Sentient – AI-powered customer feedback analysis with 95% accuracy

Hacker News

We built Sentient to automatically extract themes and sentiment from customer feedback without manual configuration. The system processes documents in sub-second time while maintaining 95% accuracy. Built with Next.js 14 and FastAPI, using fine-tuned OpenAI models trained on millions of feedback examples. Key features include automatic customer segmentation, multi-format support (PDF, DOCX, CSV), and real-time processing. The core insight: most tools analyze individual reviews instead of understanding customer journeys and behavioral patterns. Sentient connects feedback sentiment to business outcomes through multi-phase AI analysis. Technical approach uses theme detection, emotion recognition, and intelligent caching for enterprise-grade performance. Try it at: https://data-decoder.vercel.app/

Visit product View on Hacker NewsOct 31, 2025

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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