Lead to Conversion AI · Revy
Convert high-intent visitors with Revy.
Revy answers product questions, captures intent, and sends qualified leads to your team.
Same visitors, 2X more conversions
Lead to Conversion AI and FlowComplete AI turn interest into revenue and finished applications - vernacular voice, KB-grounded agents, go live in minutes.
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Lead to Conversion
FlowComplete
Products in action
See Revy and Flux in action
Switch between Revy and Flux to see how SpotInfo helps teams convert leads, recover stuck users, and complete applications.
Platform
Built for production AI journeys
Shared runtime capabilities behind Revy and Flux.
Zero hallucinations
Responses grounded in your knowledge base: APIs, docs, Drive, and URLs
Sub-1s voice
Low-latency voice for natural back-and-forth, even on mobile networks across India.
Omnichannel context
One conversation thread across Website, WhatsApp, Phone and App. Users pick up where they left off.
CRM sync
Transcripts, summaries, and lead fields flow into your CRM on each interaction automatically.
Resources
Latest from the SpotInfo Blog
Learn how AI agents are transforming user engagement, boosting conversions, and supercharging sales performance across industries.
Voice AI
The Conversion Problem Isn’t Traffic. It’s Conversation.
Voice outperforms static channels for complex decisions, but human-only scaling breaks on cost, language coverage, and training lag. A hybrid AI + human model turns voice into a scalable layer without losing trust or quality.
Read articleProduct & Technology
The Complete Guide to LLM Evaluations
Part I - The Foundations & Reliability Checks
Understanding why traditional testing fails for AI, the two main categories of evals, how to use public benchmarks (MMLU, HumanEval, GSM8K, Chatbot Arena) for model selection, and deterministic evals (JSON validation, regex matching, code execution) for reliability checks.
Read articleProduct & Technology
The Complete Guide to LLM Evaluations
Part II - The Cognitive Engine
Master the RAG Triad (context relevance, faithfulness, answer relevance) and LLM-as-a-judge patterns (direct scoring, pairwise comparison, chain-of-thought reasoning). Learn how to evaluate context-aware systems and handle judge bias.
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