Delfyn, AI Agent to help businesses get paid faster

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Hi HN, I'm Junior, founder of Delfyn, and I’ve been building an AI-powered agent that helps finance teams get paid on time. If you've ever dealt with slow-paying B2B customers, broken invoice matching, or cash flow uncertainty, this might resonate. The problem: In B2B, waiting 60–90 days to get paid is common. AR teams spend hours manually: - Reconciling payments to invoices - Chasing customers - Updating spreadsheets Explaining why cash flow projections keep being wrong Meanwhile, late payments quietly bankrupt 1 in 4 SMEs in Europe. What Delfyn does: We built an AI agent that helps automate: Invoice-to-payment matching using rules, embeddings (e.g. “0099” → “INV-2023-0099”), and retrieval models Cash flow forecasting with transaction-level granularity Proactive reminders + incentive recommendations to reduce DSO We're combining deterministic logic (amount, IBAN, date) with semantic models (e.g. e5-base-v2) and LLMs (for gray-zone explanations only). Who it's for: Finance teams (esp. in B2B SaaS, agencies, professional services) Founders managing AR themselves Companies tired of relying on NetSuite exports + wishful thinking. Or even worst sending payments collections emails to customers who have already paid. I've seen that a lot.

Visit product View on Hacker NewsJun 25, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, models · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, active · Missing: arr, mrr, revenue

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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