Knowledge buried in documents
Decades of reports, research, contracts, and operational records sit in PDFs and legacy archives. Turning that into usable answers at the point of decision is the work.
Decision-grade answers from your own knowledge.
Most AI tools run on public data. Dash runs on yours. Private, cited, and isolated to your organisation, it turns large bodies of documents — reports, research archives, contracts, technical libraries, policies, internal knowledge bases — into answers your teams can trust and act on.
Dash Document Intelligence
Live query
Cited answer forming
Condition 14ARFI-07SoE 3.2
Substantive ApplicationsFTA source class
Applicant ResponsesFTA source class
RFI ResponsesFTA source class
Comment ResponsesFTA source class
Statements of EvidenceFTA source class
Using SOC 2 compliant, enterprise-grade infrastructure
Anthropic
Your knowledge doesn’t scale on its own.
Experts can’t be everywhere
Subject-matter specialists answer overlapping versions of the same question every day. Your expertise doesn’t scale beyond the people who hold it.
Generic AI isn’t built for this
Public chatbots hallucinate, strip citations, and expose your knowledge base to models that don’t answer to you. Decisions need to be defensible — not approximated.
Your knowledge. Your governance. Your AI.
Your knowledge
Trained only on your documents. No public data, no third-party leakage, no answers from elsewhere.
Your governance
Permission-aware retrieval, cited responses, and audit trails so every output is defensible to a board, regulator, or internal stakeholder.
Your AI
Tuned to your industry and grounded in your own verified, curated corpus — and it gets sharper every day from how your teams actually use it.
Your data stays yours.
Dash runs on SOC-2-grade managed cloud in an isolated, per-client deployment, encrypted in transit and at rest. Your knowledge is never used to train public models, access is permission-aware, and data residency can be pinned to your region — so your organisation stays compliant from day one.
What we’re reading on high-stakes AI.
Fortune · Aug 2025
MIT report: 95% of generative AI pilots at companies are failing
~95% of enterprise GenAI pilots show no measurable P&L impact; tools bought from specialists succeeded ~67% of the time versus internal builds at a third of that.
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Fortune · Oct 2025
Deloitte caught using AI in a $290,000 government report after hallucinations flagged
A 237-page government report contained AI-fabricated references and misquoted judgments, forcing a revised version and a partial refund.
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Stanford Law School (RegLab / HAI) · Jun 2025
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
Even retrieval-augmented, “hallucination-free” legal tools were wrong more than 17% of the time; general-purpose models hallucinated on 58–88% of legal queries.
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The Register · Oct 2025
Employees regularly paste company secrets into ChatGPT
77% of AI-chatbot users paste data in, and roughly a fifth of those pastes contain PII or card data — mostly through unmanaged personal accounts.
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FCA / Bank of England · Dec 2025
AI in UK financial services (research note)
75% of financial firms use AI, but only 34% claim complete understanding of the systems they run — a measurable governance gap.
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The Register · Apr 2026
If an AI agent screws up while running your business, there’s nobody to sue
Vendors push autonomous agents into core decisions while resisting liability — regulators insist the deploying organisation stays accountable.
Read articleGet the Dash Info Pack
A short guide on surfacing what matters in a large knowledge base — with examples of how Dash works and what a deployment looks like.