On-premise
The full platform inside the institution's data centre, models and vector stores and orchestration and logs on infrastructure the CISO already controls.
Pharma operations under GxP
AI inside your regulated perimeter, traceable by design.
Deeplinq deploys on-premise, on a sovereign cloud, or air-gapped — and produces, on every model-assisted decision, the evidence a regulator or a QP expects to see.
The compliance envelope
Why pharma needs a different AI posture
Pharma decisions are not document decisions. They are records with ALCOA+ obligations, electronic-signature posture under 21 CFR Part 11 and EU Annex 11, change-control traces against filed dossiers, deviation histories that a quality review may reconstruct years later, and pharmacovigilance signals whose handling is reviewable by the authority that supervises each product.
Most AI platforms were built for a different customer — one that accepts vendor-hosted models, data paths a QP would never sign off on, and outputs whose provenance dissolves the moment the model is silently updated. A summary without a citation is not an answer a quality director can use. An output that cannot be reconstructed six months later is not an output a regulatory affairs director can defend.
Deeplinq keeps the entire chain under institutional control. The orchestration layer, the connectors, the agents, the model router, the prompt archive, the retrieval index, and the decision trace all sit inside the perimeter the institution already defends. Data integrity stays operative as a platform concern, not a logging afterthought. The sections below open four workflows, the evidence layer they share, and the deployment envelope that holds them.
Workflow 1
Quality document intelligence under ALCOA+
Your quality archive is the institutional memory of every deviation, CAPA, batch release, and supplier qualification — spread across QMS, EDMS, paper archives, and legacy quality software each site configured differently. The answers are in there. No one is ever sure the archive is complete before an inspection.
Deeplinq deploys agents against this archive. Ask in plain language — "show every CAPA on product X with supplier-attributable root cause", "list deviations on line 3 correlating with the Q3 raw material change", "which batch records reference the cleaning protocol revised last spring" — and receive answers with citations back to the source. A cited answer a quality director can verify, a QP can challenge, and an auditor can trace.
Pre-inspection gap surfacing works the same way. Missing signatures on a CAPA closure. A supplier qualification renewed against a scope no longer matching a product in purchase. A deviation logged without the follow-up investigation attached. The agents read what is there and flag what is not — in time to close gaps before the inspection does.
Data integrity stays operative. Every retrieval is attributed, every output carries its source citations, every interaction is archived with the model version that produced it. ALCOA+ is a platform concern, not a post-hoc reconstruction.
Workflow 2
Regulatory-submission intelligence and change control
Filed dossiers. Authority correspondence. Variation histories. Commitments made in section X of an application that reach years later into a renewal or post-approval question. A decade of change-control decisions supported by technical reports, quality reviews, and risk assessments — the reviewable record of how a product came to be approved and how it has evolved. Reconstructing that record under an authority question means opening four systems in sequence, against a clock the authority sets.
Deeplinq deploys agents against the regulatory organization's own filed dossiers, correspondence, and change-control archive. Ask for the rationale supporting the limit proposed in section 3.2.P.5.6 of a filed application, and receive the answer with citations back to the source filing — with the model version and retrieval context pinned to the output.
Inspection-readiness works proactively. The agents surface commitments in dossiers whose operational evidence is thin, variation histories where a downstream section was not updated consistently with an upstream change, authority follow-ups whose response record is incomplete. Not an audit judgement — an operational index of what a regulatory affairs director should look at before an inspector does.
Every workflow here sits strictly inside the sponsor organization. Deeplinq reads your own filed dossiers, your own correspondence, your own change-control history. It does not reach across organizations, it does not aggregate regulatory intelligence from other sponsors, it does not operate outside the organizational boundary your regulatory affairs function controls.
Workflow 3
Pharmacovigilance signal review inside the perimeter
Adverse-event case narratives. Signal-detection outputs. Periodic benefit-risk evaluation reports. Literature surveillance. Aggregate reports prepared for each authorized jurisdiction. Pharmacovigilance organizations carry a standing load of case triage, signal review, and reporting deadlines — under residency and jurisdictional constraints no vendor-hosted AI platform was designed to respect.
Deeplinq deploys agents against the safety database the pharmacovigilance function already operates. Plain-language queries — "show cases coded under PT X for product Y in the last reporting period", "which literature references since the last PSUR mention the signal flagged on product Z", "summarize the medical review rationale for cases downgraded from serious to non-serious last quarter". Answers with full citations back to case record, reviewer note, and literature reference.
Signal review works against the institution's own data and the literature it surveys under its own licences. Model-version pinning holds across reporting periods, so a signal conclusion documented in one PSUR cycle can be reconstructed in the next. Every agent interaction is archived with its prompt, retrieval, and decision trace.
Every retrieval, every inference, every archived trace stays inside your perimeter. Deeplinq does not federate pharmacovigilance data across organizations, does not build networked safety intelligence, does not pull signal patterns from outside the sponsor's own archive. The safety function you already run, made queryable inside the walls it already operates behind.
