Secure AI Infrastructure for Biotech & Enterprise Research

Researgency is a secure AI infrastructure platform built specifically for biotech, pharmaceutical and enterprise research teams. It deploys autonomous AI agents for scientific research, scientific intelligence automation and private research workflows inside your own VPC, on-premise cluster or air-gapped environment. The platform unifies ontology-aware retrieval, domain-specific LLMs for biotech, multi-agent verification and enterprise knowledge retrieval into a single control plane. Teams use Researgency to accelerate biomedical literature monitoring, drug discovery AI agents, patent landscape analysis, competitive pharma intelligence and regulatory workflow automation — while preserving complete data sovereignty and audit readiness.

Security is engineered at every layer. Researgency uses zero-trust AI architecture, customer-managed encryption keys, encrypted AI workflows for biotech and read-only LLM deployment by default. All inference, vector search and observability can run inside a customer-owned VPC or on-premise environment, supporting AI compliance frameworks for life sciences such as 21 CFR Part 11, HIPAA, GDPR and GxP. Every retrieval, prompt and tool invocation is captured in cryptographically verifiable audit trails to satisfy enterprise security and regulatory review.

Yes. Researgency is designed for on-premise AI for regulated industries and single-tenant VPC AI deployment for biotech. We support AWS, Azure, GCP and on-prem Kubernetes clusters, including fully air-gapped AI for pharma where no outbound network connectivity is permitted. Hybrid topologies allow sensitive data to remain on-prem while non-sensitive coordination workloads run in a managed control plane — without compromising your private AI deployment posture.

Autonomous AI agents are goal-directed reasoning systems that decompose a research question into sub-tasks, retrieve evidence from authorised sources, verify each claim against citations and synthesise a structured answer. In Researgency, private AI agents for R&D are bound by access control lists, run under configurable human-in-the-loop AI policies, and emit a complete trace that can be replayed, reviewed and exported for compliance audits.

AI data sovereignty in Researgency means your research data, vector embeddings, prompts, model outputs and agent traces never leave a perimeter you control. Storage, vector indexes, model endpoints and observability all run inside your chosen jurisdiction. We support EU, UK, US, Japan and other residency requirements, and integrate with your existing identity, key management and network segmentation policies to deliver GDPR-ready AI deployment across multinational research teams.

Researgency supports biotech, pharmaceutical, medical device, in-vitro diagnostics, CRO and CDMO organisations, hospital research networks, genomics companies and translational research institutes. Our compliance posture is aligned with GxP, HIPAA-compliant AI platform requirements, GDPR-ready AI deployment, ISO 27001, SOC 2 Type II and 21 CFR Part 11 audit ready AI systems — providing a defensible posture for IT security, quality and regulatory affairs reviewers.

Multi-agent verification systems route a research question through specialised agents that cross-check evidence, citations and reasoning. A retriever agent gathers candidate sources, a synthesis agent drafts an answer, a verifier agent rejects unsupported claims and a critique agent challenges weak inferences. This choreography sharply reduces hallucination on regulated biotech and pharmaceutical tasks where unverified statements can have safety, regulatory or commercial consequences.

Researgency automates systematic literature reviews, evidence synthesis, biomarker discovery AI, drug discovery AI agents, patent landscape analysis, competitive intelligence AI for pharma, pharmacovigilance AI, signal detection automation, regulatory horizon scanning, KOL identification AI, real world evidence AI, medical writing automation and CMC documentation AI. Each workflow is delivered as a configurable agent with measurable inputs, outputs and quality gates.

Generic enterprise AI platforms are optimised for office productivity. Researgency is purpose-built for scientific rigour: ontology-aware AI search, domain-specific LLMs for biotech fine-tuned on biomedical corpora, citation-grounded retrieval augmented generation for pharma, GxP-aligned audit trails and a deep library of regulated industry agents. The result is an enterprise AI platform for life sciences that scientific, regulatory and quality reviewers can actually trust.

Yes. Researgency provides HIPAA-aligned controls for protected health information, GDPR-ready data residency and subject rights tooling, and GxP-aligned validation evidence including IQ/OQ/PQ documentation, formal change control and 21 CFR Part 11 electronic record requirements. Our AI governance platform supplies the documentation, controls and traceability your compliance team needs to bring a regulated AI system into validated use.

Enterprise AI knowledge retrieval in Researgency uses ontology-aware retrieval augmented generation with a private knowledge graph for biotech. The platform indexes SOPs, ELN entries, LIMS data, clinical documentation, regulatory dossiers, internal reports and external literature, then returns citation-backed answers that respect document-level and field-level access control. Semantic search for pharma, ELN integration AI, LIMS data intelligence and enterprise document intelligence all share the same governed retrieval layer.

Yes. Researgency ships with connectors for the major ELN, LIMS, CTMS, regulatory information management, document management, SharePoint, Confluence, Box and S3 systems used in biotech and pharma. An open API plus webhook surface lets you plug bespoke data sources, internal services and model registries into a governed enterprise model registry — extending your AI governance platform without bypassing security controls.

Most enterprise biotech AI intelligence platform deployments go live within 4 to 8 weeks. Phase one provisions the secure infrastructure (private VPC, identity, KMS, network policies). Phase two onboards two to three priority knowledge sources with ontology mapping and access controls. Phase three configures domain-specific agents for your research, regulatory and competitive intelligence teams. A dedicated solutions engineer guides validation, change management and user enablement.

Every retrieval, prompt, citation, tool call and agent decision is recorded with cryptographic integrity. Reviewers can replay any agent run, inspect citations, compare to prior versions and approve or reject outputs before they are released into a validated workflow. This audit-ready AI systems posture satisfies 21 CFR Part 11, GxP review expectations and internal audit programs for biotech and pharmaceutical enterprises.

Read-only models are AI agents that can read enterprise data and propose actions but cannot mutate source systems directly. In a read-only LLM deployment, an agent can summarise an ELN entry, draft a regulatory section or surface a competitor signal — but cannot silently change records in your ELN, LIMS, submission system or quality management system. Any write operation must be explicitly approved by a human reviewer, preserving the integrity of your validated systems while still delivering the speed of autonomous AI agents.

Researgency supports a curated catalogue of open and commercial models that can run inside your environment: domain-specific scientific LLMs fine-tuned on biomedical corpora, general-purpose foundation models and lightweight specialist models for classification, extraction and embedding. Customers can bring their own fine-tuned scientific LLM, manage versions via the enterprise model registry and apply governance policies for explainable AI for pharma and interpretable AI for clinical research.

Adverse event monitoring and pharmacovigilance AI agents continuously scan authorised sources for safety signals, run automated triage against your case definitions and produce structured outputs that feed into your safety database. Combined with clinical safety AI and risk management automation, this lets safety and medical affairs teams move from reactive case handling to proactive, evidence-driven signal management — all under audit trail.