ClinOps · CRAs
Monitoring reports and site letters
Checked against your monitoring plan and templates before they enter the record.
- Deviations, visit windows, action items
- Letters cross-checked against the visit report
- Fewer review cycles per report
Attune Clinical · by Attune Technology
Work you can trust. Answers you can explain.
Defensible AI for clinical research. Attune separates what a model proposes from what your rules decide, so every AI-assisted result carries its evidence, its rule version and a record you can replay.
ONE EXAMPLE · NOT CUSTOMER DATA
CheckFollow-up letter cites every open action item
RuleSITE-CORR-004 · v3 · approved by your team
ResultFAILAction item 2 has no response date
Evidenceletter §2 ↔ visit report §5.1
THE PROBLEM
AI promised to save time. Then it became one more thing to check.
Language models are probabilistic by nature. They read your documents like a capable new hire, but they don't learn your judgment, can't show why they answered, and may not give the same answer twice. In GxP work, an output you can't reconstruct is an output you can't defend.
THE ANSWER
Your judgment, turned into code. AI only where it belongs.
AI prepares the work. Your judgment and obligations, encoded as executable rules, make the decision. Every result is replayable. You see the facts used and the rule applied, linked to the source, so you can explain every decision and improve it.
A worked example
Caught before sign-off.
A monitoring visit report is checked against your rules before it enters the record. What's missing or inconsistent is flagged with its rule and evidence, linked to the source. Only clean work reaches the record, and your reviewers start from the items that need a person.
One example. The same approach applies to the workflows below.
ONE EXAMPLE · NOT CUSTOMER DATA
Monitoring visit report, checked against MVR-011 v2
DeviationsPASS
Each noted deviation is in the deviation log
Visit windowFAIL
Visit 4 fell outside the protocol window; the report doesn't record it as a deviation
Safety follow-upINSUFFICIENT
The source page for the follow-up is not in the record
Held from the record until each item is resolved, with the rule and evidence attached.
How it works
Three steps, in this order.
1
AI proposes the rules. You approve them.
Attune reads your SOPs, plans and templates and proposes versioned, executable rules. Your experts review and approve each one; nothing runs until they do.
2
AI drafts. Rules decide.
The model extracts and proposes. A deterministic rule returns Pass, Fail or Insufficient, with the rule version and the evidence attached.
3
People handle exceptions.
Work is checked before it enters the record, so your reviewers' time goes to the flagged items and the hard calls.
01
Your expertise, at scale
Your best people can't review every case. Their judgment can.
02
Consistency
Same facts, same decision, across teams and over time.
03
Control
Your judgment changes when you decide, not when a model update does.
04
Proof
Every AI-assisted workflow has receipts: facts, rules and outcome, each traceable to its source.
More than rules
Infrastructure for AI you can defend.
Deterministic rules are the base layer: provenance for every fact, judgment expressed as versioned code, and an append-only record that can be replayed. That infrastructure lets probabilistic models take on more of a regulated process without losing control of it. Every workflow we build inherits the same controls, from drafting and review to monitoring and multi-step agents.
Drafting to your standard
Reports, letters and narratives drafted by AI and checked before review.
Document intelligence
Find, extract and cite, with every answer linked to its source passage.
Continuous monitoring
Records and AI outputs checked as work happens.
Guarded agents
Multi-step work where each step passes a rule before the next one runs.
What we build with you
New workflows for the problems that matter most to your team.
Attune Core Platform
- Facts traced to source
- Judgment as versioned code
- Replayable record
- Model-neutral
Where teams start
The work that costs you the most review time.
ClinOps · TMF owners
TMF quality checks
Documents checked against your TMF plan and filing rules before QC, alongside the system of record you already use.
- Completeness, placement and metadata
- Missing or late documents flagged early
- Vault keeps the documents; Attune checks the content
Clinical QA · Compliance
Continuous monitoring
Your rules keep checking records and AI outputs as work happens, in line with risk-based quality management.
