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Vault is an early prototype intended to summarize legal text, extract dates, and flag potentially sensitive information using local AI. Its author says document processing runs through Ollama and Gemma 2 2B without external AI API calls. That describes the intended design—not an independently audited privacy guarantee or validated legal-analysis system.

What Vault does

Mawaddah Dawood introduced Vault in a DEV Community post published October 2, 2026, describing it as a tool built for a lawyer relative. Its stated tasks are summarizing long text files, pulling out dates, and flagging information such as contact details. The linked public GitHub repository shows an app.py file, requirements.txt, and a demo directory; it shows no releases or descriptive project metadata at the time reviewed.

The author describes a stack of Streamlit for the interface, Ollama for local inference, and Google’s Gemma 2 2B model. Ollama lists the Gemma 2 2B artifact as 2.61 billion parameters and a 1.6 GB Q4_0 build, and lists text summarization among its intended use cases. Those are model-listing details, not performance measurements of Vault.

How to run the documented local setup

The project post gives this launch sequence:

  1. Install Ollama on the computer you intend to use.
  2. In a terminal, run ollama run gemma2:2b to obtain and start the model.
  3. From the project directory, run python -m streamlit run app.py to launch the interface.

The author says the prototype runs on a standard computer with 8 GB of RAM. Treat that as the author’s stated baseline, not a universal minimum or a tested guarantee: no independent device benchmark is provided. Speed and practical document size can vary with hardware, operating system, model quantization, runtime, and input length. The author also says this local path has no operation or API-token costs; that does not account for the computer, electricity, setup, or maintenance.

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What “local” does—and does not—establish

According to the author, Vault sends confidential text into local RAM, processes it with Ollama and Gemma 2 locally, and produces structured legal and security output. The author says no external AI API calls are configured and describes a privacy dashboard intended to show local-processing status. These are project design claims, not findings from an independent security audit.

Running inference locally can avoid sending document text to a remote model provider, but it does not by itself establish confidentiality, attorney-client privilege protection, legal compliance, or safe retention. The materials reviewed do not independently assess dependencies, telemetry, operating-system services, or later changes to the application. A local model is one part of a data-handling decision, not a substitute for evaluating the full system and its use.

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What the demonstration shows

The post describes a sample audit of a case note involving a possible NDA dispute. Vault produced a summary, extracted a hearing date and evidence deadline, and flagged a phone number and email address. The author explicitly labels the demo’s names, contact details, case references, and dates as synthetic. This illustrates the intended workflow; it does not show accuracy on real legal files or establish that the output is reliable for case decisions.

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Is Vault appropriate for legal work?

The available description presents Vault as a prototype, not an independently certified legal product. The reviewed materials do not provide an independent security audit, legal compliance review, or benchmark of its summaries and extractions. Do not treat its output as legal advice or as verified legal analysis; a qualified professional must check any result before relying on it.

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Whether local AI is suitable for a particular matter depends on more than where inference runs. Before choosing Vault, a cloud service, or another local tool, evaluate the actual data path and transmission behavior, independent security review, supported file formats and practical document limits, accuracy on representative legal material, setup and upkeep, and the obligations that govern the organization and matter. No comparative results on those criteria are provided for Vault.

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