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Startups & Enterprises

AI development company in India for production AI applications

Xenotix Labs is an AI development company in India that ships LLM-powered applications, RAG systems, AI chatbots, and ML pipelines into production. We are not an AI consultancy that writes strategy decks. We are an engineering team that has shipped AI features into 6+ live products — Growara (AI WhatsApp automation for SMB customer engagement), Alcedo (AI-powered education discovery and adaptive learning), 7S Samiti (offline-first adaptive AI tutor for rural India), Corporate Gate (LLM-driven resume optimization and job matching), the AI layer in ClaimsMitra (claim summary generation), and the recommendation engine in Cricket Winner.

We work in three patterns. Pattern one: integrate an off-the-shelf model (OpenAI, Anthropic, Gemini) behind a thin guardrail layer for chat, summarization, classification. Pattern two: build a RAG system on top of your private documents — typically Postgres + pgvector for sub-100k-document corpora, OpenSearch or pinecone-class vector DB above that. Pattern three: train or fine-tune small specialized models for narrow domains where prompt engineering hits a ceiling.

For most founders the right answer is pattern one or two. Training your own LLM is almost never the right answer for a startup — the data, the GPUs, the eval harness, and the ongoing red-team cost are too high. We will tell you that on the first call. Our incentive is your product working, not our hours billed.

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Written by Xenotix Labs Engineering

Founding Engineering Team

A small, opinionated engineering team that has shipped 33+ production startup products across D2C, fintech, edtech, sports, healthcare, and legal-tech.

Our Expertise

AI Development Solutions We Offer

Comprehensive solutions tailored to your business needs

Machine Learning Models

Custom ML models for prediction, classification, and pattern recognition tailored to your business needs.

Chatbot Development

Intelligent conversational AI chatbots for customer support, lead generation, and engagement.

Natural Language Processing

NLP solutions for text analysis, sentiment detection, language translation, and content understanding.

Computer Vision

Image and video analysis solutions for object detection, facial recognition, and visual inspection.

Recommendation Systems

Personalized recommendation engines to boost user engagement and conversion rates.

AI Integration

Seamlessly integrate AI capabilities into your existing applications and workflows.

Sub-services

What we ship inside ai development

Real engagements, real case studies — not a feature list. Each sub-service is one we have shipped to production.

AI Chatbot Development

Production-grade chat interfaces over LLM APIs with conversation memory, tool use, guardrails, and PII redaction.

Use cases: Customer support, sales-qualification, internal-knowledge bots

Shipped on: Growara WhatsApp AI agent

RAG System Development

Retrieval-Augmented Generation over your private documents. Postgres + pgvector for small corpora, vector-DB-backed for larger.

Use cases: Internal docs Q&A, legal research, product docs assistant, sales enablement

Shipped on: AI tutor RAG in Alcedo, content generation in 7S Samiti

AI Agent Development

Tool-using AI agents that can search, fetch, write, and call your APIs autonomously, with deterministic guardrails on side-effects.

Use cases: Workflow automation, customer-ops automation, research assistants

Shipped on: Growara escalation agent, Corporate Gate resume optimizer

LLM Integration Services

Drop AI into your existing app — content generation, classification, summarization, search relevance — without rebuilding the product.

Use cases: SaaS product enhancement, content tools, search

Shipped on: Cricket Winner news summary, ClaimsMitra summary generation

Custom ML Model Development

Train or fine-tune specialist models when LLMs are not the right shape — recommendation, computer vision, fraud detection.

Use cases: Personalization engines, vision/OCR pipelines, time-series forecasting

Shipped on: Cricket Winner recommendations, fraud signals in BullBot

AI Product Strategy & Discovery

2-week paid engagement to map your AI use case to the right pattern (off-the-shelf vs RAG vs custom), with a build estimate at the end.

Use cases: Founders evaluating "should we add AI?", post-AI-hype clarity

Shipped on: Default first phase for AI engagements

AI Evaluation & Guardrails

Eval harness, red-team prompts, drift monitoring, output filtering, prompt-injection defense.

Use cases: Production AI in regulated industries, customer-facing AI, AI handling user data

Shipped on: Production guardrails in Growara, Corporate Gate

Tech stack reasoning

Why we default to OpenAI / Anthropic + pgvector + Python orchestration

We have shipped enough AI features into production to have strong opinions. For 80% of startup AI use cases, the right stack is: OpenAI or Anthropic API for the LLM, pgvector inside your existing PostgreSQL for retrieval, Python (FastAPI) or Node.js for the orchestration layer, and your existing app stack untouched. This pattern lets you swap LLM providers without re-architecture and keeps vector data adjacent to your transactional data.

We reach for a dedicated vector DB (Pinecone, Weaviate, Qdrant) when corpus size crosses ~500k documents or when retrieval latency under load is mission-critical. We reach for fine-tuning when prompt engineering plateaus and you have a narrow, well-labeled task. We reach for self-hosted open models (Llama, Mistral) when data residency or cost-per-token economics demand it.

On evals: we treat AI features the way we treat any backend feature — they need an eval harness. Before any production AI feature ships, we build a test set of 50-200 representative inputs with expected outputs, run it on every prompt change in CI, and track output quality drift over time. Without this, "the AI got worse" is a phone call you cannot debug.

Pricing & timeline

How much does ai development services cost?

Published tiers, not opaque quotes. Every range below is one we have shipped engagements at.

