Validated systems, fewer people rekeying between them.

Clinical operations, regulatory submissions, medical affairs and commercial teams each run on a validated platform, and the handoffs between them are manual. The work between systems is where the hours, the errors and the compliance risk sit.

Colleagues in a meeting

Where it starts, and what changes.

Read the "before" and find yours. Most pharmaceuticals and life sciences teams start here and find that half of what they assumed needed AI didn't.

Below L1 → L1

Study start-up, mapped

Before

Site activation runs on three parallel trackers doing the same job, and the real sequence exists only in the CRA's head.

After

One tracker, one owner per site, one written sequence. Everyone knows what's missing and who's on it.

L1 → L2

Regulatory document assembly

Before

Submission components are collected and version-checked by hand from four systems, over weeks.

After

Deterministic assembly and version control from the systems of record. A missing or outdated document halts the build loudly.

The next two levels for pharmaceuticals and life sciences, and what we typically see
L2 → L3

Medical inquiry drafting

Before

Inbound inquiries wait days for a reviewer to search approved content and draft from scratch.

After

An agent reads the inquiry, drafts from approved content, and routes to a medical reviewer: read-only first, drafting once it agrees with reviewers.

L3 → L4

Deviation pattern review

Before

Deviations are closed one at a time; the same root cause is written up across sites without anyone seeing it.

After

Recurring causes surface monthly across sites. Quality adjusts controls, logs the change, and repeat deviations fall.

4 to 7
validated systems a single study's data passes through by hand
25 to 40%
of clinical ops effort commonly spent reconciling the same information across trackers
Weeks
saved per submission cycle when document assembly is deterministic

KPIs that move, and who notices.

Baseline these before anything changes. The Survey instruments them; the Pilot is measured against them.

MetricHow to measure itWho it moves
Site activation timeDays from site selection to first patient inClinical ops
Submission assembly timeHours per submission spent assembling and checkingRegulatory
Inquiry response timeDays from medical inquiry to approved responseMedical affairs
Repeat deviationsDeviations sharing a root cause with a prior oneQuality
What you get when it's built this way

Validated stays validated

We build on your platforms and inside your change control.

CRAs back at sites

Reconciliation work leaves the week.

Compliance evidence as output

The system produces the trail and the attribution as it runs, so nothing is assembled for the audit.

Systems pharmaceuticals and life sciences teams usually run on and where we've built.

We start with what you own. None of these is a partnership we take margin on.

Questions pharmaceuticals and life sciences teams ask before the first call.

Where should a pharmaceuticals and life sciences team start with automation?

With study start-up, mapped. It's the step that returns hours before any software is bought, and it makes everything after it cheaper. The assessment tells you whether your process is already past that point.

Do we need AI to improve site activation to first patient?

Usually not at first. Most of the delay in site activation to first patient comes from manual hops between systems, which deterministic rules remove. An agent earns its place only where the inputs vary, and we start it read-only.

Which systems do you build on in pharmaceuticals and life sciences?

The ones you already run. We start with capability you license and nobody switched on, and we take no margin on anything we recommend. If a different tool would remove a real roadblock, you get the case and the switching cost in writing.

Thirty minutes. Worst case, you lose half an hour.

Bring one process. You leave knowing which level it's on, what to fix first, and whether it's worth doing anything at all.