Peer AI

Learned models perform the central authoring and predictive work. Document specific agents turn source material into protocols, clinical study reports, safety narratives, and other submission content, while the Anticipate function analyzes likely regulatory questions. Orchestrate connects that work to document dependencies, resource capacity, and timeline risks across a program.

Templates, style guides, and source traceability shape the output, but the main service remains AI assisted preparation and analysis rather than a standalone document repository. Medical writers review generated material and provide feedback throughout authoring. Removing the models would remove the platform's core drafting and prediction functions even though customer permissions and program tracking would still support the surrounding workflow.

Medical writers review generated drafts against source material and provide feedback during authoring. In the complex study report case, the workflow began with a tailored document shell and data mapping, then moved through AI drafting and collaborative review with customer writers. Outputs can be delivered in DOCX for quality control and further revision. Customer administrators also control user access and third party integrations.

These are identifiable human control points rather than a general promise of oversight. Automatic stopping thresholds, mandatory escalation criteria for unreliable content, and the recovery process after an incorrect regulatory prediction remain unspecified. Draft generation does not remove the customer's responsibility for deciding what enters a filing.

Specialized agents extract and organize source information, draft text, apply document requirements, and support review. Author uses materials such as protocols, statistical plans, tables and listings, safety notes, templates, and style guides. Anticipate analyzes regulatory review patterns by therapeutic area, submission type, and review committee, including prior submission history. Orchestrate connects authoring activity to a program's dependencies and resource needs.

This explains the tasks and information flow without naming the underlying foundation models or their suppliers. Training collection composition, production model versions, adaptation methods, and the evaluation and approval process for model updates remain unspecified across the platform.

AWS is identified for infrastructure, and deployment can be placed in the customer's AWS environment. Peer AI also says its vendors follow zero retention policies and that customer data is excluded from model training. Those commitments do not identify the model vendors, their individual roles, or which parties receive source documents and prompts. The website privacy policy names Framer and Google Cloud for site delivery and security, a separate scope from the clinical authoring service.

A complete product processor register, model routing map, and provider specific retention and training terms remain unspecified. The existence of a private deployment option does not establish an entirely internal model chain.

An unnamed biotech compared 56 oncology safety narratives using identical tables, patient profiles, and MedWatch forms. Manual first drafts required 336 writer hours; the AI workflow required 16 writer hours plus four hours of model processing. Reviewers scored accuracy, completeness, and readability on a five point scale, but the evaluation date, customer identity, underlying ratings, and reviewer independence remain insufficiently specified.

Another unnamed customer reduced writing work for a complex study report from 40 to 17 working days, while elapsed time fell from five months to one. These are different measures. The cases concern authoring, without establishing current performance of the newer program orchestration or query prediction functions.

Peer AI commits that customer data is not used for model training and that it and its vendors follow zero retention policies. The application terms also describe customer control over retention and export settings, indicating that operational document storage and model processing need to be distinguished. Source traceability and medical writer review help detect errors in a draft.

Fixed service deletion windows, the scope of each provider's retention commitment, and deidentification rules for clinical source information remain unspecified. The website's separate permission to use deidentified visitor information does not define patient data rights in the service. Technical hallucination controls and a detailed AI safety incident response are also unclear.

GDPR compliance and protection for clinical data are claimed for the platform. The privacy policy principally addresses website visitors, contact forms, analytics, and site infrastructure, while the application terms place customer data management under a separate customer agreement. Customers retain ownership of uploaded information and control access, permissions, retention, exports, and integrations.

These controls are relevant to regulatory source files, which can include patient profiles and safety information. They do not supply a readable HIPAA BAA, explicit business associate scope, or complete service privacy commitments for clinical data. Website deidentification and marketing provisions do not establish the terms applying to submitted trial records.

The trust center lists a 2025 SOC 2 Type 2 report and describes penetration testing, while product controls include encryption at rest, segmented networks, identity permissions, secure development practices, and continuous monitoring. The report requires a company access request rather than direct download, leaving the complete audit scope and current renewal details behind that process.

