded renders, and training visuals — how Vidhaath solves real engineering communication problems.
A patented wrist assembly that reduced cycle time by 23% — invisible inside every brochure.
A robotics manufacturer had developed a six-axis articulated arm for automotive assembly lines. The mechanism was genuinely innovative — a patented wrist assembly that reduced cycle time by 23%. But every sales presentation stalled at the same point: buyers could not visualise how the wrist moved through tight tolerances in a live cell. The product brochure showed the arm at rest. It could not show the thing that made it worth buying.
CAD geometry was prepared and cleaned in Blender. AI-assisted texture synthesis accelerated the creation of realistic surface materials for the fixture environment — cutting asset production time by roughly half. All mechanical motion paths were hand-keyed by a mechanical engineer against the actual CAD joint limits. AI was used on the environment; engineering accuracy was never delegated to it.
Distributor network across Tier-2 cities — customers making Rs 40,000 decisions based on a photograph.
A premium kitchen appliance brand was expanding its distributor network across Tier-2 cities. Physical showrooms were expensive to set up and inconsistent in presentation quality. Distributors were selling off printed catalogues — and customers were making Rs 40,000 purchase decisions based on a photograph. The brand needed its products in front of customers at full scale, in context, wherever the distributor happened to be.
Product 3D models were built from manufacturer CAD files. AI-assisted PBR material generation produced realistic surface finishes for appliance-grade stainless and tempered glass — materials that traditionally require hours of manual shader work. WebXR integration, lighting rigs, and interaction logic were all coded by hand. The result: photorealistic material quality at a fraction of the production time, with full technical control retained over the AR behaviour.
Technicians were getting the assembly sequence wrong in the field. The training material was from 2011.
A surgical instrument manufacturer needed to train distributor service technicians on a complex sterilisation-compatible biopsy forceps — 47 components, three sub-assemblies, and a spring mechanism that failed if reassembled in the wrong sequence. Their existing training material was a 14-page PDF with hand-drawn diagrams from 2011. Technicians were getting the sequence wrong in the field.
Each of the 47 components was modelled from engineering drawings. AI-assisted rendering (denoising and lighting estimation) was used to achieve final render quality in a fraction of the compute time that traditional path-tracing requires. Component annotation layouts were designed manually — automated placement tools consistently placed labels over critical geometry, defeating the purpose. Hybrid approach: AI accelerated render quality; human judgment controlled what the viewer needed to see.
The innovation was inside the housing. The housing was closed during operation.
A CNC machine tool manufacturer had developed a new turret indexing system that was quieter, faster, and 40% more accurate than the industry standard. Every technical evaluation cleared — but plant managers signing off on capital expenditure kept asking the same question: "Can you show me exactly how it works?" The innovation was inside the housing. The housing was closed during operation.
Mechanism motion paths were defined entirely from CAD data — the cam profile, coupling geometry, and servo rotation were not approximated or artistically interpreted. AI tools were used in post-production: automated scene colour grading and motion blur enhancement to make the slow-motion segment feel cinematic without additional render passes. No AI was involved in defining the mechanical motion itself — that required engineering judgement about what a plant manager needed to understand, not what looked impressive.
New technicians were taking 4–6 weeks to become proficient. Errors during the 14-step procedure were the leading cause of rework.
An aerospace MRO facility was onboarding technicians onto a complex hydraulic actuator overhaul procedure. The existing process documentation was a 200-page PDF written in 1998, with 2D engineering drawings that took a trained eye to interpret. New technicians were taking 4–6 weeks to become proficient. Errors during the 14-step seal replacement procedure were the leading cause of rework.
Step sequencing was defined in collaboration with the MRO facility's lead technician — the person who knew exactly where new recruits went wrong. AI-assisted depth-of-field rendering and environment lighting were used to draw visual attention to the active component in each step, replacing the need for manual highlight overlays in post-production. The "what to look at" decisions — the engineering and pedagogical judgement — stayed human throughout. AI accelerated how we communicated those decisions visually.
Their flagship compressor weighed 1,200kg and cost Rs 18 lakhs to transport. There had to be a better way.
A compressed air systems manufacturer was exhibiting at an industrial trade fair. Their flagship compressor unit weighed 1,200kg and cost Rs 18 lakhs to transport and install at a booth. Their competitors were bringing smaller demo units. The manufacturer needed a way to show the full-scale, full-specification machine — without the logistics bill and without asking buyers to imagine it from a brochure.
The 3D model was built from the manufacturer's CAD assembly. AI-assisted PBR material generation produced the industrial paint, cast iron, and rubber hose surface finishes needed for photorealism at trade show scale. The AR session management, hit-test surface detection, and interaction logic were hand-coded using the WebXR Device API. IBL lighting was loaded from a custom HDR map of a typical exhibition floor. AI handled materials; engineering and code handled everything the customer interacted with.
Configuration approval was the single biggest bottleneck in their sales cycle — 2–3 days per quote.
A modular conveyor manufacturer sold systems that were configured differently for every customer — length, width, drive unit position, incline angle, surface type, and side-guard configuration all varied. Their sales engineers were spending 2–3 days per quote producing CAD renders for customer approval. Customers would review the render, request a change, and the cycle would restart. Configuration approval was the single biggest bottleneck in their sales cycle.
The parametric model logic — how segment length changes affect belt tension geometry, how drive unit position constrains frame design — was defined by a mechanical engineer before any code was written. AI-assisted code generation accelerated the Three.js configuration UI development. The engineering constraints that govern what configurations are physically valid were hard-coded by hand. Customers can only configure what the machine can actually be built as — the AI-assisted UI serves valid engineering, not the other way around.
The lag between a sensor reading and a technician understanding its implication — in context, on the machine — was costing hours per incident.
A facility management company overseeing a large commercial chiller plant was experiencing recurring failures in one compressor bank. Maintenance engineers were diagnosing problems by walking the plant floor, reading gauges, cross-referencing a 2D P&ID drawing, and making judgment calls. The lag between a sensor reading and a technician understanding its implication — in context, on the machine itself — was costing hours of diagnostic time per incident.
The 3D plant model was built from P&ID drawings and site survey data — traditional engineering modelling work. AI-assisted anomaly detection was explored as a layer beneath the visualization: a pattern recognition model trained on the plant's historical sensor data flags components showing early-warning signatures before they cross alert thresholds. The AR interface then surfaces these flags visually — not as a dashboard alert, but as a colour state on the physical component the technician is standing next to. Human engineering defined the model and the thresholds; AI found patterns in the data that no manual review would have caught in time.
Tell us about your product and the communication challenge you're facing. We'll recommend the right approach.