Local-first AI workspace that traces answers back to their source documents.

Key facts

Overview

Korvo is a local-first AI workspace designed to keep data on the user's machine whilst providing AI-assisted reasoning with explicit source attribution. Built as an open-source project and launched on Show HN in August 2026, it addresses a specific pain point: AI assistants that hallucinate or obscure where information comes from. The tool works by indexing documents locally, processing queries without sending them to external servers, and returning answers tagged with the source material they came from. This architecture is particularly suited to teams handling confidential research, legal documents, or proprietary data where cloud processing is a barrier or compliance risk. The underlying mechanism relies on embedding-based retrieval and local LLM inference, allowing organisations to choose their own model backend. Unlike hosted alternatives such as Notion AI or Perplexity, Korvo requires manual setup and infrastructure; the trade-off is complete control over data. The project is early and actively maintained on GitHub, with a web interface for document upload and query interaction. Pricing has not been publicly announced, but the core software appears freely available under an open-source licence. Feature completeness lags behind commercial tools—batch processing, advanced filtering, and integration connectors are not yet standard—but the foundational idea of local-first reasoning with source tracing resonates with privacy-first teams. Korvo's competitive position is narrow by design. It does not attempt to replace ChatGPT or Claude for general tasks; instead, it targets users who cannot or should not send documents to cloud LLM providers. Organisations in regulated sectors (finance, healthcare, government) and teams handling intellectual property see the strongest fit. The Show HN launch indicates early developer interest, though user-facing documentation and deployment guides remain sparse. Setup time and ongoing maintenance are non-trivial for non-technical users. The clearest limitation is feature scope. Version 1 covers document indexing and QA; it lacks team collaboration, advanced permissions, multi-user caching, and integration with enterprise systems. Performance on large datasets or complex reasoning tasks has not been independently benchmarked. For users comfortable with setup friction and able to self-host, it offers genuine privacy and auditability; for everyone else, it remains a niche tool.

Who it is for

• Best for: Teams handling sensitive documents or research who need verifiable source attribution and data privacy guarantees..
• Avoid if: You need enterprise support, cloud convenience, or integration with mainstream productivity apps out of the box..

Who this is for

Knowledge workers, researchers, and small engineering teams who handle confidential information and want AI assistance without cloud dependency. Legal teams reviewing contracts, data scientists examining datasets, and security-conscious organisations would find the local-first model appealing.

Who should skip this

Large enterprises needing seamless cloud integrations, users who prefer zero setup overhead, and those without technical expertise to manage local deployment. If you need immediate out-of-the-box functionality and enterprise support, this is not yet mature enough.

Verdict

Korvo solves a real problem—keeping sensitive data local whilst using AI for research and Q&A—but requires technical expertise and accepts feature immaturity as the cost of privacy. It is a strong fit for research teams, legal departments, and regulated organisations; mainstream users should wait for easier deployment or use established cloud tools. The open-source model and source-attribution focus are genuinely novel, making it worth watching as it matures.

Korvo FAQ

What is Korvo? +

Korvo is a local-first AI workspace designed to keep data on the user's machine whilst providing AI-assisted reasoning with explicit source attribution. Built as an open-source project and launched on Show HN in August 2026, it addresses a specific pain point: AI assistants that hallucinate or obscure where information comes from. The tool works by indexing documents locally, processing queries without sending them to external servers, and returning answers tagged with the source material they came from. This architecture is particularly suited to teams handling confidential research, legal documents, or proprietary data where cloud processing is a barrier or compliance risk. The underlying mechanism relies on embedding-based retrieval and local LLM inference, allowing organisations to choose their own model backend. Unlike hosted alternatives such as Notion AI or Perplexity, Korvo requires manual setup and infrastructure; the trade-off is complete control over data. The project is early and actively maintained on GitHub, with a web interface for document upload and query interaction. Pricing has not been publicly announced, but the core software appears freely available under an open-source licence. Feature completeness lags behind commercial tools—batch processing, advanced filtering, and integration connectors are not yet standard—but the foundational idea of local-first reasoning with source tracing resonates with privacy-first teams. Korvo's competitive position is narrow by design. It does not attempt to replace ChatGPT or Claude for general tasks; instead, it targets users who cannot or should not send documents to cloud LLM providers. Organisations in regulated sectors (finance, healthcare, government) and teams handling intellectual property see the strongest fit. The Show HN launch indicates early developer interest, though user-facing documentation and deployment guides remain sparse. Setup time and ongoing maintenance are non-trivial for non-technical users. The clearest limitation is feature scope. Version 1 covers document indexing and QA; it lacks team collaboration, advanced permissions, multi-user caching, and integration with enterprise systems. Performance on large datasets or complex reasoning tasks has not been independently benchmarked. For users comfortable with setup friction and able to self-host, it offers genuine privacy and auditability; for everyone else, it remains a niche tool.

How much does Korvo cost? +

Korvo pricing: Pricing not publicly listed at time of review. Always confirm current pricing on the official site, as plans change.

Does Korvo have a free tier? +

Yes. Korvo offers a free plan or free credits you can use to evaluate it.

What is Korvo best for? +

Teams handling sensitive documents or research who need verifiable source attribution and data privacy guarantees..

When should you avoid Korvo? +

Avoid Korvo if: You need enterprise support, cloud convenience, or integration with mainstream productivity apps out of the box..

What are the main pros of Korvo? +

Runs locally without sending data to external servers, addressing privacy concerns; Explicitly traces reasoning back to source material, reducing hallucination risks; Open-source foundation makes it auditable and customisable for technical teams.

What are the main cons of Korvo? +

Early-stage product with limited feature depth compared to commercial alternatives; Requires technical setup and local infrastructure investment; No clear path to paid features or advanced capabilities documented.

Does Korvo have an affiliate program? +

No public affiliate program is listed for Korvo at the time of review.

How is Korvo rated? +

WireTensors rates Korvo 3.8 out of 5, based on capability, value, and fit for its intended use case.

What category does Korvo fall under? +

Korvo is categorised under productivity on WireTensors.

When was this Korvo review last verified? +

This review was last verified on 2026-08-04 against the vendor's official site.

Sources

• Korvo — official website — verified 2026-08-04