Workflow 4
Supplier and CDMO quality surveillance, inside your control
Quality agreements. CDMO batch-review documentation. Change notifications from API suppliers and packaging vendors. Audit reports. Deviation notifications from CDMO partners. In a multi-site pharmaceutical organization, supplier surveillance is a standing obligation against files arriving from dozens of external parties.
Deeplinq deploys agents against the supplier archive the quality organization already holds. Plain-language queries — "list CDMO deviation notifications in the last twelve months with closure status", "which API supplier change notifications are outstanding against active production products", "cross-reference the last audit report for supplier X against the recent deviation trend". Answers with citations back to the supplier document, the audit report, and the quality-agreement clause.
Change-control continuity surfaces as a pattern, not a search. A supplier qualification renewed against a scope no longer matching currently-used material. A CDMO deviation trend correlating with a process change your side approved months earlier. An audit finding whose corrective action was logged but whose verification inspection was never closed. The agents surface the gaps before a regulator does.
The boundary is explicit and architectural. Deeplinq works against the supplier and CDMO data your organization already holds — quality agreements, audit reports, change notifications, deviation records, batch-review files. It does not ask your suppliers or your CDMOs to integrate, federate, or exchange. It does not reach into their systems. Your supplier surveillance is built from the archive you control, inside the perimeter you defend.
The evidence layer
What a GxP auditor receives
A regulatory inspection is not passed on claims. It is passed on evidence. Deeplinq treats the evidence layer as a first-class platform concern, and narrows what a regulator or internal auditor can expect from any model-assisted decision in a pharma context.
Every prompt and response is archived with full context. Every retrieval — document, record, field, correspondence — is attributed to its source. Every model call is logged with identifier, version pin, parameters, timestamp, outcome. Every agent action carries the decision trace: inputs, retrieved context, human approval status, access-control evaluation.
Model-version pinning is the structural backbone. The model that produced an output six months ago reconstructs the reasoning today. Silent substitution is eliminated by architecture. Electronic-records posture under 21 CFR Part 11 and EU Annex 11 is addressed through archival, access control, and signature-state handling. ALCOA+ principles stay operative across the full surface.
When an inspector asks for an evidence bundle scoped to a specific deviation, submission question, or quality event, the institution exports a structured archive covering every element above. No reconstruction project. No vendor-side retrieval.
Deeplinq does not certify GxP compliance — no platform can. Compliance is a property of your validated system, your quality organization, your QP, and your regulatory function. What deeplinq produces is the evidence trail your auditor expects to see, with the integrity posture your quality function expects to preserve.
Deployment modes
Deployment inside the regulated perimeter
Pharma residency is not one question. It is several that the data itself asks.
Clinical trial data: residency obligations shaped by the country of conduct. Pharmacovigilance safety data: jurisdictional handling constraints per reporting territory, each authority expecting the safety system it oversees to remain reviewable inside its rules. Supplier and CDMO process data: contractual residency clauses your procurement and legal functions negotiated. Manufacturing batch records: QP access requirements under the institution's own control, not through a vendor tenancy.
Deeplinq supports four deployment modes, aligned with parent platform architecture and narrowed here to pharma-specific drivers.
National or private cloud
A sovereign region or private-tenant environment chosen by the institution for residency alignment with a jurisdictional requirement.
Air-gapped
No external network path, inference and retrieval and orchestration entirely local, model and knowledge updates handled through controlled physical transfer.
Hybrid with a sovereignty boundary
Sensitive GxP workloads and regulated data on-premise or on a sovereign cloud, non-sensitive workloads extending where the institution chooses, with the boundary defined explicitly and enforced at the platform layer.
Model inference stays where the data requires. Evidence export stays inside the perimeter the institution defends. The data shapes where the platform runs. Not the other way around.
Model agnosticism
Model choice as a compliance decision
Locking a pharma platform to a single model provider is a compliance decision, whether or not the institution frames it that way. The provider's roadmap becomes the institution's model roadmap. The provider's regulatory posture becomes a variable in the institution's inspection readiness. The provider's silent model updates become silent shifts in how a filed application is supported a year after submission.
Deeplinq holds model choice behind an interface the institution controls. Cloud APIs for non-sensitive workloads where data residency can be enforced through vendor contracts. Open-weights models for on-premise or air-gapped deployments where the model lives inside the institutional perimeter. Selected by task, by data class, and by the regulator's expectation for the workflow at hand. Model versions are pinned so an output produced during submission preparation can be reconstructed exactly during a post-approval authority question. Models can be replaced without rewriting the platform above them.
Currently supported
Cloud APIs (with data residency)
- OpenAI
- Anthropic
- Mistral
Open-weights (self-hosted)
- Llama
- Qwen
- Mistral open
- Gemma
- Falcon
Start with the workflow that earns the evidence
Start a conversation. Not a sales process.
A working session with our team on your regulatory envelope, your deployment constraints, and the quality, regulatory, pharmacovigilance, or supplier-surveillance workflow where the evidence posture matters most. Pragmatic, technical, short.