Built natively on Cloudbyz eClinical with Salesforce Agentforce
Every clinical trial begins with a protocol that reads like a promise: this is exactly how the study will run. The Schedule of Assessments spells out which procedures happen at which visit, inside which windows, in which order. On paper, it is airtight.
Then the study meets the real world. A subject travels and misses a window. A newly activated site interprets an eligibility criterion the way the last three sites already got wrong. A lab draw happens a day late, an assessment happens out of sequence, a safety follow-up slips. Individually, none of these looks catastrophic. In aggregate, they become the single most common category of finding in regulatory inspections — and they almost always surface after the fact, during monitoring, source data review, or worst of all, during the inspection itself.
The uncomfortable truth is that most protocol compliance work is reactive by design. The protocol sits as a static PDF. Study teams are expected to have memorized it, cross-referenced it, and applied it perfectly across dozens of sites and thousands of data points. When something goes wrong, a human notices weeks later, opens a deviation, and documents the damage.
The Cloudbyz Protocol Intelligence Agent is built to close that gap — to turn a static protocol into live guidance for every site, and to move compliance upstream, before a risk becomes a deviation.
At its core, the agent reads the Schedule of Assessments and continuously analyzes operational data to surface compliance risks proactively. It is deliberately scoped: insights and alerts only. It does not act on records, it does not close deviations, and it does not overwrite anyone's judgment. It watches, reasons, and warns — so that people can act while there is still time to act.
Because it is built natively on the Cloudbyz eClinical platform and orchestrated through Salesforce Agentforce, it isn't a bolt-on assistant looking at a copy of your data. It lives where your CTMS, EDC, and eTMF data already live, which means it reasons over the same protocol, the same visits, and the same operational signals your team works from every day.
Here is what that looks like in practice.
Ask it whether a specific procedure is required at an unscheduled visit, or what the allowable window is for a particular assessment, and it answers in plain English, grounded in the actual protocol document. Crucially, every answer comes with citations, so a coordinator or CRA can verify the source rather than trusting a black box. The protocol stops being a document you hunt through and becomes a colleague you can ask.
A single late visit is noise. The same late visit happening at four sites, always around the same procedure, is a signal. The agent aggregates across sites, visit types, and deviation categories to detect the patterns a human staring at one site at a time would never see. That is the difference between fixing one incident and fixing the root cause behind twenty of them.
Newly activated sites inherit none of the hard-won lessons the rest of the study has already learned. The agent changes that. When a category of risk has already appeared at experienced sites, it proactively warns newer sites before they repeat it. Institutional memory, distributed automatically to the sites that need it most.
This is the headline capability. Out-of-window visits are one of the most frequent and most preventable deviation types in clinical research. The agent watches the schedule against the protocol's windows and flags visits that are drifting toward the edge — while there is still an opportunity to reschedule, escalate, or document intent, rather than after the window has already closed.
Before a monitoring visit, the agent assembles a risk briefing for the site, deliberately ordered so that safety findings come first. Instead of a CRA spending the first day rebuilding context from scratch, they arrive already knowing where the risk sits and where to focus their limited time on site.
The agent isn't a pure language model guessing at answers, and it isn't a brittle rules engine either. It combines structured rules — the hard logic of windows, required procedures, and sequencing — with grounded retrieval from the protocol itself. That hybrid design is what lets it be both precise about the rules and fluent about the reasoning behind them.
Features are only interesting because of the outcomes they produce. The Protocol Intelligence Agent is built to deliver four of them.
It moves compliance upstream. The entire premise is intervention before a risk becomes a deviation. Every alert delivered early is a deviation that never has to be written, investigated, and defended.
It protects patient safety. By prioritizing safety findings and visit timing, the agent keeps the most consequential risks at the top of the queue. In clinical research, timing isn't administrative — a missed or delayed safety assessment is a patient-safety event, and the agent treats it that way.
It improves audit readiness. When deviations do occur, the agent helps confirm they've been documented, so the study is defensible when an inspector arrives. Audit readiness stops being a frantic pre-inspection sprint and becomes a steady-state property of how the study runs.
It delivers live guidance for every site. The protocol stops being a static reference that lives on a shared drive and becomes an always-on layer of guidance that every site can draw on in real time.
In a regulated environment, autonomy is not a virtue — accountability is. That is why the agent's boundaries are a feature, not a limitation.
The Protocol Intelligence Agent produces insights and alerts only. It never edits deviation records. A qualified human always decides what a signal means and what to do about it. The agent's job is to make sure that human is never surprised, never under-informed, and never learning about a preventable risk too late to act. Everything it surfaces is traceable, cited, and reviewable — which is exactly the posture ICH E6(R3)'s risk-based, proactive quality management expects of the tools supporting a modern trial.
This is the model that makes AI adoptable in clinical operations: the machine widens the field of view, the human keeps the wheel.
The agent is purpose-built for the roles that carry protocol compliance on their shoulders:
What makes this different from a generic AI assistant pointed at clinical data is where it lives. Built natively on the Cloudbyz eClinical platform and powered by Salesforce Agentforce, the Protocol Intelligence Agent operates on your unified CTMS, EDC, and eTMF data without export gymnastics, brittle integrations, or a separate system of record to reconcile. It inherits Salesforce's security, governance, and audit trail, and it reasons over the same source of truth your teams already use.
For sponsors, CROs, and sites, that means the intelligence isn't sitting off to the side of your workflow. It's woven into it.
Protocol deviations have always been treated as an inevitability — something you catch, document, and defend after the fact. The Cloudbyz Protocol Intelligence Agent challenges that assumption. By reading the Schedule of Assessments, watching operational data continuously, and alerting the right people before a window closes, it turns compliance from a rear-view exercise into a forward-looking one.
The protocol was always meant to be the plan. Now it can be live guidance — for every site, every visit, every day of the study.
The Cloudbyz Protocol Intelligence Agent is part of the Cloudbyz portfolio of AI agents built natively on Cloudbyz eClinical with Salesforce Agentforce. To see it in action on your protocols, connect with the Cloudbyz team.