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Obsei VS Chattermill

Compare Obsei VS Chattermill and see what are their differences

Obsei

Open source, privacy-first Voice of Customer analytics. A self-hosted alternative to Enterpret and Unwrap that strips PII from feedback before any LLM sees it.

Rating
0 reviews
Pricing
Open source
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Chattermill

Extract actionable insights from customer feedback using deep learning

Rating
0 reviews
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Which is more popular?

Chattermill might be a bit more popular than Obsei. We know about 1 link to it since March 2021 and only 1 link to Obsei.

social mentions
1 vs 1
AI popularity
37% vs 63%
alternatives listed
15 vs 81

Base details

Website, pricing, platforms and company facts side by side.

Obsei
Chattermill
Website obsei.com chattermill.com
Pricing
Open source
Official pricing
Company 2020 —
Listed in
Voice Of Customer Customer Feedback Customer Insights Feedback Management Customer Experience Product Management AI Sentiment Analysis +5
Customer Feedback User Experience Surveys Forms And Surveys +1

About Obsei and Chattermill

In their own words, as submitted to SaaSHub.

Obsei
Chattermill

Obsei is an open source, self-hosted Voice of Customer platform. It reads customer feedback from wherever it lives, removes personal data before any AI model sees it, and shows your team what customers are saying. What it does Collects feedback from the App Store, Google Play, Zendesk, Freshdesk,...

Read more about Obsei

No description of Chattermill yet.

Features and specs

What each product offers, as listed by its team.

Obsei 11 features
Chattermill 5 features
  • PII redaction at ingest
    Emails, phone numbers, card numbers, IBANs and national IDs for 16 countries are removed before any model, sink or agent sees the text. Checksums are validated where the format has one, such as Aadhaar and cards.
  • Self-hosted and air-gapped by default
    Runs on a laptop, a server or Docker inside your network. The database is encrypted, there's no telemetry, and model and sink traffic to outside hosts is blocked until you allow it.
  • Local AI classification
    Labels sentiment, intent and your own custom questions with a model that runs in process, with no API key needed. Ollama, vLLM, OpenAI, Azure, Bedrock and Vertex also work. Low-confidence answers go to a person for review.
  • Multilingual themes
    Groups the same issue across 50+ languages into stable themes, with trend tracking, near-duplicate detection and k-anonymity, which hides groups that are too small.
  • 20+ feedback sources
    App Store, Google Play, Zendesk, Freshdesk, Intercom, Gong, GitHub issues, Hacker News, Bluesky, YouTube, RSS, email, SQL databases, CSV files, webhooks and any REST API or MCP tool.
  • Routing and alerts
    Sends feedback to Slack, Microsoft Teams, Jira, Linear, GitHub issues or signed webhooks, using rules on labels and confidence. Alerts on theme spikes, new themes and pipeline health, plus regular digests.
  • MCP server for AI agents
    Claude Code, Codex, Cursor, GitHub Copilot CLI and Gemini CLI can search feedback and get cited, redacted quotes. Any issue an agent proposes waits for human approval.
  • Privacy controls built in
    Salted author pseudonyms, erasure on request (obsei forget), data export and an audit log. These support GDPR, India's DPDP Act, LGPD and CCPA work.
  • Guided setup, no YAML editing
    obsei init creates a working project with sample data, obsei try <link> previews any app or repo, and obsei add slack sets up sources and sinks interactively.
  • Open Source
    No paid tier, no open-core split and no hosted upsell. Signed container images, an OpenSSF Scorecard and a plugin system for your own connectors.
  • License
    Apache-2.0
  • Comprehensive Analytics
    Chattermill provides advanced analytics to gain deep insights into customer feedback and sentiment, enabling businesses to make data-driven decisions.
  • Machine Learning Integration
    The platform uses machine learning algorithms to automatically categorize and analyze qualitative feedback, saving time and improving accuracy.
  • Multi-channel Feedback
    Chattermill supports integration with multiple feedback channels, including surveys, social media, and customer reviews, offering a unified view of customer sentiment.
  • Customizable Dashboards
    Users can create personalized dashboards to visualize insights that matter most to their business, providing flexibility and tailored reporting.
  • Real-time Insights
    Chattermill offers real-time processing of customer feedback, allowing businesses to react quickly to changes in customer sentiment and preferences.

