AI Product Development Services

AI consulting focuses on strategy, assessment, and roadmap creation — advising organizations on where and how to deploy AI. AI product development is the hands-on engineering engagement that actually builds, trains, deploys, and operates the AI system. The best engagements combine both: strategic consulting anchored by technical delivery accountability.

Timelines vary by product complexity. A focused AI MVP (e.g., a document classification tool or chatbot) can be delivered in 6–10 weeks. A full-featured, production-grade AI SaaS platform with multi-model architecture, MLOps infrastructure, and enterprise security typically requires 16–24 weeks from discovery to launch.

Data requirements depend on your AI approach. For supervised ML models, you typically need 5,000–100,000 labelled examples. For LLM-based products using RAG, you need structured knowledge sources (documents, databases, APIs) rather than large labelled datasets. For fine-tuning foundation models, 500–5,000 high-quality examples can be sufficient. We conduct a data readiness assessment before every engagement.

AI product development costs in India vary from ₹25 lakhs for a focused AI MVP to ₹2–10 crores for enterprise-grade AI platforms. The primary cost drivers are model complexity, data engineering scope, infrastructure architecture, and the number of product features. India-based AI development offers 40–60% cost savings versus equivalent teams in the US or UK without compromising engineering quality.

Yes — AI feature integration into existing products is one of our most common engagement types. We have pre-built integration patterns for major tech stacks (React, Node.js, Python, Java, .NET) and can add AI capabilities via microservice APIs, SDK integration, or direct model embedding — minimising disruption to your existing product architecture.

Retrieval-Augmented Generation (RAG) is an architecture that combines a large language model with a real-time retrieval system connected to your organization's knowledge base. Instead of relying on a model's static training data, RAG enables the AI to retrieve up-to-date, organization-specific information before generating responses — dramatically reducing hallucinations and ensuring domain relevance. RAG is now the standard architecture for enterprise AI products that need to work with proprietary data.

We implement multiple layers of data protection: end-to-end encryption (AES-256 at rest, TLS 1.3 in transit), role-based access control, data anonymisation and pseudonymisation, privacy-preserving ML techniques (federated learning, differential privacy), and comprehensive audit logging. We ensure compliance with GDPR, India's DPDP Act 2023, HIPAA, and PCI-DSS as applicable to each client's context.

Model drift occurs when an AI model's predictive accuracy degrades over time because the real-world data distribution diverges from the training data distribution. We prevent drift through automated monitoring using tools like Evidently AI and WhyLabs, which detect statistical shifts in input data and model output distributions. When drift thresholds are breached, automated retraining pipelines are triggered to update the model with fresh data.

Yes — we offer post-launch AI product support and evolution packages ranging from basic model monitoring to comprehensive AI product management. Our most popular post-launch package includes monthly model performance reviews, quarterly model retraining, a dedicated ML engineer for on-call support, and two feature development sprints per quarter.

Traditional software follows deterministic, rule-based logic — the same input always produces the same output. AI products are probabilistic, learning-based systems that improve with data exposure and can handle inputs and scenarios that were never explicitly programmed. AI products also require ongoing maintenance (model retraining, drift monitoring) that has no equivalent in conventional software maintenance.

Absolutely. While enterprise AI products receive more coverage, small and medium businesses often achieve some of the highest ROI from AI development. AI products for SMBs are increasingly affordable due to API-based LLM access, cloud-based ML infrastructure, and pre-trained foundation models that reduce custom training costs. A well-targeted AI product can help an SMB compete with companies 10x their size.