Klipy VS Diffyn

Klipy's answer

Jung (co-founder, prior ML startup exits) was in a board meeting when a hot prospect replied — and lost the deal to a competitor who responded in 10 minutes.

He built the first version to draft his own follow-ups automatically. After sharing it with early users, 4,450 agent conversations in 8 weeks revealed the same pattern: "when A happens, do B, I'll approve, then execute C." 75% of requests were for execution nobody else could deliver.

That gap between conversation and revenue-producing action became the product. Backed by UBS, AppWorks, Google Cloud, and Microsoft for Startups.

Diffyn's answer:

I started working on Diffyn when I notice that prompting has become an essential part of work across many industries. While there are version control platofrms like github, they are not designed for just prompt management are can be overkill such applications, it is also not integrated natively with various LLMs and relevant tools for users to validate ideas and visualise results properly.

Klipy's answer

• Sunil Gulati, Sr Director Sales, TekPioneers
• Phil Bouwman, Head of Strategy, Million Circles
• Alexander King, Co-Founder, Regulars
• Chip Royce, Founder, Flywheel Advisors

Klipy's answer

B2B sales teams and GTM-background founders who lose deals to slower competitors.

• Primary: SaaS founders who run their own sales — 5–50 deals in flight, no time for manual CRM
• Secondary: Lean sales teams of 5–20 reps where the bottom 50% need to follow up at machine speed
• Also: RevOps leaders and VPs of Sales who need AI execution that passes SaaS procurement

Diffyn's answer:

Professionals incorporating LLMs or AI tools in their workflow and wants to keep track of changes and test their prompts.

Klipy's answer

• AI: Google Gemini + Claude via Vercel AI SDK, RAG / vector search on conversation data
• Backend: Convex real-time serverless database, Next.js 15
• Integrations: Nylas v7 (email/calendar), Recall.ai (meeting transcription), Unipile (LinkedIn/WhatsApp/Telegram)
• Auth & Payments: Clerk, Stripe, Orb (usage metering)
• Infrastructure: Vercel, Google Cloud, Microsoft Azure

Diffyn's answer:

React, Next.js, POSTGRESQL

Klipy's answer

Your agent occupies the only position in the market that works: fast AND supervised.

Every other tool either records and suggests (slow) or acts without you (fast, but burns domains and gets 0 replies). Your agent watches every conversation, drafts the follow-up with full deal context, and waits for your approval. Speed without giving up control.

• 6 minutes vs. the 47-hour industry average
• Supervised: every draft requires your approval — nothing sends without you
• Conversation-native: RAG on real conversations, not what reps remember to type into a CRM
• Multi-channel: email, calls, LinkedIn, WhatsApp, and Telegram in one agent
• 2-minute setup: first draft appears in 60 seconds. No workflow builder.

Diffyn's answer:

Addresses workflow and change management on LLM prompts, provide teams with traceability and visualization of tests across multiple models, provide deeper understading into efficiency of these prompts.

Klipy's answer

78% of deals go to whoever responds first. Not whoever's best. Your agent makes that speed structural — not dependent on how fast you type.

vs. Gong: Gong is the best call recording platform. But recording a conversation doesn't follow up on it. Your agent does.

vs. HubSpot / Salesforce: The most popular CRMs for sales teams. But your CRM only knows what reps remember to type. Your agent knows what was actually said.

vs. Fireflies / Otter.ai: Great for transcription. Transcripts don't close deals. Your agent drafts the follow-up from the conversation — in 6 minutes.

vs. autonomous AI SDRs (11x, Artisan): 1,400 emails, 0 replies. Burns domains. $550/positive reply. Your agent drafts from real conversations and you approve before anything sends. That's supervised, not autonomous.

Speed of a machine. Control of doing it yourself. That's why it works and they don't.

Diffyn's answer:

Diffyn is the platform that specializes on both change management and multi-model analysis.