Custom RAG development in Philadelphia.

Build vs Veeva vs ContractPodAi vs custom.

Regulated-corpus questions from Philadelphia teams.

Can a RAG system handle FDA submission cross-referencing across NDAs, BLAs, and supplements?

Yes, and it is one of the highest-value patterns in pharma RAG. A regulatory specialist preparing a supplement to an existing approval typically has to cross-reference the original NDA or BLA, every prior supplement, the labeling history, the inspection findings, and any related ANDA filings if the molecule has reached generic availability. The pipeline ingests each submission package with hierarchical chunking aligned to the eCTD structure (Module 1 through Module 5), preserves the section headings and document IDs, and indexes the cross-references explicitly so the retrieval pass can return the linked documents alongside the matched chunk. We have built this against Veeva Vault Submissions and against on-prem regulatory document repositories at large pharma sponsors.

How does the system handle Vanguard-style prospectus and SEC filing libraries?

Mutual-fund prospectuses, SAIs, and SEC filings have a predictable structure that retrieval can exploit. We chunk along Item headings (Item 1 Business, Item 1A Risk Factors, etc. for 10-Ks; the equivalent prospectus sections for fund filings) with parent-section context preserved, so a query about a specific risk factor returns the relevant paragraph plus the section heading it sits inside. Metadata tags include filing date, fund family, fund class, and CUSIP. A query like 'show me how the fund describes derivative use across the last five annual prospectus updates' runs as a metadata-filtered semantic search rather than a brute-force read. This is the workflow Vanguard, SEI, and other Philadelphia asset managers run constantly during proxy season and annual update cycles.

What does payer-contract retrieval look like for an Independence Blue Cross style insurer?

Payer contracts are document-heavy and structurally specific: master services agreements, fee schedules, medical policy bulletins, prior-auth criteria, NCD and LCD references from CMS, and amendment letters. The corpus often lives in a contract-management system (Icertis, ContractPodAi, or a homegrown system on SharePoint) and the queries come from medical-policy, network-management, and provider-contracting teams. We build the retrieval to support Boolean-style filters on contract type, effective date, provider taxonomy, and amendment number, with semantic search running over the filtered subset. The output includes the contract section, the amendment that introduced the language, and the effective date so a provider-relations specialist can answer questions in seconds rather than a half-day of manual lookup.

Is RAG enough for clinical decision support or does it need an FDA SaMD review?

Clinical decision support that augments a human clinician's decision typically stays on the augmentation side of the FDA's 21st Century Cures Act guidance and does not require SaMD classification. A RAG system that retrieves the relevant clinical protocol, payer policy, or medication-interaction reference and presents it to the clinician for their decision is augmentation. A system that auto-prescribes, auto-orders tests, or makes a diagnosis without clinician review pushes into SaMD territory. We design the UI surface (clinician confirmation on every retrieved suggestion, explicit non-diagnostic framing, audit trail of clinician action) and the retrieval scope (clinical references rather than patient-specific predictions) to stay on the augmentation side for the systems we ship at Penn Medicine, Jefferson, and similar academic medical centers.