The Data Valley of Death

Every biotech faces the same existential crisis. You spend years in R&D using flexible tools—ELNs, spreadsheets, whatever gets the science done. Your team moves fast. Experiments are documented, but loosely. The priority is discovery, not compliance.

Then you nominate a candidate and hit the GMP wall.

Suddenly, your flexible data is useless. You can't validate Excel. You can't release batches from an R&D notebook. So you buy a MasterControl or Veeva, hire consultants, and spend 12-18 months migrating data into rigid templates that don't quite fit your process—at a cost of $500K to $2M depending on complexity. The consultants leave. Your team is now maintaining two systems that don't talk to each other. Your IND timeline just slipped by six months.

The result is two silos. Your discovery data stays in the past—archived, unsearchable, disconnected. Your clinical data lives in a compliance box designed for Big Pharma, not a 30-person biotech burning runway. When you need to investigate a Phase 1 deviation by looking at original process development parameters, you're digging through PDFs and asking "does anyone remember why we chose this feed strategy?"

This is the valley of death. Not the funding gap—the data gap. The place where institutional knowledge goes to die because your systems weren't designed to grow with you.

One Platform, Three Modes

Seal spans the entire drug development lifecycle on a single data model. The same platform handles discovery, development, and manufacturing—but the rules change based on where you are.

In discovery, you get flexibility. Capture experiments with structured schemas, but don't enforce rigid workflows. Track your plasmids, cell lines, and reagents with lot numbers and expiry dates, but without the overhead of full GMP inventory. Share live data across teams instantly. The goal is speed and collaboration, not audit trails.

In process development, structure emerges. You define Critical Process Parameters and Critical Quality Attributes directly in the system. You model scale-up from 2L to 200L to 2000L, linking small-scale experiments to predicted clinical performance. The process definition you build here isn't a document—it's a living object that will eventually drive manufacturing.

In GMP manufacturing, control tightens. The same process definition becomes an electronic batch record with full Part 11 compliance. Electronic signatures, audit trails, review by exception. QA reviews deviations and outliers, not every data point. The Certificate of Analysis builds itself as testing completes.

The key insight is that "tech transfer" isn't a document handoff. It's a digital promotion. The parameters you defined in PD are the same objects that constrain execution in GMP. When something goes wrong in manufacturing, you can trace back to the original experiments that established those parameters—because it's all in one system.

The Sample Black Hole

Most biotechs struggle with sample tracking. Samples are generated during manufacturing, sent to QC, shipped to a CRO for specialty testing, and somewhere in the gaps, the chain of custody breaks. Which freezer? How many freeze-thaw cycles? Did the CRO results ever get linked back to the batch?

Seal maintains unbroken chain of custody from generation to final disposition. Samples are accessioned automatically from the batch record. Location, freeze-thaw cycles, and shipment status are tracked continuously. When QC results come back—whether from internal testing or external CROs—they link automatically to the sample, which links to the batch, which links to the process, which links to the original development experiments.

This isn't just good practice. It's what makes your IND defensible.

Quality That Prevents, Not Documents

Most QMS implementations are archaeological records of things that went wrong. Deviations happen, someone writes them up in a separate system, QA investigates, a CAPA gets filed, and maybe—months later—something changes.

Seal integrates quality into execution. Operators flag deviations directly inside the batch record, and the context captures automatically: which step, what values, who was logged in, what time. The deviation record links to the exact point of failure. When you implement a CAPA, you can verify it actually worked by looking at subsequent batch data—because the CAPA links to the process change, which links to the batches that ran after.

Training works the same way. The system prevents untrained operators from executing critical steps. Training isn't a spreadsheet that QA checks manually—it's a permission gate enforced by the platform.

IND Velocity

The speed of your IND filing is determined by data integrity. If you spend weeks manually collating data from three systems and verifying every copy-paste, your filing drags on. If your regulatory team is rebuilding traceability matrices from scratch, you're burning time and money.

With Seal, your IND data room builds itself. Click any final product lot and see the full lineage: which batch, which process version, which raw materials, which cell bank, which original PD experiments established the parameters. The traceability isn't reconstructed—it was captured as work happened.

All GxP modules are pre-validated. You focus on process validation, not system validation. Data is ALCOA+ by design—attributable, legible, contemporaneous, original, accurate. No shadow spreadsheets. No "which version is the real one?" Your regulatory team can pull submission-ready data packages without chasing down scientists.

The Virtual Biotech

Most modern biotechs don't own steel tanks. You design the process, but a CDMO runs the batches. This model works until you realize you're dependent on their paper records, their timelines, their PDF reports that arrive weeks after the batch finished.

Seal lets you operate as a virtual plant manager. Give your CDMO a secure portal to enter batch data directly into your system. Monitor progression in real-time—don't wait for the weekly status call. When deviations happen, you see them immediately, not in a summary report.

Most importantly: you own the data. The full manufacturing history lives in your system, not theirs. When you switch CDMOs—and you will—your process knowledge comes with you. The institutional memory stays with the asset, not the contractor.

AI That Builds Your Platform

The traditional biotech software implementation: six months with consultants, hundreds of hours configuring workflows, endless template creation. AI changes this completely.

Describe what you need: "We're developing a monoclonal antibody. We need to track cell line development, upstream and downstream process development, and analytical method qualification." AI generates the configuration—entity types for your cell banks, protocols for your unit operations, templates for your analytical methods. Days instead of months.

Batch record templates build conversationally. "Create a batch record for CHO fed-batch production with glucose feeding, daily sampling, and harvest criteria." AI generates the record structure with appropriate parameters, calculations, and acceptance criteria. You review, refine, approve through your standard change control.

Every AI proposal is transparent. When AI drafts a protocol or configures a workflow, you see exactly what it created. You edit and approve. Configuration changes go through review. New entities require approval. AI accelerates the work; your team controls the system.

And AI works throughout operations. In discovery, AI identifies patterns across experiments—which conditions correlate with success. In process development, AI suggests scale-up parameters based on historical data. For IND preparation, AI drafts CMC sections from your structured manufacturing data—you review rather than write from scratch.

Batch record review accelerates. AI highlights anomalies—parameters outside historical ranges, timing deviations. QA reviews exceptions rather than every data point. The AI that built your batch records now helps you review them.

Getting Started

You don't need to rip and replace anything. Most biotechs start with ELN and Inventory—the tools their scientists use every day. That's live in a day or two.

When you're ready to add structure, you turn on process development templates. When you nominate a candidate and need GMP, you enable batch records and QMS. Each step is incremental. No migration project, no consultants, no 18-month timeline.

The platform grows with you because your data is already in the system. The experiments you ran in R&D link directly to the processes you'll run in GMP. That's not possible when discovery and manufacturing live in different systems.