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How a Pharma Team Cut Disclosure Prep to 10 Minutes
A pharma compliance team spent two weeks hand-building each state pricing disclosure. An RPA workflow now pulls data, runs calculations, and builds a filing package in 10 minutes.
2 wks → 10 min
Cut from a two-week prep cycle
4–8 weeks
Implementation Time
Under $25K
Project Cost
the challenge
The team had a recurring compliance disclosure process that required manual preparation of state-level pharmaceutical pricing forms.
Each product required multiple forms, with information pulled from internal trackers and supporting files, then converted into PDF and prepared for review before submission.
The work was repetitive, detail-heavy, and time-sensitive, with a high risk of delays or errors if someone had to manually copy data, generate documents, track status, and manage each state submission one by one.
The project was triggered by the need to reduce manual effort, improve consistency, and create a more controlled workflow for recurring disclosure filings.
what they built
RAM Solutions built an RPA-based workflow automation that prepared recurring pharmaceutical pricing disclosure packages for state-level filings.
The automation read the required product and filing information from controlled trackers, generated the needed disclosure forms, converted the outputs into PDF, organized the files for review, and helped track which products and states were completed or still pending.
The workflow was designed with a human review step before final submission, so the team kept control over compliance decisions while removing much of the repetitive document preparation, data entry, file handling, and status tracking work.
RAM Solutions started with process discovery, mapping the existing disclosure workflow, the required state forms, the product-level inputs, the source trackers, the PDF outputs, and the review and submission steps. The team scoped the first release to a focused set of states and forms instead of automating every variation at once. They then designed the workflow logic: how the automation would read the controlled tracker, prepare the required forms, generate PDFs, organize output files, update status, and flag items needing human review. A key decision was to keep compliance approval human-in-the-loop, so the bot handled preparation and tracking and final business review stayed with people. With the design approved, the team built and tested the RPA workflow against sample products and filings, adjusted for form and layout differences, added basic exception handling, and validated outputs with the business team. A final phase of pilot testing, production rollout, and handoff documentation made the process repeatable. A focused build of this kind typically takes four to six weeks.
best fit for
This type of workflow is best for regulated companies with recurring disclosure, filing, reporting, or document preparation requirements. The strongest fit is a pharmaceutical, life sciences, healthcare, finance, legal, or compliance-heavy organization where teams must prepare similar forms across multiple products, states, regions, business units, or filing periods.
It is especially useful when the process depends on product-level calculations, dosage or package details, pricing inputs, controlled trackers, PDF generation, review steps, and submission readiness. The best-fit team is usually Legal, Compliance, Regulatory Affairs, Finance Operations, or Business Operations - especially when the work is repeated often enough that manual preparation creates delays, rework, audit risk, or too much dependency on a few people.
Ai ROLE
The automation reads structured product and filing inputs from controlled trackers, applies the required calculation logic for each product, and uses those values to prepare the state-level disclosure forms. • It then generates the PDF outputs, organizes the files by product and state, updates the completion status, and flags the package for human compliance review before submission. In this case, the intelligence was mainly processing logic and rule-based automation rather than a generative AI model.
impact
2 weeks to 10 minutes
Reduced the preparation cycle for state-level pharmaceutical disclosure packages from roughly two weeks to about 10 minutes.
3 state workflows automated
The automation supported disclosure preparation for Connecticut, Vermont, and Colorado, each with its own state-level filing requirements.
Human review preserved
The workflow automated document preparation, calculations, PDF generation, and tracking while keeping compliance review and approval in human hands.
implementation complexity
The project started with process discovery: mapping the current disclosure workflow, the required state forms, the product-level inputs, the source trackers, the PDF outputs, and the review/submission steps. From there, the team defined the first automation scope around a focused set of states and forms rather than trying to automate every possible variation at once.
RAM Solutions then designed the workflow logic, including how the automation would read the controlled tracker, prepare the required forms, generate PDFs, organize output files, update status, and flag items that still needed human review. A key decision was to keep compliance approval human-in-the-loop, so the automation handled preparation and tracking but did not remove final business review.
After the design was approved, we built and tested the RPA workflow using sample products and filings, adjusted for form/layout differences, added basic exception handling, and validated the outputs with the business team. The final phase included pilot testing, production rollout, and handoff documentation so the process could be repeated consistently.
A focused version of this type of workflow can typically be implemented in about 4-6 weeks, depending on the number of states, forms, products, validation rules, and submission requirements included in scope.
Will Sanchez
AI and automation consultant at RAM Solutions, implementing Microsoft 365 Copilot, Copilot Studio and workflow automation across sales, HR, finance and operations teams.
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Talk to this teamindustry
Healthcare & Life Sciences
business organization
Legal & Compliance
AI TYpe
Process Automation (RPA + AI)
value type
Risk & Compliance
more from this expert
frequently asked questions
How did a large pharmaceutical company cut compliance disclosure prep from two weeks to minutes?
The experts built an RPA workflow that automates preparation of state-level pharmaceutical pricing disclosure packages. It reads product and filing data from controlled trackers, applies each product's calculation logic, and generates the disclosure forms and PDFs. That compressed a roughly two-week manual cycle to about 10 minutes while keeping compliance review in human hands.
What AI and automation tools were used for the disclosure workflow?
The solution centers on process automation (RPA) using Microsoft Power Automate Desktop, with Excel-based control trackers, PDF and form generation, Microsoft 365 file storage, and browser-based web-portal automation. The intelligence is mainly rule-based processing and product-level calculation logic rather than a generative AI model.
What results did the compliance team achieve?
Preparation of state-level disclosure packages dropped from roughly two weeks to about 10 minutes. The workflow automated filings across three states, Connecticut, Vermont, and Colorado, while preserving human compliance review and approval before every submission.
How long did the automation take to implement?
First meaningful results appeared within about four to eight weeks. A focused version of this kind of workflow can typically be built in four to six weeks, depending on the number of states, forms, products, and validation rules in scope.
Who is this compliance automation approach best for?
Regulated organizations with recurring disclosure, filing, or reporting requirements, especially pharmaceutical, life sciences, healthcare, finance, and legal teams that prepare similar forms across many products, states, or filing periods. It fits best where the work depends on product-level calculations, controlled trackers, and repeatable PDF generation.
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