How SpeciGen uses AI to speed up clinical trials.
Faster study setup. Continuous reconciliation. Earlier risk detection. Less manual work. Where AI-native and agentic AI actually change the speed, cost, and accuracy of running a trial — and where deterministic rules and human judgment still lead.
SpeciGen is the AI-native biospecimen operations platform for clinical trials, using agentic AI to accelerate study setup, continuous cross-vendor reconciliation, operational oversight, and submission-ready eTMF closeout. Sponsors, CROs, and academic PIs use it standalone or across their existing clinical stack. AI accelerates the work, humans retain control, and a deterministic core maintains the study record. That’s how SpeciGen out-performs manual processes and legacy clinical vendors alike — delivering more accurate trials, more aggressive and timely tracking, and a materially lower operational cost.
The idea is simple. Let AI handle the expensive, repetitive operational work at machine speed. Let study teams stay in control of the decisions that matter.
- AI proposes. Reads, drafts, suggests.
- Humans approve. Judge, accept, sign.
- SpeciGen records. Deterministic state.
Biospecimen operations are full of that expensive, repetitive work. Before the first sample is collected, teams translate protocols, schedules of activities, and lab manuals into operational configurations. Once the study starts, they reconcile what should have been collected against what sites, central labs, and specialty labs actually report; investigate discrepancies; monitor protocol windows; track shipments and aliquots; and prepare operational reports. When the protocol changes, much of that work happens again.
SpeciGen uses AI to compress that workload — here’s where.
Start the study faster.
The protocol already describes the study. The Schedule of Activities defines visits and collections. Lab manuals contain specimen requirements. Why rebuild all of that by hand in another system?
SpeciGen’s AI interprets those documents and proposes visits, cohorts, specimen schedules, collection requirements, windows, and other operational rules. The study team reviews, changes what needs changing, and approves.
Instead of weeks of translation and configuration, the team starts with a structured draft grounded in the documents they already created.
Reconcile continuously, not months later.
Once specimens start moving, another expensive manual process begins. The EDC says a visit occurred. The site records a collection. The central lab receives the specimen. Aliquots move to PK, ADA, or biomarker laboratories. Each system reports its piece of the story, often at different times and under different identifiers.
SpeciGen brings those signals together and continuously compares what should have happened with what actually happened. AI normalizes incoming data, clusters anomalies, and helps make sense of discrepancies. Protocol-aware deterministic rules drive the reconciliation itself.
Teams see missing specimens, mismatched identifiers, unexpected aliquots, off-window collections, and other inconsistencies without repeatedly assembling and comparing spreadsheets.
Find problems while you can still fix them.
A missing PK sample discovered three months later is a reconciliation finding. The same sample surfaced today might still be sitting at the site, recorded under the wrong visit, shipped under another identifier, or waiting for transfer to a downstream lab.
Earlier means more options. SpeciGen continuously watches for operational and protocol-related signals across the specimen lifecycle and surfaces them for human review. The AI isn’t deciding whether a study is compliant — it’s helping the team continuously ask a single question:
Amend the study without rebuilding it.
Then the protocol changes.
Instead of treating every amendment as another configuration project, SpeciGen compares the amended documentation against the current configuration and proposes what changed — a visit, a collection, a window, a cohort, a specimen requirement. The team reviews and approves while SpeciGen preserves the historical context.
A specimen collected correctly under an earlier protocol version shouldn’t suddenly become an exception because a later amendment changed the rules. The system remembers which rules applied when.
Ask the question you need answered today.
Operational questions change throughout a trial.
- Which sites have the most unresolved specimen discrepancies?
- Which expected PK samples from completed visits haven’t appeared at the lab?
- Where are we seeing repeated collection-window issues?
Through SKIA (SpeciGen Knows It All), teams ask questions in plain English against their operational study data — no waiting for someone to build another report or dashboard. That’s another part of being AI-native: the software doesn’t have to anticipate every question before the study begins.
The economics are just as important.
Sponsors already pay for this work — through internal operations teams, CRO resources, laboratory services, data management, analysts, custom pipelines, BI development, and the time spent chasing discrepancies across all of them.
SpeciGen uses AI and automation to reduce that manual burden while helping teams identify problems faster and more consistently: faster setup, less manual reconciliation, earlier visibility into operational and compliance risks, faster amendments, fewer custom reports. And all of it works as an operational layer across the sites, laboratories, vendors, and clinical systems the sponsor already uses.
- AI reads, interprets, compares, finds patterns, eliminates repetitive work.
- Rules enforce study logic, reconciliation, and controlled state.
- People judge, approve, and stay accountable.