Hire an LLM Engineer, Philadelphia

Hire an LLM engineer in Philadelphia.

This is for the regulatory affairs associate at GSK in Upper Providence, AstraZeneca in Wilmington (25 miles from Philadelphia), or Incyte in Wilmington. Your team writes clinical study reports, regulatory responses, and safety updates. These documents follow ICH E6 format requirements with sections that have mandated language and sections that require accurate narrative from structured source data.

An LLM that generates first drafts from structured source data , clinical database outputs, statistical tables, patient narratives, reduces the initial drafting time from weeks to days. The regulatory writer reviews and corrects the draft, the same way they would review a junior writer's work. The critical requirement: the LLM must reproduce specific ICH E6 language exactly in required sections. We build the template injection layer that prevents this from being a generation problem.

How we work

Tell us which regulatory document type you need to draft and the structured data sources it pulls from.

The specific problem this solves.

A clinical study report for a Phase III trial has 14 required sections under ICH E6. Some sections are templated (ethics committee compliance, investigator listing), these are essentially unchanged from study to study. Some sections require accurate narrative generated from structured data (statistical results, safety summary, subject disposition). Some sections require judgment-intensive writing that benefits from human drafting (discussion of results in context of prior studies).

The LLM handles the second category: generating accurate narrative from structured source data. A statistical results section that currently takes a regulatory writer 3 to 4 days can be drafted in hours if the writer is reviewing and correcting an LLM-generated draft rather than writing from scratch.

The technical constraint for Philadelphia pharma is ICH E6 compliance. The model cannot paraphrase mandated language, it must reproduce it exactly. We solve this with template injection: verbatim required text is stored as verified constants and inserted directly into the draft at the appropriate section. The LLM generates only the variable content.

Numbers in the narrative come from the source tables, not from model inference. The generation step receives the table data as structured input and uses it to write the narrative. This prevents numeric transcription errors, which are the primary data integrity risk in LLM-generated regulatory text.

Components of the Philadelphia regulatory drafting pipeline.

Template injection for mandated language

ICH E6 required text sections are stored as verified constants and injected directly. The LLM never generates text for these sections. No paraphrase risk.

Structured data input parser

Reads clinical database output tables, statistical result files, and patient narrative summaries. Structures the data before the LLM generation step.

Section-level generation

Each ICH section is generated independently with its own prompt. Statistical results section generation differs from safety summary generation.

ICH E6 checklist review interface

Shows the generated draft alongside the applicable ICH checklist items. Reviewer marks items met or flags them. Correction instructions trigger regeneration.

Revision audit trail

Every generated draft version, correction instruction, and revision is logged. Mirrors the review workflow your regulatory team uses for junior writer review.

GxP validation package

IQ/OQ/PQ validation documentation for 21 CFR Part 11 compliance. Electronic signatures on document approvals. Included for GxP deployments.

How regulatory drafting engagements work.

We start with a completed CSR or regulatory response from a recently submitted study. This is the calibration target: the pipeline needs to produce output that matches your company's established style and format, which may differ from the bare ICH minimum. We annotate the document to identify which sections are templated, which are LLM-generated from structured data, and which require senior writer judgment.

The structured data sources are identified and the input schemas are defined before build starts. If the current data export from your clinical database does not include all the fields the generation step needs, we identify the gap before committing to a scope.

For GxP deployments, we begin the validation planning in parallel with the build. The validation approach is reviewed and approved before go-live. Build time: 8 to 14 weeks for a full CSR drafting pipeline with GxP validation.

Questions from Philadelphia regulatory affairs teams.

