Healthcare
Clinical and administrative questions answered from material you approved: sensitive fields removed before the model call, the answer cited, the run logged, inside your network.
What gets built
The difference between them is what arrives at the front and what leaves at the back. The middle — de-identify, retrieve from approved material, generate, check, log — does not change, which is why the second use case is much cheaper than the first.
A question is de-identified before it goes anywhere, answered from the corpus your organisation approved, drafted by the model you chose, curated against a confidence floor, and logged. Answers cite the source policy they came from, so the reader can check rather than trust.
A referral packet or a scanned chart is read, keywords extracted, matched against your own provider and specialty records, and returned as a drafted summary document. What normally makes this project hard — the scanning, the matching and the audit record — is nodes in the same flow rather than three systems.
Research, drafting and retrieval assistance for the specialists who write clinical and educational material, with accuracy standards enforced as guardrails and a human review step that is part of the workflow rather than a policy about it. The repetitive half moves; the judgement stays.
Benefits, coverage, scheduling and process questions answered from one verified knowledge base, with harmful-language filtering and sensitive-data detection on every reply, and the same answer whichever channel it was asked through.
Who runs it
Expert-in-the-loop authoring for a medical-education company, and a student concierge for a public university — the same pipeline, pointed at different material and different readers.
Medical education · Pharma & biotech
Higher education · Student services
Story 1 of 2: AdMed
Also running NeuralSeek in healthcare and research
What a privacy office checks
None of these is a healthcare tier or a compliance add-on. They ship in every deployment, including the managed one you would evaluate on — they are listed here because these are the four a privacy office opens with.
13 detector categories and five enforcement actions — mask, flag, hide, delete, or pass through with a warning — configurable per intent.
TLS 1.2+ on all network traffic. Data at rest under AES-256, with keys held in a FIPS-validated KMS.
Every question, answer, score and configuration change logged, timestamped, attributable, and exportable to S3, Splunk or Datadog.
Your data stays yours. NeuralSeek does not train models on your prompts, documents or interaction logs.
Document intake
A referral packet is read, its sensitive fields taken out, its keywords matched against your own provider and specialty records, and a summary drafted and written to your log. The scanning, the matching and the audit record are steps in one flow.
Read, not re-keyed
The container runs inside the boundary you put it in, and inherits it: your network controls, your key management, your logging, your incident process. That is why the useful question is not whether the vendor holds a certificate but whether the software can run inside the environment you have already had audited — and it can, down to a network with no route to the internet.
Attested
SOC 2 Type II
Attested at the vendor level and audited annually. The report is available under NDA.
The frameworks below are not certifications NeuralSeek holds. They are requirements it is built to meet inside your environment, where your own controls and your own audit apply.
Control mappings, not compliance claims. Validate your posture against your own obligations, with your own counsel — the certifications page sets out where the line between the two sits, and the security team answers questionnaires directly.
Each one has a short answer, and each short answer is about architecture rather than about a promise.
What you configure to reach it, and nothing else. Sensitive-data detection runs on the request path before the model call, with the enforcement action set per intent — mask, flag, hide, delete, or pass through with a warning — so a question about scheduling and a question about a patient record need not be handled identically. If the answer has to be that nothing leaves at all, point the model layer at an open-weight model on hardware you own.
Yes, and that is a standard topology rather than a special engagement. The same container runs as a managed service, in your own cloud account, in a sovereign region, on OpenShift, or fully air-gapped on-prem with no connection to the public internet — with the same isolation, redaction and audit primitives in all five. There is no reduced feature set for the deployments that are hardest to reach.
Because it says so. Retrieval runs over the corpus you loaded, the answer carries the passages it was grounded in and a confidence score, and an answer that cannot clear the floor you set is routed to a fallback rather than shipped. The run is replayable afterwards — which agent, which model, what each step returned — and the whole record exports to the system you already keep logs in.
Talk to a NeuralSeek expert about how it fits your stack, where your data has to live, and the governance your auditors already expect.
