AI Agent Development · Philadelphia

AI agent development in Philadelphia.

The Philadelphia and Princeton-corridor economy concentrates in three of the most regulated industries in the country: pharma R&D and manufacturing (GSK in Collegeville, Merck across NJ-PA, Spark Therapeutics in University City), academic medicine (Penn, Jefferson, CHOP), and asset management (Vanguard in Malvern, SEI in Oaks, Lord Abbett in Jersey City). An agent that ships into any of these environments has to come with an audit and validation story that matches the regulatory frame: 21 CFR Part 11 for pharma, HIPAA for clinical, SR 11-7 for financial services.

We build agents that scaffold the regulated work without replacing the regulated decision. A deviation-handling agent at GSK assembles the CAPA package; the quality engineer signs off. A prior-auth agent at Penn Medicine runs the administrative steps; the clinical reviewer approves the submission. A transfer-agent automation bot at Vanguard handles routine address changes; an associate confirms the SOX-relevant moves.

Projects are scoped and fixed-price. Discovery includes a regulatory walkthrough with your quality, compliance, or model-risk team.

How we work

Tell us about the regulated workflow.

The regulated-industry ROI math.

A pharma quality engineer at $150,000 fully loaded spends 20 to 30 percent of their time on deviation and CAPA paperwork. That is $30,000 to $45,000 per engineer per year that an agent can recover by assembling the routine package, leaving the engineer to focus on the regulated decision. Across a 50-engineer quality organization the math compounds quickly.

A Penn Medicine prior-auth specialist spends similar time ratios on data assembly: pulling the payer policy, checking eligibility, formatting the submission. An agent that handles the administrative wrapper while the clinical reviewer makes the clinical judgment recovers most of that time. The math at Jefferson Health, CHOP, and the broader academic-medicine cluster is the same.

A Vanguard transfer-agent associate handles a high volume of routine requests. Even modest automation of the truly routine band (address changes, beneficiary updates, RMD processing) frees the associates for the cases that actually require judgment.

The architecture is conservative on purpose. Agent runtime on ECS Fargate or Azure Container Apps depending on whether your shop is AWS or Microsoft. State checkpointed in DynamoDB or Cosmos DB. Model layer through Bedrock Claude Sonnet 5 or Azure OpenAI under existing enterprise BAA for HIPAA workloads, or under DPA for non-PHI work.

Every regulated action writes an audit record compatible with 21 CFR Part 11 or the equivalent recordkeeping requirement for your industry. Records include the agent run ID, the human reviewer's identity, the input and output hashes, and the decision rationale. Records land in S3 Object Lock or Azure Blob immutable storage so they survive subsequent edits or deletions.

Approval gates are configurable per workflow. The regulated decisions (CAPA approval, prior-auth submission, SOX-relevant account changes) require human confirmation before the agent acts. The administrative wrapper steps (data assembly, prior-package retrieval, draft generation) run automatically.

What we build for Philadelphia teams.

Six components that show up in every regulated-industry agent we ship in the metro.

21 CFR Part 11 compatible audit trail

Every agent action writes an immutable record with agent run ID, reviewer identity, input and output hashes, timestamp, and decision rationale. Storage in S3 Object Lock or Azure Blob immutable.

BAA-covered model routing for PHI

Bedrock Claude Sonnet 5 under signed BAA or Azure OpenAI under Microsoft BAA for clinical workflows. Public model APIs never reached. PHI scrubbed from non-BAA observability sinks.

Configurable approval gates

Regulated decisions (CAPA approval, prior-auth submission, SOX-relevant moves) require human confirmation. Administrative wrapper steps run automatically. Configuration per workflow, not hard-coded.

QMS and EHR integration

Veeva QMS and TrackWise for pharma quality. Epic on FHIR and Cerner for clinical. DST, SS&C, and Iress for transfer-agency. Integration through documented APIs, never screen scraping.

Model-risk documentation pack

For SR 11-7 contexts, the agent ships with model inventory, workflow scope, validation benchmarks, drift monitoring, and rollback procedure. What an OCC examiner or your model-risk team asks for.

Eval harness as OQ documentation

Labeled historical case set, recall, accuracy, hallucination rate, and tool-call correctness in CI. Doubles as Operational Qualification documentation for non-SaMD validation packages.

Why this work fits Philadelphia.

