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The regulator changes. The evidence does not.

Different regulator, same four requirements: answers from material you approved, enforcement in the request path, a record afterwards, and a deployment inside your own boundary.

Request a scoping callHow the platform is built

The buying question

Four questions, in roughly this order, every time.

They arrive in a different vocabulary in each industry, and behind the vocabulary they are the same four. Each one is answered somewhere on this site rather than in a sales conversation.

  • Where did that answer come from?

    Answers are generated from your own corpus and carry a citation back to the passage they came from, with a confidence score attached. An answer that cannot be grounded well enough is routed to a fallback rather than shipped.

  • What stops it saying the wrong thing?

    Screening and sensitive-data handling run in the request path, before the model call and again on the response — not as a post-hoc review of transcripts, and not as advice the model is asked politely to follow.

  • What can we show an auditor afterwards?

    Every question, answer, score and configuration change is logged, timestamped, attributable and exportable to the collector you already run. The configuration itself is versioned, so “what was it doing in March” is a question with an answer.

  • Where does our data actually live?

    Wherever you put the container. Managed service, your own cloud account, a sovereign region, OpenShift, or fully air-gapped on-prem — the same software, the same controls, and no separate edition for the deployments that are hardest to reach.

Who runs it

The same four answers, at enterprise scale.

Investor relations at a telecom, HR triage at a software company, a Slack assistant for a distributed workforce, and a multi-model chat platform inside a company's own Azure tenant. Different front ends; one governed platform behind them.

  • Telecom · Investor relations

    Investor Q&A preparation, automated end to end.

    • Anticipates likely earnings-call and analyst questions
    • Cross-references historical Q&A archives and financial data systems
    • Drafts context-rich, data-backed responses for executive review
    • A foundation for agentic search across the enterprise
    Earnings & analyst Q&A · Private cloud

  • Software · HR operations

    HR inbox triage, and a Center of Excellence for agentic AI governed by IT.

    • High-volume HR inbox triage — common questions answered straight away
    • Complex cases routed to the right HR specialist with full context
    • One knowledge layer over email, messaging and document repositories
    • Business users build agents without code; IT governs them centrally
    HR triage · Agentic AI CoE · Private cloud

  • Consumer tech · Internal HR

    Chilla — a Slack-native HR assistant for a distributed workforce.

    • An HR assistant inside Slack, held to strict policy guardrails
    • Context-aware by country and city, drawing on internal pages and Workday
    • Nudges managers on time-off approvals, onboarding tasks and milestones
    • Opens a Zendesk escalation when a person needs to review
    Slack HR assistant · Public cloud

  • Home improvement · HR & call center

    yETI — a governed, multi-model chat platform inside the company's own Azure tenant.

    • One chat platform serving HR and the call center
    • The right model chosen per task, from several
    • Word and PDF upload, chat history, and company knowledge indexed for retrieval
    • Deployed end to end inside the existing Azure tenant — no data leaves
    Multi-brand chat · Azure-nativeRead the story

Story 1 of 4: Verizon

By industry

Where the argument gets specific.

Two industry pages, one regional page, and the deployment argument that matters most to everyone whose constraint is jurisdiction rather than sector.

These are the industries this site has material for, not the limit of where the platform runs. If yours is not here, the platform pages describe the same capabilities without an industry lens — and a scoped conversation is faster than a page.

  • Financial services

    Deal and market research, customer-facing banking, and the internal policy answers a branch network runs on — with redaction, citation and an exportable audit trail on every request.

    Financial services

  • Healthcare

    Clinical and administrative questions answered from approved material, document intake and summarisation, and expert-in-the-loop authoring — with sensitive data removed before anything reaches a model.

    Healthcare

  • Latin America & the Caribbean

    The same regulated-institution argument for a region rather than a sector: answers in Spanish, Portuguese or English from one knowledge base, running as a container inside your own perimeter, and an engagement that starts on site.

    Latin America & the Caribbean

  • Sovereign and air-gapped deployment

    For public sector, defence and any organisation whose binding constraint is jurisdiction: the topologies NeuralSeek runs in, what each one means for residency, and why an air-gapped deployment has no vendor kill switch.

    Data and AI sovereignty

Three questions about industry fit.

All three come up before the first demo, and all three have short answers that are worth having in writing.

  1. Is there a different product per industry?

    No. There is one container, and the industry pages differ only in which workloads they lead with and which framework vocabulary a review will use. There is no healthcare edition, no sovereign edition and no compliance add-on: the isolation, redaction, grounding and audit primitives ship in every deployment, including the managed one you would evaluate on.

  2. We are regulated, but not in either of those industries.

    The thing that determines fit is the shape of the obligation, not the sector label: whether you have to prove where an answer came from, keep sensitive fields out of a model call, produce a record for an examiner, and control where the workload physically runs. If that describes you, the platform pages answer it directly — start with guardrails and deployment.

  3. Can we speak to someone in our sector?

    Reference conversations are arranged individually rather than advertised, and this site deliberately does not publish a customer list — several deployments are real but not cleared to be named, which is a different thing from not existing. Ask for a reference in your sector and it is either arranged or you are told plainly that there is not one to offer.

See it running inside your own boundary.

Talk to a NeuralSeek expert about how it fits your stack, where your data has to live, and the governance your auditors already expect.

Request a demo