The application terms also require individual credentials and place account protection responsibilities on authorized users. Those responsibilities are separate from the vendor's operational assurance. A SOC claim and technical control descriptions establish useful security context, but the actual attestation is needed to confirm which services and review period it covers.

Peer AI prepares documents and supports program coordination for life sciences regulatory teams. Author handles source driven drafting, Orchestrate tracks deliverables and dependencies, and Anticipate estimates the questions regulators may raise. These functions support a sponsor's submission process rather than independently diagnosing a patient or selecting treatment. No medical device clearance is claimed for the platform's stated use.

References to faster drug approvals or stronger regulatory alignment describe the intended value of the workflow, not authorization of the software itself. The April 2026 expansion also adds functions whose regulatory predictions are distinct from the underlying clinical evidence contained in a customer's submission.

The platform records AI assisted decisions and preserves their sources and reasoning so regulatory teams can review work across a program. Medical writers assess drafts, and the oncology narrative case used ratings for accuracy, completeness, and readability. Those measures address document quality within a particular authoring exercise. They do not establish comparative performance across patient groups, therapeutic areas, languages, or regulatory jurisdictions.

A formal model governance framework, release acceptance criteria, independent model behavior audit, and a documented process for approving model changes remain unspecified. Program visibility and audit history support customer oversight, but do not by themselves evaluate fairness or reliability of the newer query prediction function.

Recorded decisions, source links, and editable drafts allow medical writers to inspect and challenge generated content before filing. The customer controls access and data settings under a separate agreement, and remains responsible for the completeness and quality of material supplied to the service. The application terms disclaim accuracy, reliability, completeness, and error free operation unless a separate written agreement provides otherwise.

They do not establish a standard output guarantee or contractual remediation obligation for an erroneous draft or prediction. A private agreement could change those terms, but its protections cannot be inferred from the review workflow or from general statements about regulatory expertise.

API and MCP connections are offered for organizational data sources, templates, and style guides. The authoring cases use study files such as protocols, statistical plans, tables and listings, patient profiles, and safety forms, with DOCX output supporting an existing quality review workflow. These are useful document and data exchange routes for regulatory teams. They do not establish a named clinical record system connection or a production EHR interface.

Patient record ingestion permissions, supported EHR standards, data directions, and chart writeback remain unspecified. A source document containing clinical information is therefore different from a verified live connection to the system that originally held it.

Customers can use Peer AI's hosted environment or deploy within their own AWS environment. The security architecture includes segmented virtual private clouds, identity and access controls, encrypted storage, and continuous monitoring on AWS infrastructure. Customer administrators manage access and can enable or disable integrations under the service agreement. These details establish deployment choices and some separation controls.

Committed storage and inference regions, the full customer isolation boundary, disaster recovery geography, and processing by external model services remain unspecified. A customer controlled AWS environment does not establish that every inference operation stays inside it, and website hosting statements do not settle service residency.

Organizations obtain access through a separate customer agreement and authorize individual users. The customer can provision or remove access, enable integrations, manage permissions, and control retention and export settings within the arrangement. Deployment can use Peer AI hosting or the customer's AWS environment, and onboarding can include medical writing expertise and adaptation to templates and style guides. These choices make the delivery model understandable before contact.

License amounts, billing units, minimum commitments, document allowances, implementation fees, and the price difference between deployment options remain unspecified. Reported authoring efficiency is a benefit claim rather than a price or a contractual saving.

The platform serves pharmaceutical sponsors, biotechnology companies, and CROs preparing regulatory submissions. Its document scope spans preclinical, chemistry and manufacturing, and clinical work, including protocols, IND modules, safety narratives, and Phase 3 study reports. An oncology narrative comparison supplies a concrete authoring application, and another case covers a six-part report combining several studies and protocol amendments.

Those examples support part of the intended scope, but the customers remain unnamed. Comparable validation across therapeutic areas, submission types, jurisdictions, and the newer predictive review functions is incomplete. Broad document coverage does not establish equivalent performance for every document or regulatory setting.