The Philadelphia and Princeton-corridor economy is built around regulated work. Pharma manufacturing under 21 CFR 210/211. Clinical research under FDA and IRB oversight. Asset management under SEC and OCC regimes. Healthcare insurance under HIPAA and CMS. Defense engineering at Lockheed Martin Moorestown under CMMC and ITAR. The workforce knows these frameworks; what they need is the automation that respects them.

The pattern we see most often is a team with an agent prototype that does something useful in a notebook but cannot ship because the audit story, the approval gates, the validation documentation, and the SRE runbook are missing. We come in to harden the architecture into something an internal audit, model-risk, or quality leadership review can sign off on, while preserving the capability the prototype was built around.

We work remotely with Philadelphia, Princeton, and broader Mid-Atlantic clients on Eastern time. Discovery includes a regulatory walkthrough with your quality, compliance, or model-risk team. Code ships to repos you own with full IaC and validation documentation.

Industries where we see strongest fit: pharma quality and regulatory ops, clinical-trial operations, academic-medical-center revenue cycle and prior-auth, asset-management transfer-agency and compliance, healthcare-payer claims, and defense-adjacent engineering workflows.

Regulated-workflow questions from Philadelphia teams.

Can an agent handle pharma deviation and CAPA workflows under 21 CFR 210/211?: Yes, with the right approval-gate design. A deviation or CAPA agent at GSK or Merck does not make the regulated decision; it scaffolds the paperwork the quality engineer would otherwise spend hours assembling. The agent ingests the deviation report, pulls the relevant prior deviations and CAPAs from the QMS, drafts an initial impact assessment against batch records, and routes the package to the quality engineer with the supporting context attached. The engineer reviews, edits, and signs off through the existing QMS workflow. Every agent action writes an audit record compatible with 21 CFR Part 11. The eval harness against a labeled set of historical deviations doubles as Operational Qualification documentation. We have shipped this pattern against Veeva QMS and TrackWise environments.
How does the agent help a Penn Medicine prior-auth team without creating clinical risk?: The agent runs the administrative steps of prior authorization (eligibility verification through Change Healthcare or Availity, payer policy lookup, criteria match against the clinical documentation, automated submission of the prior-auth packet) but never makes the clinical decision. The clinical reviewer at Penn approves the proposed submission before it goes out. The agent surfaces the relevant payer policy text, the criteria checklist with current chart values, and the prior-auth history for the patient, so the reviewer's time goes to clinical judgment rather than data assembly. For HIPAA, the model layer routes through Bedrock Claude under signed BAA, with PHI scrubbed from any non-BAA observability sink. Audit logs preserve the reviewer identity and timestamp for every approval.
What does a Vanguard-scale transfer-agent automation agent actually do?: Mutual-fund transfer-agent operations involve high-volume routine work: address changes, beneficiary updates, fund-to-fund transfers, rollover requests, RMD processing. Most of this is clearly defined by the operating procedures and the IRS code, which makes it a good agent fit. The agent handles routine requests end-to-end (read the request, validate against account state, post the change, generate the confirmation) and routes anything outside the routine band to a human associate with the full context. For SOX-relevant changes (anything affecting account ownership or large monetary moves) the agent proposes and the associate confirms. We integrate with the major transfer-agency platforms (DST, SS&C, Iress) and respect the audit and segregation-of-duties requirements your operations team already operates under.
What about model risk management for agents in a regulated FinTech context?: OCC SR 11-7 and the equivalent guidance treat AI models used in financial-services decisions as model risk that has to be inventoried, validated, and periodically re-validated. For an agent in this context, we work with your model-risk team during discovery to inventory the LLM models used (Bedrock Claude Sonnet 5, GPT-5), document the workflow scope, and design the eval harness to support periodic re-validation. The agent ships with documentation covering data inputs, model selection rationale, performance benchmarks against the validation set, drift monitoring through the OpenTelemetry traces, and a rollback procedure. This documentation is what the model-risk team or an OCC examiner asks for when reviewing the system.

Ship the agent your auditors can sign off on.

Describe the workflow, the regulatory frame, and the systems the agent has to touch. We'll reply within one business day with a rough scope, a recommended architecture, and a price range.

AI agent development in Philadelphia.

Projects are scoped and fixed-price. Discovery includes a regulatory walkthrough with your quality, compliance, or model-risk team.

The regulated-industry ROI math.

What we build for Philadelphia teams.

