• Kanpur, Uttar Pradesh
  • veyronventures640@gmail.com
  • Office Hours: 10:00 AM – 6 PM

AI Integration for Web2 & Web3 Products

From customer-facing chatbots to backend automation — we integrate LLMs into your existing stack without a rebuild.

  • Customer support automation — LLM chat layered onto existing CRM/helpdesk
  • Internal Q&A tools — natural-language queries over existing DBs/APIs
  • Document intelligence — extraction, summarization, classification (invoices, contracts, tickets)
  • Workflow triggers — LLM-driven approvals, routing, tagging in existing pipelines
  • RAG search — semantic search over existing docs/knowledge base, no data migration
  • Dev tooling — internal AI assistants wired into existing repos/CI
LLM API Integration

OpenAI, Gemini, Groq, Claude — wired into your existing backend.

AI Chat & Support Agents

Customer-facing chatbots built into your product, not bolted on.

Document & Data Pipelines

RAG, extraction, summarization over your own data.

Prompt Engineering

Prompt design, evaluation, and fine-tuning guidance.

AI-Assisted Internal Tools

Admin copilots and dashboards powered by LLMs.

MCP Server Development

Model Context Protocol servers that connect your APIs, databases, and internal tools to Claude and other AI assistants.

why us

Model-Agnostic, Production-First

Most agencies wrap one API call in a chat UI. We architect for provider-swap flexibility — Groq for low-latency inference, Claude for complex reasoning/code, OpenAI for ecosystem breadth, Gemini for multimodal input — and pick per use case, not by default. Every integration ships into your existing Next.js/Node.js/MongoDB stack, with evaluation loops to catch hallucination before it reaches production.

faq

Common questions about AI integration

Depends on the task — Groq for speed, Claude for reasoning/code, OpenAI for ecosystem support, Gemini for multimodal. We architect for provider-swap flexibility, not lock-in.

Yes — nearly all our AI work is retrofitted into existing Next.js/Node.js/MongoDB stacks, not greenfield builds.

Yes — embedding, vector search, and retrieval setup over your existing docs/DB, no data migration required.

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