AI & Application Audit Services
Independent, evidence-based assessment of production AI systems, microservice architectures, and data platforms — identifying reliability gaps, compliance risks, and improvement roadmaps before they become incidents.
Full enterprise platform assessment timeline
Maximum microservice estate assessed in a single engagement
Every finding backed by code, data, or architectural evidence
We confirm before we report — no speculative risk ratings
What We Assess
Application Architecture Audit
- Microservices Mapping: Complete inventory and dependency mapping of distributed service architectures
- Service Boundary Analysis: Identification of coupling issues, shared database anti-patterns, and boundary violations
- Data Flow Documentation: End-to-end data flow tracing across all service interactions with evidence at file and line level
- Integration Pattern Review: Assessment of API contracts, event-driven patterns, retry logic, and failure handling
Reliability & Resilience Review
- Error Handling Assessment: Systematic review of exception handling, dead-letter queues, and failure recovery across all services
- Circuit Breaker & Retry Audit: Identification of absent or misconfigured circuit breakers creating silent failure risks
- Queue Resilience Analysis: Redis, Kafka, and message queue depth, timeout, and drain-time risk assessment
- Race Condition Identification: Multi-writer database patterns, transaction coordination gaps, and consistency risks
Security & Compliance Assessment
- PHI / PII Data Flow Tracing: Systematic identification of sensitive data exposure across event payloads, APIs, and storage
- HIPAA Compliance Gap Analysis: Service-by-service assessment against HIPAA requirements with prioritised remediation
- CI/CD Security Review: Pipeline configuration assessment covering secrets management, access controls, and deployment gates
- Secrets & Credential Audit: Identification of hardcoded credentials, unrotated tokens, and insecure secret handling patterns
Observability & Performance Review
- Monitoring Coverage Assessment: Evaluation of Datadog, PagerDuty, and team-owned monitoring standards across services
- Distributed Tracing Gap Analysis: Assessment of end-to-end trace capability across service boundaries
- Performance Bottleneck Identification: Database query analysis, queue depth patterns, and peak load handling review
- Testing Strategy Assessment: Test coverage, CI/CD automation quality gates, and production-environment parity evaluation
Technology Stack
Languages & Frameworks
Ruby on Rails
Python (Django, FastAPI)
Node.js
Java / Spring BootInfrastructure Assessed
AWS (Lambda, ECS, RDS)
Microsoft Azure
Kubernetes / VMware
On-Premises SQL ServerObservability Tools
Datadog
PagerDutyCloudWatch
SentryCI/CD & Source Control
GitHub Actions
Jenkins
GitLab CI/CD
TerraformIndustry Applications
Healthcare & Health IT
- HIPAA and PHI compliance gap assessment
- Prior authorization and claims processing platform audits
- EHR integration and data integrity review
Financial Services
- Payment processing reliability assessment
- API security and credential management review
- Regulatory compliance gap analysis
Technology & SaaS
- Pre-acquisition technical due diligence
- Pre-migration architecture assessment
- Production reliability review for scaling teams
Enterprise & Consulting
- Platform consolidation readiness assessment
- Multi-team observability and monitoring standardisation
- Architecture documentation for undocumented legacy systems
Featured Success Stories
Healthcare Technology Platform — Enterprise Microservices Audit
Our Methodology
Scope Definition & Access Setup
Engagement boundary agreed, codebase access provisioned, stakeholder workshops scheduled
Architecture Reconnaissance
Service inventory, dependency mapping, and domain structure documented from code, Confluence, and engineering interviews
Deep Code Review
Systematic analysis of high-traffic and high-risk services across all 8 assessment areas with file and line-level evidence
Observability & Tooling Analysis
Datadog, PagerDuty, and CI/CD pipeline assessment against engineering best practices
Risk Register Construction
Findings prioritised by severity (RED / AMBER / GREEN) with evidence-backed rationale for each
Remediation Roadmap Delivery
Quick wins and long-term initiatives structured by effort, impact, and dependency order
Stakeholder Presentation
Executive summary and engineering deep-dive presentations delivered to relevant audiences
Why Choose Agility.AI
Evidence-First Methodology
Every finding is backed by specific code evidence at file and line level. We do not produce risk ratings based on assumptions, interviews alone, or pattern-matching without confirmation.
Production System Experience
Our assessments are conducted by engineers who build and operate production systems at scale — not auditors working from checklists. We understand what the risks actually mean in live environments.
Independent Perspective
As an external party with no stake in the existing architecture, we surface issues that internal teams normalise over time — the gaps that are genuinely invisible from inside.
Actionable, Not Academic
Our deliverables are remediation roadmaps, not audit reports. Every finding comes with a specific, prioritised recommendation that an engineering team can act on immediately.
Frequently Asked Questions
Everything you need to know about our AI audit and assessment process
Ready for an Independent Assessment?
Schedule an assessment with our audit specialists to get an evidence-based view of your platform's reliability, security, and compliance posture.
Schedule Your Assessment