AI-Powered Vastu Consulting at Scale
We built the platform that turns a floor plan into a Vastu compliance report. The guidance that used to require a human consultant now arrives in 30 seconds, and the same rules apply every time.
In short: The first Vastu consultant built on machine learning. Upload a floor plan, get a compliance report. Kshetra is a PropTech product that uses computer vision and machine learning to read floor plans against Vastu Shastra principles. A user uploads a floor plan.
Kshetra is a PropTech product that uses computer vision and machine learning to read floor plans against Vastu Shastra principles. A user uploads a floor plan. The model returns a compliance report with room-by-room scoring, directional analysis, and concrete fix suggestions. The whole loop runs in under 30 seconds.
- Vastu consultations are subjective, expensive, and hard to book. Most homebuyers never get one.
- No labelled dataset existed for training a model on Vastu rules. We had to build one before we could train anything.
- Floor plans arrive as photos, scans, PDFs, and hand-drawn sketches. The vision pipeline has to make sense of all of them.
- The audience cares about Vastu. The product has to be technically rigorous and culturally literate, or it loses them on day one.
Custom Dataset & Model Training
Built a dataset of 5,000+ floor plans, each annotated against Vastu rules. Trained a multi-label classifier that scores compliance across 40+ parameters per plan.
Computer Vision Pipeline
Built a floor-plan parser with OpenCV and a custom CNN. It picks out rooms, walls, entrances, and cardinal directions from images that arrive in a dozen different formats.
Report Writer
An LLM turns the raw scores into a written report. The tone is tuned to be culturally appropriate, and the fixes are ranked by impact.
Consumer-Grade UX
The product is mobile-first. Upload a floor plan, get the audit back, share it. The whole flow is designed to feel like taking a photo, not running a scan.
AI & ML
Backend
Frontend
Cloud
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Get Free ConsultationFrequently asked questions
How long does a typical project take?
Most MVPs ship in 8 to 12 weeks. Larger products with AI components typically take 12 to 20 weeks. We will give you a realistic timeline once we have had the discovery call.
What does a prototype cost?
Prototypes start at $1,000 for a focused proof of concept. Full MVPs sit between $5,000 and $25,000 depending on complexity, how much AI is involved, and which platforms you need to ship on.
Do you work with early-stage startups?
Yes. About half our clients are pre-seed to Series A. We are used to requirements that move, budgets that are tight, and the occasional pivot.
Can you integrate AI into our existing product?
Yes. We regularly add search, recommendations, automation, and content generation to existing codebases. A full rebuild is rarely required.
What AI models and platforms do you use?
We do not standardise on one vendor. We work with OpenAI GPT, Anthropic Claude, Google Gemini, open-source models like Llama and Mistral, and custom fine-tuned models where the use case calls for it.
How do you handle ongoing maintenance?
We run monthly retainers for post-launch support, monitoring, bug fixes, and feature work. Most clients stay on one after launch.
What if we already have a technical team?
We plug in however fits best. Embedded engineers inside your team, a parallel squad on a separate workstream, or advisory consultants reviewing your roadmap. We work with whatever stack and rhythm your team already uses.