Possible disadvantages

  • Complex Setup
    Some users may find the initial setup and integration process to be complex, requiring technical knowledge or support from Chattermill's team.
  • Pricing
    Chattermill may be relatively expensive compared to other feedback analysis tools, which could be a barrier for small businesses or startups.
  • Learning Curve
    New users might face a learning curve in getting acquainted with all the features and functionalities of the platform, which could slow down implementation.
  • Limited Customization for Smaller Businesses
    While the platform offers customization, some features may be more suited for larger enterprises, potentially limiting its utility for smaller businesses.
  • Dependence on Data Input
    The effectiveness of Chattermill's insights heavily depends on the quality and volume of input data, making accurate and comprehensive data collection crucial.

Videos

Walkthroughs and reviews on video.

Obsei 0 videos + Add
Chattermill 3 videos + Add

No Obsei videos yet. You could help us improve this page by suggesting one.

Dmitry Isupov, Co Founder, Chattermill

More videos

  • - Chattermill raises £600K to use ‘deep learning’ to help companies make sense of customer feedback
  • - David Ascott, Head of Sales - Chattermill Segment at January Conference

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Obsei
Chattermill
37% 37%
AI
63% 63%
13% 13%
Customer Feedback
87% 87%
100% 100%
Sentiment Analysis
0% 0%
0% 0%
NLP And Text Analytics
100% 100%

Questions & Answers

As answered by people managing Obsei and Chattermill.

What makes your product unique?

Obsei's answer

Obsei removes personal data before any AI model sees your customers' words. Emails, phone numbers, card numbers and national IDs are stripped as feedback arrives, authors become pseudonyms, and the database is encrypted. The default classifier runs on your own machine, so you can analyse reviews, tickets and calls without sending them to a SaaS vendor or a public LLM. It is also open source under Apache-2.0, with no paid tier, and it works with AI agents through MCP, so Claude, Cursor or Codex can answer "what are customers saying?" with real, redacted quotes.

Why should a person choose your product over its competitors?

Obsei's answer

Choose Obsei if you can't, or don't want to, send customer text to another company. Enterpret, Unwrap and Chattermill process your feedback on their servers and usually start at several thousand dollars a year. Obsei runs inside your own infrastructure, costs nothing, and lets you bring your own model, from a small local classifier to Ollama, Azure OpenAI or Bedrock. You also aren't locked in: the data stays in an encrypted DuckDB file you own, and every source, sink and model can be swapped.

How would you describe the primary audience of your product?

Obsei's answer

Product, support and CX teams who want to understand customer feedback at scale but work under privacy rules: banks, insurers, healthcare, public services, and companies working under GDPR or India's DPDP Act. It also suits app developers and open source maintainers who want to track App Store, Google Play, GitHub or Hacker News feedback for free, and engineering teams building AI agents that need customer evidence without exposing personal data.

What's the story behind your product?

Obsei's answer

I started Obsei in 2020 as a small open source Python library to read app reviews and tweets with AI models. In 2022 it was a joint runner-up in the Reserve Bank of India's first global hackathon, HARBINGER, and my co-founder Girish Patel and I started a company, Oraika, around it. We had encouraging meetings with banks, but we were engineers with no experience of enterprise sales, and we couldn't close a deal. The project went quiet for almost three years.

In 2026 I came back to it. Customer feedback had become even more sensitive with LLMs everywhere, so I rewrote Obsei from scratch around one rule: understand customers without moving their personal data anywhere it shouldn't go. Version 1.0 shipped in October 2026. I maintain it in my free time, and the long-term plan is to move it to a neutral foundation such as LF AI & Data.

Which are the primary technologies used for building your product?

Obsei's answer

Python 3.11+ with Pydantic for configuration, Typer for the CLI, httpx for connectors and Starlette for the server. DuckDB with its encryption extension for storage. Hugging Face Transformers for the default zero-shot classifier (bge-m3-zeroshot-v2.0), fastembed for multilingual embeddings and GLiNER for optional name redaction. The official MCP Python SDK for the agent server. The Studio UI is plain TypeScript bundled with esbuild, and the docs and website are built with Astro.

User comments

Share your experience with using Obsei and Chattermill. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Obsei 1 mention
Chattermill 1 mention
  • Introducing Obsei
    Allow me to introduce Obsei (pronounced "Ob see" | /əb-'sē/). It is a low-code AI powered automation tool. Name was derived from three words OBServe, AnalyzE, Inform. - Source: dev.to / almost 5 years ago
  • Analyzing text data
    Https://chattermill.com - if you have the resources for it. Source: about 4 years ago

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When comparing Obsei and Chattermill, you can also consider the following products.

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