Build Smarter: A Generative AI Development Company

Three things most generative AI companies can't genuinely claim. First, CMMI Level 5 process maturity — independently audited, not self-assessed. Second, 10+ years of actual AI and ML engineering experience, not general software development relabeled as AI. Third, a delivery record of 700+ projects with measurable outcomes. We also have a strict no-hallucination policy for production systems — we run RAGAS evaluation scoring on every deployment before go-live and don't release until it passes.

Costs vary based on what you're building, how complex your data is, and what integrations are required:

• Proof of Concept: $12,000 – $35,000 | 3–6 weeks
• Focused AI feature (RAG chatbot, document assistant, AI copilot): $35,000 – $90,000 | 6–14 weeks
• Full production system (custom LLM, enterprise knowledge assistant): $80,000 – $220,000 | 10–20 weeks
• Enterprise AI platform or multi-agent system: $220,000+ | 5–10 months

These are honest figures from real projects. Contact us for a tailored estimate.

A focused RAG chatbot connected to an existing knowledge base can go live in 4–6 weeks. A custom LLM fine-tuned on enterprise data with full production deployment typically takes 10–16 weeks. A full multi-agent platform is usually 5–10 months from discovery to launch. We work in 2-week sprints — so you see working software every fortnight, not just at the end.

RAG is the better choice when your AI needs to answer questions from a knowledge base that changes — documents, databases, product specs. It retrieves information in real time, so answers stay current and can be cited. Fine-tuning is the better choice when you want the model to adopt a specific communication style, domain vocabulary, or behavior pattern. Most enterprise use cases need both: fine-tuning for tone and domain knowledge, RAG for factual retrieval. We recommend the right approach during our discovery phase after looking at your actual use case.

Hallucination prevention isn't one thing — it's a combination of engineering decisions. RAG grounds answers in verified sources. System prompt engineering sets strict behavioral rules. Confidence scoring flags uncertain responses. Citation requirements force the model to show its sources. RAGAS evaluation measures faithfulness before deployment. Continuous production monitoring catches accuracy drops early. We apply all of these together. We don't ship systems that we haven't validated against your real data.

Yes — and that's how most of our projects start. Our generative AI integration services connect AI capabilities into existing CRMs, ERPs, SaaS platforms, mobile apps, and internal tools without requiring a full platform rebuild. We've integrated with Salesforce, HubSpot, SAP, ServiceNow, Slack, MS Teams, and dozens of custom-built enterprise systems. The integration approach depends on your stack, but we always prioritize minimal disruption to what's already working.

Yes. We have a track record with startups from seed through Series C alongside enterprise clients. For startups, we offer faster MVP timelines (6–10 weeks to a working AI product), flexible engagement structures, and advice on which AI features actually drive user retention and investor confidence vs. which ones are interesting but not worth the cost yet. Book a free consultation and we'll tell you honestly what makes sense at your stage.

All of the major ones — GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, LLaMA 3, Mistral, and others. We recommend based on your specific accuracy, latency, cost, and data privacy requirements — not on vendor preference. For clients with strict data residency requirements, we deploy open-source models inside your own cloud environment so your data never leaves your infrastructure.

Yes, if it's built correctly — which is exactly why the build approach matters. We've delivered HIPAA-compliant systems for healthcare, GLBA-aligned AI for financial services, and CCPA-compliant data pipelines for consumer businesses. Compliance is an engineering constraint we work into the architecture from day one, not a box we tick at the end. We can deploy everything inside your private cloud with full data residency guarantees.

Book a Free GenAI Consultation at shantiinfosoft.com/contact-us. It's a 60-minute session with a senior AI engineer — not a sales person. We look at your use case, your data, and your constraints, then tell you what makes sense to build, in what order, and what it'll realistically cost. If we're not the right fit, we'll tell you that too.