AI Software Development Services
Build LLM applications, RAG systems, AI agents, computer vision, and AI-powered SaaS. Production-ready, DPDP-compliant. Based in India.
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What We Build
AI Agent Development
Autonomous AI systems that research, analyze, and take actions across tools and APIs without human intervention at each step.
AI Automation Services
Automate repetitive workflows with AI — document processing, data entry, customer support triage, and intelligent routing.
Generative AI Development
Build applications powered by GPT-4, Claude, Gemini, and open-source LLMs — from chatbots to content generation systems.
AI Consulting Services
Strategic guidance on AI adoption — feasibility studies, architecture reviews, and build-vs-buy recommendations.
AI Chatbot Development
Intelligent conversational interfaces for customer support, sales qualification, and internal knowledge management.
RAG Development Services
Retrieval-Augmented Generation systems that ground LLM answers in your documents, contracts, and business data.
AI Integration Services
Connect AI capabilities to your existing systems — CRMs, ERPs, databases, and third-party APIs.
Core Capabilities
Technical Capabilities
LLM Application Development
Custom applications powered by GPT-4o, Claude 3.5 Sonnet/Opus, Gemini 1.5 Pro, Llama 3.1, and Mistral. We handle prompt engineering, context management, and production deployment—not just API calls wrapped in a UI.
RAG Systems & Knowledge Bases
Retrieval-Augmented Generation pipelines that connect LLMs to your documents, support tickets, contracts, and internal wikis. Built with LangChain, LlamaIndex, Pinecone, Weaviate, or pgvector. Accurate answers grounded in your data.
Computer Vision Solutions
Document OCR, object detection, visual inspection, image classification, and facial recognition. We use AWS Rekognition, Google Vision AI, Azure Computer Vision, or custom PyTorch models deployed on your infrastructure.
Predictive ML Models
Fraud detection, churn prediction, demand forecasting, credit scoring, and price optimization. Purpose-built models for structured business data using XGBoost, LightGBM, scikit-learn, and PyTorch.
AI-Powered SaaS Products
End-to-end development of B2B and B2C SaaS platforms with AI at the core. We build the AI pipeline, backend API, Next.js frontend, mobile app, and cloud infrastructure together—not in silos.
Natural Language Processing
Sentiment analysis, entity extraction, text classification, summarization, and translation. For Indian businesses, we handle multilingual NLP in Hindi, Tamil, Bengali, and other regional languages.
Process
How We Build AI Products
Data Audit & Discovery
Before writing code, we audit your data—what exists, how clean it is, whether it's sufficient to train or fine-tune models. Most failed AI projects trace back to this question being skipped. We also define measurable success metrics upfront, not vague goals like 'the AI should feel smart.'
Architecture & Proof of Concept
We design the AI pipeline and build a working proof of concept against your actual data—before committing to 3 months of development. This is when we discover whether the approach is viable, what the cost-per-query will be, and whether accuracy meets business requirements.
Production Development
Full development with automated evaluation tests on every deployment. We track cost-per-query, latency, and output quality from day one—not as an afterthought. Code is versioned, prompts are versioned, and experiments are reproducible.
Deployment & Monitoring
Deployment on your cloud (AWS, GCP, Azure) with full observability—Grafana or CloudWatch dashboards for model performance, API costs, and latency. We stay engaged post-launch to tune the system as real usage data comes in, not disappear after handoff.
Frequently Asked Questions
What's the difference between AI Software Development and AI Consulting?
AI Consulting is strategic advisory—feasibility studies, architecture reviews, vendor selection, and roadmap planning. AI Software Development is building and deploying the actual system. Most clients start with a 2-4 week consulting engagement to assess feasibility, then move to development if the business case makes sense. See AI Consulting for details.
Do we need a large dataset to build AI software?
Not always. Foundation models like GPT-4o and Claude come pre-trained on vast datasets. For many use cases, prompt engineering and RAG with your existing documents is sufficient. For custom ML models (fraud detection, demand forecasting), we assess during discovery whether you have enough labeled data—typically 10,000+ examples for supervised learning. If not, we discuss data collection strategies, synthetic data augmentation, or alternative approaches.