AI Feature Drop-In

₹3,00,000 – ₹6,00,000

$4,000 – $7,500

Timeline2-4 weeks
Team1 AI engineer + 0.5 backend
ScopeAdd AI to one feature in your existing product (chat, summarize, classify, search)
Best forExisting SaaS adding AI, content tools, internal AI assistants

RAG / Chatbot MVP

₹8,00,000 – ₹15,00,000

$10,000 – $18,000

Timeline6-10 weeks
Team1 AI, 1 backend, 1 frontend, 0.5 designer
ScopeStandalone AI assistant or chatbot over your private data, with admin panel, basic eval
Best forCustomer support automation, internal knowledge bots, SMB AI products

AI Platform / Multi-Tenant

₹20,00,000 – ₹50,00,000

$25,000 – $60,000

Timeline4-7 months
TeamFull pod with dedicated AI engineer
ScopeAI-first SaaS — multi-tenant, billing, admin, eval harness, drift monitoring, full guardrails
Best forVertical AI SaaS, AI-first startups, regulated AI applications

Custom Model / Fine-Tune

₹15,00,000 – ₹50,00,000+

$18,000 – $60,000+

Timeline3-9 months
TeamAI specialist + MLOps engineer
ScopeTrain or fine-tune specialist models with full data pipeline, training infra, eval, deployment
Best forDomain-specific tasks where prompt engineering plateaus, regulated industries, cost-sensitive scale

Pricing varies with scope, integrations, and compliance needs. Every engagement starts with a fixed-scope written estimate after a 2-week paid discovery — never an open hourly meter.

Real case studies

Production case studies — not deck pages

Each ai development engagement below is live, in production, and serving real users today.

Growara — AI WhatsApp automation

Problem

Build an AI agent that handles SMB customer queries on WhatsApp 24/7, escalates to human only when needed.

Stack

Next.js dashboard, Node.js, PostgreSQL, Meta WhatsApp Business API, LLM integration, RabbitMQ

Outcome

Live AI agent handling production query volume; human escalation rate within target SLA.

Read case study

Alcedo — AI-powered education discovery

Problem

Combine geo-discovery of nearby institutions with AI generation of quizzes, study notes, assignments per student level.

Stack

Flutter, Next.js, Node.js, PostgreSQL, LLM integration for adaptive content

Outcome

Live platform serving generated content to students; adaptive difficulty per learner.

Read case study

7S Samiti — offline-first adaptive AI tutor

Problem

Bring quality AI tutoring to rural Indian students with low connectivity, in their local language.

Stack

Flutter offline-first, Next.js, Node.js + PostgreSQL, LLM-backed adaptive tutor

Outcome

Mission-driven deployment with offline content sync and language adaptation.

Read case study

50+

Projects Delivered

10M+

Users Reached

4.9★

Client Rating

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Why founders pick Xenotix

Why Xenotix Labs for ai development

AI in production, not in slides

6+ live products with AI features serving real users today.

Honest pattern selection

We will tell you when off-the-shelf is enough. We will not push fine-tuning when a system prompt does the job.

Eval harness from day one

Every AI feature ships with a test set. Quality drift is measurable, not vibes.

Guardrails for India

PII redaction, prompt-injection defense, language detection — built in, not bolted on.

Vendor-portable architecture

Swap OpenAI for Anthropic for self-hosted Llama without re-architecture.

How We Work

Our Ai Development Process

Our proven methodology ensures successful project delivery

1

Discovery

Understanding your business goals and AI opportunities

2

Data Analysis

Analyzing available data and defining model requirements

3

Development

Building and training AI models with your data

4

Testing

Rigorous testing and validation of AI performance

5

Integration

Seamless deployment into your systems

6

Optimization

Continuous monitoring and improvement

Have a project in mind?

Let's discuss how we can help you achieve your goals.

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Common Questions

Frequently Asked Questions

Everything you need to know about ai development services

Should I use OpenAI, Anthropic, Gemini, or build my own model?

For 80% of startup use cases, off-the-shelf APIs (OpenAI / Anthropic / Gemini) win on cost and time-to-market. We build custom models only when prompt engineering plateaus on a narrow, well-labeled task or when data residency requires self-hosting. We will tell you which on the first call.

How much does AI development cost in India?

AI feature drop-ins start at ₹3-6 lakh. RAG / chatbot MVPs run ₹8-15 lakh. Multi-tenant AI platforms are ₹20-50 lakh. Custom model training varies widely with data and use case — ₹15-50 lakh+.

What is RAG and do I need it?

Retrieval-Augmented Generation lets an LLM answer questions over your private documents without retraining. You need RAG when you want answers grounded in your specific data (product docs, legal contracts, customer support archive) rather than the LLM's general knowledge.

Can you build a WhatsApp AI chatbot?

Yes. Growara is a live AI agent on WhatsApp Business API handling production customer queries today. We can replicate that pattern for your business — typical timeline 6-10 weeks.

How do you handle prompt injection and PII?

PII redaction at input, output filtering with category-specific blocklists, prompt-injection detection via secondary LLM call when stakes warrant it, full audit log of every AI input/output for the first 90 days post-launch.

Do you train your own foundation models?

No, and we will tell you not to either. Training a foundation model is a $10M+ exercise; it is almost never the right answer for a startup. We fine-tune small models when warranted, and we use off-the-shelf APIs everywhere else.

Can you integrate AI into my existing app?

Yes — that is our most common AI engagement. Drop-in AI features (chat, summarize, classify, search) into existing Next.js or React Native or Flutter products typically ship in 2-4 weeks.

How do you measure AI quality?

Eval harness: 50-200 representative inputs with expected outputs, run on every prompt change in CI, tracked over time. Output drift is measurable. We share dashboards with founders.

Still have questions?

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