Six components that show up in every regulated-industry agent we ship in the metro.

21 CFR Part 11 compatible audit trail

Every agent action writes an immutable record with agent run ID, reviewer identity, input and output hashes, timestamp, and decision rationale. Storage in S3 Object Lock or Azure Blob immutable.

BAA-covered model routing for PHI

Bedrock Claude Sonnet 5 under signed BAA or Azure OpenAI under Microsoft BAA for clinical workflows. Public model APIs never reached. PHI scrubbed from non-BAA observability sinks.

Configurable approval gates

Regulated decisions (CAPA approval, prior-auth submission, SOX-relevant moves) require human confirmation. Administrative wrapper steps run automatically. Configuration per workflow, not hard-coded.

QMS and EHR integration

Veeva QMS and TrackWise for pharma quality. Epic on FHIR and Cerner for clinical. DST, SS&C, and Iress for transfer-agency. Integration through documented APIs, never screen scraping.

Model-risk documentation pack

For SR 11-7 contexts, the agent ships with model inventory, workflow scope, validation benchmarks, drift monitoring, and rollback procedure. What an OCC examiner or your model-risk team asks for.

Eval harness as OQ documentation

Labeled historical case set, recall, accuracy, hallucination rate, and tool-call correctness in CI. Doubles as Operational Qualification documentation for non-SaMD validation packages.

Why this work fits Philadelphia.

Regulated-workflow questions from Philadelphia teams.

Can an agent handle pharma deviation and CAPA workflows under 21 CFR 210/211?

Yes, with the right approval-gate design. A deviation or CAPA agent at GSK or Merck does not make the regulated decision; it scaffolds the paperwork the quality engineer would otherwise spend hours assembling. The agent ingests the deviation report, pulls the relevant prior deviations and CAPAs from the QMS, drafts an initial impact assessment against batch records, and routes the package to the quality engineer with the supporting context attached. The engineer reviews, edits, and signs off through the existing QMS workflow. Every agent action writes an audit record compatible with 21 CFR Part 11. The eval harness against a labeled set of historical deviations doubles as Operational Qualification documentation. We have shipped this pattern against Veeva QMS and TrackWise environments.

How does the agent help a Penn Medicine prior-auth team without creating clinical risk?

The agent runs the administrative steps of prior authorization (eligibility verification through Change Healthcare or Availity, payer policy lookup, criteria match against the clinical documentation, automated submission of the prior-auth packet) but never makes the clinical decision. The clinical reviewer at Penn approves the proposed submission before it goes out. The agent surfaces the relevant payer policy text, the criteria checklist with current chart values, and the prior-auth history for the patient, so the reviewer's time goes to clinical judgment rather than data assembly. For HIPAA, the model layer routes through Bedrock Claude under signed BAA, with PHI scrubbed from any non-BAA observability sink. Audit logs preserve the reviewer identity and timestamp for every approval.

What does a Vanguard-scale transfer-agent automation agent actually do?

Mutual-fund transfer-agent operations involve high-volume routine work: address changes, beneficiary updates, fund-to-fund transfers, rollover requests, RMD processing. Most of this is clearly defined by the operating procedures and the IRS code, which makes it a good agent fit. The agent handles routine requests end-to-end (read the request, validate against account state, post the change, generate the confirmation) and routes anything outside the routine band to a human associate with the full context. For SOX-relevant changes (anything affecting account ownership or large monetary moves) the agent proposes and the associate confirms. We integrate with the major transfer-agency platforms (DST, SS&C, Iress) and respect the audit and segregation-of-duties requirements your operations team already operates under.

What about model risk management for agents in a regulated FinTech context?

OCC SR 11-7 and the equivalent guidance treat AI models used in financial-services decisions as model risk that has to be inventoried, validated, and periodically re-validated. For an agent in this context, we work with your model-risk team during discovery to inventory the LLM models used (Bedrock Claude Sonnet 5, GPT-5), document the workflow scope, and design the eval harness to support periodic re-validation. The agent ships with documentation covering data inputs, model selection rationale, performance benchmarks against the validation set, drift monitoring through the OpenTelemetry traces, and a rollback procedure. This documentation is what the model-risk team or an OCC examiner asks for when reviewing the system.

Ship the agent your auditors can sign off on.

Describe the workflow, the regulatory frame, and the systems the agent has to touch. We'll reply within one business day with a rough scope, a recommended architecture, and a price range.