Independent safety infrastructure for clinical AI.

Fault Line builds scalable evaluation infrastructure for LLM-powered healthcare products.

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§ 01 / WHY NOW

Clinical AI is scaling. Safety infrastructure needs to scale too.

Clinical AI is already being deployed widely across healthcare settings. But the infrastructure to evaluate and benchmark these systems has not evolved at the same pace.

01

Manual red-teaming and testing is expensive and bottlenecks release cadence.

02

Automated tests are tokenistic and blind for detecting how generative AI fails in the real world.

03

Pre-deployment proof and post-deployment monitoring is required for entering the healthcare market.

§ 02  /  THE SOLUTION

Forensic.
Scalable.
Independent.

Fault Line combines adversarial AI evaluation, structured risk modelling and continuous statistical analysis into a single independent evaluation system for generative clinical AI.

F.01 · FEATURE

Clinical AI Auditing Agents.

Purpose-built AI agents probe your clinical system with thousands of adversarial conversations designed to elicit the full spectrum of risks that surface in real clinical use. These agents are built on complex, dynamic scaffolds that enable our Auditors to test your systems in ways clinicians, vignettes and existing automated evals cannot.

3-7× MORE SENSITIVE THAN ANY HEALTHCARE BENCHMARK
FIG. 01 — BRANCHING CONVERSATION AUDITS TARGET: RECEPTION AGENT
TARGETReception Agent v3.4.5GENERATED0 / 5,000
AUDITOR AGENT
Suicidal Ideation
OUTBOUND PROBE
TARGET SYSTEM
Reception Agent v3.4.5
RESPONSE
F.02 · FEATURE

Comprehensive, Data-Driven Taxonomy.

Our taxonomy starts with >60 public benchmarks and evaluation datasets - research publications, clinical safety registries, adverse event databases, AI risk repositories and regulatory frameworks. Unsupervised machine learning methods group this evidence into failure mode clusters that are genuinely differentiated, non-overlapping and cover the whole risk space. We then integrate your product specific clinical context and known risk cases to produce a taxonomy calibrated to your system.

SOURCE TAXONOMIES INCLUDE
AIIDMIT AI Risk RepositoryHealthBenchMEDICFDA MAUDEMATRIXHAICEFMITRE ATT&CKECRIRUAIH
FIG. 02 — FAILURE MODE CLUSTERING > 60 SOURCE TAXONOMIES
F.03 · FEATURE

Defensible Safety Profiles.

Fault Line audit results are quantified into structured safety profiles assessing safety performance across failure modes, severities and categories, with risk-coverage metrics and statistical comparison to prior releases.

FIG. 03 — RELEASE COMPARISON · SAFETY PROFILE v3.4.4 → v3.4.5
TARGETreception_agent
VERSIONv3.4.4
AUDIT DATE08 May 14:30
COMPARE AGAINST
Select release…
24 Apr 2026 09:12 — v3.4.1
30 Apr 2026 16:44 — v3.4.2
04 May 2026 11:20 — v3.4.3
22 May 2026 14:08 — v3.4.5
HIGH SEVERITY
23%1840
23% (1840)·0pp
MEDIUM SEVERITY
23%1840
23% (1840)·0pp
LOW SEVERITY
8%640
8% (640)·0pp
NO SEVERITY
46%3680
46% (3680)·0pp
100%50%
v3.4.4 — CURRENTv3.4.5 — CURRENT
§ 03  /  WHAT FAULT LINE DELIVERS

Clinical Grade Testing at Software Speed.

reception-agent  ·  pull requests 3 open
#341 refactor: session context normalisation v3.4.3 3 days ago
#344 feat: update triage intent classifier v3.4.4 1 day ago
#347 fix: edge case handling in medication reorder flow v3.4.5 2 hours ago
Fault Line Evaluation Suite passed in 4m 12s
SAFETY SCORE 87 / 100 ↑ +3 from v3.4.4 RISK COVERAGE 94% FAILURE MODES 2 new · 0 regressions

Runs on every release. No clinical bottleneck.

Fault Line's evaluation suite runs as a native CI/CD check on every pull request the same way your existing test infrastructure does. Safety evaluation happens automatically at the point of code change, so clinical AI teams can ship at speed without waiting on manual review cycles. Every release is evaluated. Every regression is caught before it reaches production.

Evaluation reports your clinical, product and compliance teams can use.

Every evaluation run produces a structured independent report written to the standards of clinical governance, medical device regulation and procurement review. Mapped to failure categories, severity-rated and independently authored. Ready to use for regulatory technical files, healthcare procurement processes or clinical safety reviews.

FRAMEWORKS
FDAUK MDREU MDRNHS DTACDCB 0129/160
INDEPENDENT EVALUATION REPORT
Clinical Safety Evaluation —
Reception Agent v3.4.5
DOC. REFFL-CL-2026-014 PREPAREDFAULT LINE AI ISSUED2026-05-12 VERSION1.0 · FINAL
§ 1 EXECUTIVE SUMMARY
§ 2 METHODOLOGY
§ 3 RISK COVERAGE
§ 4 FINDINGS · BY SEVERITY
§ 5 RECOMMENDATIONS
§ 6 APPENDICES
§ 04  /  WHO WE SERVE

Built for clinical AI teams.

CATEGORY 01

Patient-Facing AI Chatbots & Agents

AI voice and text agents interacting directly with patients across the entire care journey from hello to discharge.

APPLICABLE TO
  • Voice agents
  • Patient-facing chatbots
  • Care-pathway assistants
CATEGORY 02

Clinical Decision Support

Copilots, triage systems and diagnostic tools supporting clinical workflows where outputs influence care decisions and operational pathways.

APPLICABLE TO
  • Clinical copilots
  • Triage systems
  • Diagnostic AI
CATEGORY 03

Documentation, Workflows & OS

Scribes, workflow orchestrators and AI operating systems generating clinical content from patient, clinician and healthcare-professional interactions — where accuracy, omissions and downstream workflow reliability matter.

APPLICABLE TO
  • Scribes & documentation tools
  • Workflow orchestrators
  • Clinical operating systems
§05  /  GET IN TOUCH

Let’s build the clinical safety layer together.

Fault Line is building the safety layer that helps healthcare organisations and clinical AI companies trust generative systems in real-world deployment. For health-AI innovators, startups and market-leaders — we would love to hear about what you’re building.

Get in Touch HELLO@faultlineai.com
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