AI Development
Software where the model is part of the product, not a feature bolted on at the end.
In short: Software where the model is part of the product, not a feature bolted on at the end. Custom models, LLM integrations, and AI-native products. We design the model, the interface, and the feedback loop together, because that is where an AI product gets useful or fails.
AI development is the work of building software where a model does part of the job a person used to do. That covers generative AI, computer vision, prediction, and agents that take action. We treat the model, the interface, and the feedback loop as one design problem, because the parts that make an AI product useful are the parts where those three meet. A model that works in a notebook is not the same as a model that works inside a product people rely on.
Capabilities
Generative AI
LLM integrations tuned to your domain. Copilots, assistants, content pipelines, and search tools that speak the language of your product instead of the model's defaults.
Computer vision
Models that read images, documents, and floor plans. We have shipped vision pipelines that survive bad lighting, scanned PDFs, and hand-drawn sketches.
ML and predictive models
Custom models for forecasting, classification, anomaly detection, and recommendations. Trained on your data, not on a demo set.
Agentic workflows
Agents that take real work off your team. They make decisions inside boundaries you define and hand back to a person when they are unsure.
AI-native products
For products where the AI is the product, not a checkbox. We build the model, the interface, and the data flywheel together.
Governance and guardrails
Evaluation harnesses, safety filters, audit logs, and deployment patterns that keep a production AI system inside your risk appetite.
Deliverables
- Trained and evaluated model artifacts
- Inference API and hosting setup
- Evaluation harness with your test set
- Monitoring for drift, latency, and cost
- Guardrails and audit logging
- Source code and documentation
Tools we build with
AI and ML
Backend
Cloud
How you can work with us
Prototype
2 to 4 weeksA focused proof of concept that proves the hard part of your idea works. Clickable UX, the core feature running end to end, and a link you can share.
Testing the idea with users or investorsMVP
8 to 16 weeksThe smallest product that solves the problem for real users. Built on the same backbone the production system will use, so you do not throw it away when you grow.
Going to market with the first versionFull product
12 to 24 weeksStrategy, design, engineering, the AI work, and launch, run by one team. We take the product from the first whiteboard to production traffic.
Shipping a complete product to customersOngoing retainer
MonthlyAfter launch we stay on. Bug fixes, monitoring, new features, and the work that keeps the product improving once real users are inside it.
Post-launch iteration and supportBuilt with this
Kshetra
The first Vastu consultant built on machine learning. Upload a floor plan, get a compliance report.
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Do you build the model or use an existing one?
Both, depending on the problem. We use frontier models from OpenAI, Anthropic, and Google when they are the right tool. We fine-tune open-source models like Llama and Mistral when you need control, cost savings, or a domain the general models handle poorly. And we train custom models from scratch when the use case needs it. We will tell you which fits before we start.
How do you keep an AI feature from going wrong in production?
Every AI feature ships with an evaluation harness, guardrails, and monitoring. The eval harness catches regressions before deploy. The guardrails keep the model inside the behaviour you signed off on. The monitoring watches for drift, latency spikes, and cost overruns once real traffic hits.
Can you add AI to a product we already ship?
Yes. We regularly add search, recommendations, copilots, and automation to existing codebases. A full rebuild is rarely required. We start with a small integration that proves the value, then expand.
How long does an AI project take?
A focused prototype takes 2 to 4 weeks. An AI feature inside an existing product takes 4 to 10 weeks. A full AI-native product takes 12 to 20 weeks. We give you a realistic timeline after the discovery call.
AI Consulting and Strategy
Find the AI use cases worth building, sequence them into a roadmap, and decide on the architecture before any code is written.
Explore →AIGenerative AI Development
LLM integrations tuned to your domain. Content pipelines, copilots, and assistants that speak the language of your product.
Explore →AppMobile App Development
Cross-platform and native mobile apps. React Native and Flutter for one codebase, Swift and Kotlin when a screen needs to go native.
Explore →AppWeb App Development
Production web apps in React, Next.js, Vue, and Nuxt. Dashboards, multi-tenant SaaS, internal tools, and design systems.
Explore →CloudCloud and DevOps
Cloud infrastructure, CI/CD, and observability. Mostly AWS and Google Cloud, with Terraform for the parts you want reproducible.
Explore →StrategyUI and UX Design
Wireframes, prototypes, and design systems. Every screen traces back to a user research finding, not to a hunch.
Explore →Want this for your product?
Tell us what you are building. We will say back whether we can help.
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