Data Management
Every Question Becomes a Request
A medical monitor who wants a cut of the data raises a request, waits for a listing, then finds the answer prompts a second question.
AI Agent for Clinical Study Data
Query Study Data, Surface Gaps and Generate Insights With Ira AI.
Ask questions in plain language and get traceable findings from live EDC and CTMS data.
- Natural Language Querying
- Continuous RBM Scanning
- Protocol Deviation Detection
- One-Click Study Summaries
Ira WorkspaceRead-Only
Ask Ira
Plain-language query
Which sites have query backlog older than 14 days and missing visit forms this week?
Structured Answer
3 sites require attention. Highest risk: Pune-04, with 18 ageing queries and 6 expected forms missing.
Evidence Drill-Down
Result links to records
SiteRiskEvidence
Pune-04High18 queries
Mumbai-02Medium4 missing forms
Nagpur-01Low2 overdue visits
Source
EDC + CTMS
Audit
Logged
The Problem
The Answer Exists, Getting It Takes a Week.
Study data holds the answer to almost every operational question a team has. Reaching it usually means a listing request, a queue and a spreadsheet that is already ageing.
Monitoring
Signals Surface on Review Cycles
Missing forms, site outliers and data anomalies are found when someone looks. Between reviews, a pattern can run for weeks.
Reporting
Status Reports Are Rebuilt by Hand
Study status decks, CRA pre-visit summaries and medical review briefs are assembled manually from data the system already holds.
The Solution
An Analyst That Never Closes the Query Window.
Ira sits inside Clinera with read access to live EDC and CTMS data. Every answer opens to the records behind it. The agent surfaces findings; the study team decides what they mean.
01
Ask in Plain Language
Complex operational and clinical questions answered without a listing request or a programmer in the loop.
02
Runs Continuously
Anomaly and outlier detection scans the study database around the clock rather than at review points.
03
Checks Against Protocol
eCRF data cross-referenced against inclusion and exclusion criteria to flag non-compliance as it appears.
04
Traceable by Design
Each finding links to the subject, visit and form records it was derived from, so nothing has to be taken on trust.
How It Works
From Question to Verified Finding.
The same path applies whether a person asks a question or the agent raises a signal on its own.
Stage 01
Ask or Detect
A team member asks a question in plain language, or a continuous scan raises an anomaly, outlier or missing form.
Stage 02
Query Under Permission
Ira reads live EDC and CTMS data within the permissions of the person asking, including blinding restrictions.
Stage 03
Return With Evidence
The answer arrives as a structured result set that opens to the underlying subject, visit and form records.
Stage 04
Act or Report
The team acts on the finding, or exports it into a study status report, pre-visit summary or medical review brief.
Capabilities
Core Ira Features and Capabilities.
Four capability blocks cover conversational querying, continuous risk detection, protocol compliance checking and report generation.
Feature 01
Natural Language Data Querying
Ask the question you would have sent to a programmer, and get the answer while you are still thinking about it.
- Operational questions - Sites with a query backlog older than 14 days, subjects overdue for a visit or forms outstanding by site.
- Clinical questions - Subjects with Grade 3 adverse events missing an SAE report or con-med entries without a matching indication.
- Structured answers - Results return as tables and counts rather than prose, so they can be sorted, filtered and exported.
- Follow-up in context - The next question builds on the last result, which is how analysis actually works.
Conversational Query
Follow-up in Context
Show subjects with Grade 3 AEs that do not have a linked SAE form.
5 subjects found
SUB-018 - Site 03 - Open
SUB-026 - Site 04 - Open
SUB-041 - Site 07 - Review
Feature 02
Automated Risk-Based Monitoring Detection
Continuous scanning for the patterns a monthly review is designed to catch.
- Data collection anomalies - Values outside expected distributions, unusual entry timing and forms completed in patterns worth a second look.
- Missing visit forms - Expected forms not present against the visit schedule, identified per subject and per site.
- Site performance outliers - Sites diverging from the study on enrollment, data entry lag or query response, ranked by degree of deviation.
Risk Signals
Continuous Scan
Missing Visit Forms12 recordsHigh
Data Entry LagSite Pune-04Medium
Query Ageing18 over SLAHigh
Feature 03
Protocol Deviation and Compliance Gap Surfacing
Check what was recorded against what the protocol requires, continuously.
- Eligibility cross-check - eCRF data compared against inclusion and exclusion criteria, so a subject who should not have been enrolled is flagged during the study.
- Deviation flagging - Protocol non-compliance surfaced automatically, with the criterion and the conflicting data point named.
- Gap visibility - Compliance gaps ranked so the team addresses the ones that matter to the endpoint first.
Protocol Check
Eligibility Conflict
Criterion Mismatch
Inclusion criterion not met for one enrolled subject.