How do you prevent AI from giving wrong answers?
No AI system is 100% accurate. What we do: (1) Build automated evaluation pipelines that measure accuracy against a test set on every deployment. (2) Use RAG to anchor answers in your documents, not the model's pre-training. (3) Add structured output validation to catch format errors. (4) Design UI patterns that set appropriate user expectations ('AI-generated, verify before acting'). (5) Build human escalation paths for high-stakes decisions. The goal is to make errors rare, detectable, and recoverable.
Which AI models and frameworks do you use?
We're model-agnostic and choose based on your cost, latency, accuracy, and data privacy requirements. Common choices: OpenAI GPT-4o / GPT-4o-mini, Anthropic Claude 3.5 Sonnet/Opus, Google Gemini 1.5 Pro, open-source Llama 3.1, Mistral Large, and Phi-3. For data that cannot leave your infrastructure, we deploy open-source models on your AWS, GCP, or Azure account. Frameworks: LangChain, LlamaIndex, Haystack, Hugging Face Transformers, PyTorch, TensorFlow.
How long does an AI software project take?
A focused MVP—one core use case, real data, production-ready deployment—typically takes 10-16 weeks. Complex systems with custom model training, multiple integrations, and compliance requirements take 4-6 months. We provide a detailed timeline after the discovery and proof-of-concept phase, not upfront based on a brief.
How much does AI software development cost in India?
Indicative ranges: AI chatbot / knowledge assistant (₹5-15 lakhs), RAG system with custom data (₹8-20 lakhs), autonomous AI agent (₹12-30 lakhs), custom ML model for prediction/classification (₹8-25 lakhs), AI-powered SaaS MVP (₹15-35 lakhs), full-stack AI platform (₹30-80+ lakhs). Actual cost depends on data complexity, integrations, and scope. See Custom AI Software Development India: Pricing Guide 2025 for breakdown.
Do you handle DPDP Act 2023 compliance for AI systems?
Yes. Every AI project includes DPDP compliance by default. We implement: (1) Consent management—users opt-in to AI processing with clear language about what data is used. (2) PII redaction—strip personally identifiable information before sending to third-party LLM APIs. (3) Data localization—Indian user data stored in India-region servers (AWS ap-south-1, GCP asia-south1, Azure Central India). (4) Audit logs—track what data was processed, when, and by whom. (5) Right to erasure—cascading delete across all systems when a user requests deletion. For regulated industries, we also handle RBI guidelines (fintech), ABDM compliance (healthcare), and SEBI requirements (capital markets).
Can you integrate AI into our existing software?
Yes—that's our AI Integration service. Common scenarios: adding AI search to an existing web app, building a chatbot that pulls from your CRM, automating document processing in your ERP, or adding fraud detection to payment flows. We work with your current tech stack (Java, Python, .NET, PHP) and connect via REST APIs, message queues, or direct database access. See AI Integration Services.
What industries do you build AI software for?
We've built AI systems for fintech (fraud detection, loan scoring, RBI-compliant lending platforms), healthcare (ABDM-compliant diagnostics, patient management, telemedicine), e-commerce (recommendation engines, catalog automation, personalized search), logistics (route optimization, demand forecasting, fleet tracking), B2B SaaS (AI-powered analytics, intelligent automation), and edtech (AI tutors, adaptive learning). AI applies across virtually every sector—our discovery process identifies where the ROI is highest.
Do you provide ongoing support after launch?
Yes. AI systems require continuous tuning as usage patterns evolve. We offer monthly retainers (₹2-8 lakhs/month) that include: monitoring dashboards for cost, latency, and accuracy; prompt tuning as new edge cases emerge; model retraining when data drift is detected; infrastructure management and scaling; and priority bug fixes. Most clients stay with us for 12-24 months post-launch, then either take over maintenance internally or continue on a reduced retainer.
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