- Drift and gaps flagged as they appear
- Mock inspections on demand*
- Audit evidence built as you go
Each one runs on the same foundation, so the next workflow starts with the controls already in place.
* A mock inspection is an internal exercise, not a regulatory inspection or a prediction of its outcome.
Why it holds up
Built for regulated work from the first line.
A model can draft. It cannot decide. Models propose; deterministic rules decide. Rules are fixed before the decision, every change is versioned, and the same approved rule on the same recorded evidence returns the same result, so any decision can be reconstructed.
What can the model do without a check?
Nothing reaches the record unless a rule admits it. An instruction in a prompt is not a control.
Can a past result be reproduced?
Input, rule version and model version are kept together in an append-only record.
What happens when the model changes?
Model versions are pinned. A change is re-tested before it goes live, under your change control.
What was measured, against what?
Performance is benchmarked against inter-reviewer agreement, so a score can be read at face value.
Designed around ICH E6(R3), ALCOA+, 21 CFR Part 11 and Annex 11, GAMP 5 and CSA, the FDA and EMA Guiding Principles of Good AI Practice in Drug Development, and FDA's draft guidance on AI credibility.
FDA's AI credibility guidance (FDA-2024-D-4689, January 2025) is a nonbinding draft. We build to its proposed framework and make no claim of FDA compliance. The regulatory determination stays with the sponsor.
How to start
How we work with you.
01
Credibility Assessment
Find out whether one AI system, in one context of use (what it does and which decision it feeds), would hold up before a regulator asks, and where it falls short.
02
Evidence-Package Build
We run the evidence plan and document it, with your rules applied to real work while your people check a sample. Your team decides whether the evidence is adequate.
03
One workflow, then the next
Rules and AI take on the workflow where errors cost the most time, under your change control. Your people handle the exceptions and the hard calls, then we move to the next workflow on the same platform.
Regulatory determinations are yours; technical findings are ours. Attune supports your team's evidence and does not guarantee any FDA or EMA outcome.
Questions
Straight answers.
Where do the rules live?+
In writing, with you. Rules are versioned and human-readable, and every result carries the rule version that produced it.
What if the AI is wrong?+
The AI drafts, the rule decides and a person acts. Every result shows the evidence it used, so extraction can be checked. Missing evidence returns Insufficient, never a quiet Pass.
Do we have to write the rules?+
No. Rule creation is governed: Attune reads your documentation and proposes the rules, and your experts review, edit and approve each one before it runs. When an SOP or plan changes, Attune proposes the updates through the same review, so your team approves rather than authors or maintains.
Is Attune a rules engine?+
Rules are the foundation. On top of them we design AI workflows for drafting, review, search and monitoring, and build new ones with your team.
Does patient data leave our systems?+
The platform is designed so patient data stays in your environment. It can run there or connect through an API.
Does this replace our reviewers?+
No. Your reviewers get cleaner work and a full record, and your people decide what's adequate.
What happens when the model changes?+
Each result records the model version that produced it. A model change is a change control event, re-tested before it goes live. Attune works with the model you choose.
We already use Veeva.+
Attune works alongside your system of record. Vault keeps your documents and versions; Attune checks their content.
We don't run AI yet.+
The Assessment works for a planned use too. We define the use and the evidence it needs before you build.
What do we need to give you?+
For an Assessment: access to the people who own the AI system and its documentation. For a workflow: the SOP or plan it follows, the templates and a set of sample documents.
What does it cost?+
Each engagement has a fixed scope, quoted in writing after a scoping call.
Book a call
Pick one workflow. We'll show you where AI can make it faster, more accurate and easier to defend.
We'll set up a 30-minute call. We'll ask about your workflow. Together we'll pick one use and the right way in, and you'll get a written scope and quote before anything starts.
Or write to hello@attunetechnology.com