Product Overview

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Carys vs General AI Assistants

ChatGPT, Claude, and Gemini are genuinely useful tools. Carys is purpose-built for a different job: structured analytics on sensitive business data, with the security controls and evidence trail that high-stakes decisions require.

General AI assistants excel at language tasks — drafting, summarising, brainstorming, first-pass research. They offer instant access, broad capability, and low setup friction. Most teams should keep using them for exactly that.

The problem emerges the moment you point the same workflow at real business data: customer records, financials, risk data, pricing, HR information. Now the bar is different. Confident answers that contain silent analytical errors become expensive. Outputs that cannot be reproduced or audited create accountability gaps. Data shared into a general-purpose chat interface may not stay where you assume it does.

Chat tools help you write and think. Carys is built for analysing sensitive data safely — with isolation, zero retention, multi-LLM validation, and audit-ready evidence.

General AI: Strong At

  • Drafting, editing, and rewriting content
  • Summaries and brainstorming
  • Code assistance and quick lookups
  • Low-stakes productivity tasks
  • Fast iteration where precision is not critical

General AI: Where It Falls Short

  • Hallucinations and silent errors in complex analytical logic
  • No defensible audit trail for how conclusions were reached
  • Results shift with prompt wording, user skill, and model updates
  • Outputs typically require an analyst to translate into an action plan
  • Unclear data retention and usage policies for sensitive business inputs

What Carys Does Differently

Isolated Secure Execution

Every request runs in an isolated sandbox with no internet access and zero prompt or output retention. Sensitive data stays in a controlled environment — not passed through a general-purpose chat service with uncertain retention behaviour.

Multi-LLM Cross-Checking

Carys uses multiple AI models as components, not a single black box. A dedicated review layer independently validates analytical steps and flags discrepancies before they reach the final output — removing the single point of failure that comes with blind trust in one response.

Reproducible Method

Analysis is backed by generated code and a documented method. The same question produces the same decision logic — not a different answer depending on who asks or how they phrase it. Outputs are replicable if challenged by stakeholders, auditors, or regulators.

Decision-Ready Outputs

Carys outputs are structured for immediate use: a target list, recommended actions, decision thresholds, an impact estimate, and a measurement plan. Not narrative that still needs an analyst to convert into a plan — the conversion is already done.

The Complementary Workflow

Keep your existing assistants for communication and productivity — drafting the email, summarising the meeting, building the slide deck. Bring Carys in when the question involves sensitive data, a high-stakes decision, or an output that someone will need to act on and defend.