Question 1

What is GenOrbit Innovations?

Accepted Answer

GenOrbit Innovations is an AI-native software development company headquartered in Kalyani Nagar, Pune, India. It designs and engineers custom software, generative AI applications, LLM and RAG systems, and AI agents for startups and enterprises worldwide, and builds its own AI products including AstroVedica, Navamsha AI, Doco AI and LeadsLe.

Question 2

Who founded GenOrbit Innovations?

Accepted Answer

GenOrbit Innovations was founded in Pune by software engineers Avinash Chandan and Abhijeet Shankar. Both founders work directly on client engagements — Avinash leads product strategy and AI/ML, and Abhijeet leads system architecture and engineering.

Question 3

What is an AI-native product engineering company?

Accepted Answer

An AI-native product engineering company builds software where artificial intelligence is embedded at the foundation — in the data layer, workflows, and user experience — rather than bolted on as an afterthought. At GenOrbit, we architect intelligent systems from day one, with AI as a core capability, not a feature checkbox. We prove this approach through our own live products: Navamsha AI, Doco AI, and LeadsLe.

Question 4

How long does it take to build an AI product from scratch?

Accepted Answer

A focused MVP with core AI functionality typically takes 4-8 weeks. Full production systems with evaluation harnesses, observability, and scale-ready architecture take 3-6 months depending on complexity. We bias toward shipping thin slices early — working systems with real interfaces and real models — so you get user signal before committing to full scope.

Question 5

What is a RAG pipeline and do I need one?

Accepted Answer

RAG (Retrieval-Augmented Generation) connects a language model to your private data sources — documents, databases, knowledge bases — so it can answer questions grounded in your specific information rather than its training data alone. You need RAG when your product requires accurate, domain-specific answers: customer support, internal knowledge bases, clinical guidelines, or specialized content like Vedic astrology texts. You don't need RAG for general creative tasks or when your use case works well with the model's built-in knowledge.

Question 6

How is GenOrbit different from a regular software agency?

Accepted Answer

Three differences: we build our own products first (Navamsha AI, LeadsLe, Doco AI are live in market), you work directly with founders who engineer the work (no account managers or telephone-game specs), and we embed AI at the foundation rather than adding chat bubbles to existing apps. We're operators who happen to take client work — not a service shop optimizing for billable hours.

Question 7

Can you build an AI product for a startup with a limited budget?

Accepted Answer

Yes. We scope honestly and prioritize the thinnest viable slice that validates your core hypothesis. A focused MVP with one AI feature done well beats a sprawling prototype with five half-built features. We've helped founders go from idea to live product in 6 weeks by ruthlessly prioritizing what matters for user validation.

Question 8

What tech stack does GenOrbit use for AI products?

Accepted Answer

Our default stack is TypeScript + Python: React or Next.js on the frontend, FastAPI on the backend, PostgreSQL for data, and LangChain or LangGraph for AI orchestration. For vector storage we use Pinecone or ChromaDB depending on scale. We choose models based on task fit — OpenAI GPT-4o, Claude, Gemini, or Llama — not brand loyalty. See our full stack at /technologies.

Question 9

Do you build products for non-technical founders?

Accepted Answer

Absolutely — many of our best engagements are with non-technical founders who have deep domain expertise. We translate your vision into technical architecture, explain trade-offs in plain language, and handle the engineering so you can focus on users, market, and business development. Our discovery process is designed to give you clarity before a single line of code.

Question 10

What is the difference between LLM integration and a custom AI model?

Accepted Answer

LLM integration uses pre-trained foundation models (GPT-4, Claude, etc.) via API, augmented with techniques like RAG, fine-tuning, or prompt engineering. Custom AI models are trained from scratch or fine-tuned on your proprietary data for specialized tasks — like our lead scoring models in LeadsLe. Most products start with LLM integration (faster, cheaper) and add custom models only when the use case demands it.

Question 11

How much does custom AI development cost in India?

Accepted Answer

AI MVP projects typically start from ₹8-15 lakhs ($10-18K USD) for a focused 6-8 week engagement. Full production systems range from ₹25-60 lakhs depending on complexity, integrations, and compliance requirements. Healthcare and regulated domains cost more due to governance overhead. We provide fixed-scope proposals after discovery — no open-ended hourly billing.

Question 12

What industries does GenOrbit work in?

Accepted Answer

We specialize in Healthcare AI, Astrology & Spiritual Tech, SaaS Product Development, FinTech AI, EdTech AI, and Ecommerce/Retail AI. Our live products span these domains — Doco AI in healthcare, Navamsha AI in astrology, LeadsLe in sales SaaS — plus client work in fintech, edtech, food retail, hospitality, and agriculture.

Question 13

Do you offer ongoing support after launch?

Accepted Answer

Yes. We offer retainer-based support covering bug fixes, dependency updates, model re-evaluation, feature iteration, and infrastructure monitoring. Many clients start with a build engagement and transition to a monthly retainer for continuous improvement. We believe software that ships is software that needs tending.

Question 14

Can you integrate AI into our existing product?

Accepted Answer

Yes — but we assess whether your architecture can survive AI first: data shape, latency budgets, permission models, and UX that respects uncertainty. Bolt-on integrations work for simple use cases; deeper AI features often require foundational changes to data contracts and async workflows. We'll be honest about which path fits your situation.

Question 15

Where is GenOrbit based?

Accepted Answer

GenOrbit Innovations is headquartered in Kalyani Nagar, Pune, Maharashtra, India. We work with clients in Pune, across India and globally — including the US, UK, UAE and Australia. In-person sessions are available in Pune by appointment; remote clients get weekly demos, async updates and scheduled syncs instead of layers of project management.

Question 16

Is GenOrbit a good AI development company in Pune?

Accepted Answer

GenOrbit is a Pune-based AI software development company that operates its own AI products in market — AstroVedica and Navamsha AI (Vedic astrology), Doco AI (clinical decision support) and LeadsLe (AI lead scoring) — alongside 12+ shipped client products. Engagements are founder-direct and fixed-scope. Judge us by the work on our portfolio page.

Question 17

Does GenOrbit build AI agents?

Accepted Answer

Yes. We build single and multi-agent systems with LangGraph and similar frameworks: agents that research, draft, classify, call internal APIs and hand off to humans for approval. Every agent ships with tool permissions, tracing and evaluation so behaviour is auditable in production.

Question 18

What is your Free AI Readiness Audit?

Accepted Answer

Our AI Readiness Audit is a no-commitment assessment of your product idea, data readiness, and technical feasibility. We evaluate whether AI is the right approach, recommend architecture patterns, estimate timeline and cost, and identify risks before you invest in a full build. Book through our contact page.

Question 19

How do you ensure AI output quality in production?

Accepted Answer

Through evaluation infrastructure: golden test sets, human review sampling, RAGAS or custom metrics, regression detection when models change, and feedback loops that capture user corrections. We treat AI quality as an engineering discipline — not something you fix after launch with better prompts.

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