ProtocolHbA1c >= 7.0
Recorded Value6.4
SourceScreening eCRF
Feature 04
Executive Study Summary Generation
Produce recurring documents from the data instead of rebuilding them each cycle.
- Study status reports - Current enrollment, data completeness, query position and open risks, generated on demand.
- CRA pre-visit summaries - What has changed at a site since the last visit, prepared before the monitor travels.
- Medical review briefs - Safety and data review packs assembled for the monitor's session rather than the week before it.
- Markdown or PDF - One-click output in a format that can be reviewed, edited and filed.
Generated Summary
Status Report Draft
Week 18 Study Status
Draft76%
Enrollment
91%
Data Complete
07
Open Risks
Report includes source-data links for each risk, query position and site outlier.
Use Cases
Where Teams Put Ira AI to Work.
Four recurring jobs where the delay between question and answer costs the most.
Faster Safety Review
Medical Monitoring Review
Work through a safety review by asking successive questions of live data, instead of waiting on a listing that answers only the first one.
Focused Site Visits
Pre-Visit Preparation
A CRA arrives at a site knowing what changed since the last visit, what is outstanding and which subjects need source verification.
Cleaner Lock
Ongoing Data Cleaning
Missing forms and anomalies surface continuously, so cleaning happens across the study rather than in a scramble before database lock.
Current Reporting
Sponsor and Board Reporting
Study status packs generated from current data remove the lag between the number in the deck and the number in the system.
Workflow
Role-Based Workflow and Benefits.
Four roles ask different questions of the same study data, within the permissions their role allows.
RoleKey Capabilities in Ira AIKey Benefit
Data Manager
Query data completeness, find missing forms and anomalies, track query ageing across sites.
Cleaning runs continuously instead of concentrating before lock.
Medical Monitor
Interrogate safety data directly, generate medical review briefs and follow a line of questioning to its end.
Review depth is no longer limited by listing turnaround.
Clinical Project Lead
Read site performance outliers and study status without commissioning a report.
Status available on the day it is needed.
Quality Officer
Surface protocol deviations and compliance gaps against eligibility criteria.
Deviations found during the study rather than at close-out.
Why Ira AI
What Makes Ira AI Different.
01
Answers That Open Up
Every result links to the records it came from. An answer you cannot verify is not usable in a regulated study, whatever produced it.
02
Read Access Only
Ira reads study data and raises findings. It does not edit records, close queries or make decisions, which keeps accountability where a regulator expects to find it.
03
Inside the Platform
Because Ira sits on the same platform as the EDC and CTMS, there is no export step, no copy of the study database and no lag between the data and the question.
Specifications
Platform and Governance.
Ira AI runs inside the Clinera platform and operates within its security, audit and access-control model.
- Data Scope
- Live Clinera EDC and CTMS study data, read within the querying user's existing permissions.
- Access Mode
- Read-only. Ira does not create, edit or delete study records, and does not close queries.
- Detection Coverage
- Data collection anomalies, missing visit forms, site performance outliers and protocol eligibility conflicts.
- Output Formats
- Structured result sets in the interface, and generated summaries exported as markdown or PDF.
- Traceability
- Findings and report content link to the subject, visit and form records they were derived from.
- Audit Logging
- Queries, generated outputs and the data accessed are logged against the user and timestamp.
FAQ
Frequently Asked Questions.
How Do We Know an Answer Is Correct, and What Stops the Agent Inventing a Number?+
Answers are produced by querying the study database rather than by the model recalling or estimating, and every result opens to the subject, visit and form records that produced it. Ira's output is a fast first pass that a human confirms against source.
Does Ira Respect Blinding and Role Permissions?+
Yes. Ira queries under the permissions of the person asking rather than with elevated access of its own, so a blinded role receives no treatment-revealing data through the agent. A question requiring unblinded data returns nothing for a blinded user rather than an approximation.
Can Ira Change Study Data or Close Queries by Itself?+
No. Ira has read access only. It surfaces findings and drafts documents; people act on them. This keeps accountability for a query raised or a record changed with a named person.
Does This Replace Our Risk-Based Monitoring Plan?+
No. ICH E6(R3) puts weight on quality by design, so the operational parameters of AI-assisted monitoring should be written into the monitoring plan rather than added mid-study. Ira gives your RBM approach continuous detection where it currently has periodic review, but the plan, thresholds and decisions stay yours.
How Is an AI Tool Validated in a GxP Environment, and What Happens When the Model Changes?+
Current guidance, including the ISPE GAMP AI guide, expects AI systems in GxP settings to carry validation documentation and audit records as rigorous as conventional computerised systems, plus the model version in use at inference, prompt-level logging and monitoring for behaviour drift after deployment. Nirmitee supplies that documentation and records model version against every generated output. Your quality team owns the validation decision.
Next Step
Schedule an Ira AI Demo.
Bring the three questions you most often send to data management. We will ask them of a live study on the call and open the records behind each answer.