How do you make the LLM reproduce specific ICH E6 language exactly in required sections?: The ICH E6 required sections, such as the good clinical practice compliance statement and the investigator agreement text, have mandated language that cannot be paraphrased. We handle these with a template injection approach: the required verbatim text is stored as a constant and injected directly into the draft at the correct section, bypassing the LLM generation entirely for that section. The LLM generates the variable content (study description, subject population, endpoints) while the mandated language comes from verified templates. This eliminates hallucination risk for the sections where exact language is required.
What structured source data does the LLM need to generate a CSR narrative section?: For the statistical section of a CSR, the LLM needs: the analysis population definition, the primary and secondary endpoint names and statistical hypotheses, the analysis method (ANCOVA, logistic regression, etc.), the key results table (means, confidence intervals, p-values), and any pre-specified sensitivity analyses. These inputs come from your clinical database output or your statistician's table shells. The LLM generates the narrative description of what was tested and what was found, not the numbers themselves. Numbers are injected from the source tables to prevent transcription errors.
How does the regulatory writer review the LLM draft against an ICH checklist?: We build a review interface that shows the generated draft alongside the applicable ICH E6 checklist items for each section. The reviewer works through the checklist, marking each item as met or flagging it for revision. For flagged items, they can write correction instructions directly in the interface and trigger a regeneration of that section. The revision history is logged: what the original generated text was, what correction instruction was given, and what the revised text is. This creates an audit trail that mirrors the junior writer review workflow your team already uses.
What does validation look like for a GxP LLM system at GSK or Incyte?: For a GxP LLM system, the validation package includes IQ (installation qualification confirming the system is installed correctly), OQ (operational qualification showing the system produces outputs within defined specifications), and PQ (performance qualification demonstrating the system performs its intended function in your environment with your data). We document the validation approach in a Validation Plan before build starts and provide the completed Validation Summary Report as a deliverable. The system includes electronic signatures for document approval actions that fall under 21 CFR Part 11.

Ready to compress your regulatory drafting timeline?

Share a sample CSR section and the data source it was written from. We will assess the automation path and reply within two business days with a technical approach and scope.

Hire an LLM engineer in Philadelphia.

The specific problem this solves.

Components of the Philadelphia regulatory drafting pipeline.

Template injection for mandated language

ICH E6 required text sections are stored as verified constants and injected directly. The LLM never generates text for these sections. No paraphrase risk.

Structured data input parser

Reads clinical database output tables, statistical result files, and patient narrative summaries. Structures the data before the LLM generation step.

Section-level generation

Each ICH section is generated independently with its own prompt. Statistical results section generation differs from safety summary generation.

ICH E6 checklist review interface

Shows the generated draft alongside the applicable ICH checklist items. Reviewer marks items met or flags them. Correction instructions trigger regeneration.

Revision audit trail

Every generated draft version, correction instruction, and revision is logged. Mirrors the review workflow your regulatory team uses for junior writer review.

GxP validation package

IQ/OQ/PQ validation documentation for 21 CFR Part 11 compliance. Electronic signatures on document approvals. Included for GxP deployments.

How regulatory drafting engagements work.

Questions from Philadelphia regulatory affairs teams.

How do you make the LLM reproduce specific ICH E6 language exactly in required sections?

The ICH E6 required sections, such as the good clinical practice compliance statement and the investigator agreement text, have mandated language that cannot be paraphrased. We handle these with a template injection approach: the required verbatim text is stored as a constant and injected directly into the draft at the correct section, bypassing the LLM generation entirely for that section. The LLM generates the variable content (study description, subject population, endpoints) while the mandated language comes from verified templates. This eliminates hallucination risk for the sections where exact language is required.

What structured source data does the LLM need to generate a CSR narrative section?

For the statistical section of a CSR, the LLM needs: the analysis population definition, the primary and secondary endpoint names and statistical hypotheses, the analysis method (ANCOVA, logistic regression, etc.), the key results table (means, confidence intervals, p-values), and any pre-specified sensitivity analyses. These inputs come from your clinical database output or your statistician's table shells. The LLM generates the narrative description of what was tested and what was found, not the numbers themselves. Numbers are injected from the source tables to prevent transcription errors.

How does the regulatory writer review the LLM draft against an ICH checklist?

We build a review interface that shows the generated draft alongside the applicable ICH E6 checklist items for each section. The reviewer works through the checklist, marking each item as met or flagging it for revision. For flagged items, they can write correction instructions directly in the interface and trigger a regeneration of that section. The revision history is logged: what the original generated text was, what correction instruction was given, and what the revised text is. This creates an audit trail that mirrors the junior writer review workflow your team already uses.

What does validation look like for a GxP LLM system at GSK or Incyte?

For a GxP LLM system, the validation package includes IQ (installation qualification confirming the system is installed correctly), OQ (operational qualification showing the system produces outputs within defined specifications), and PQ (performance qualification demonstrating the system performs its intended function in your environment with your data). We document the validation approach in a Validation Plan before build starts and provide the completed Validation Summary Report as a deliverable. The system includes electronic signatures for document approval actions that fall under 21 CFR Part 11.

Ready to compress your regulatory drafting timeline?

Share a sample CSR section and the data source it was written from. We will assess the automation path and reply within two business days with a technical approach and scope.