Tier 01 / Rapid Impact / 3–4 weeks
Trust Audit.
Know what your AI system is actually doing — and whether you can trust it. A structured, end-to-end evaluation of accuracy, reliability, and risk across your real workflows.
Why it matters
AI systems are no longer simple. They are multi-step, multi-model, and deeply integrated into business workflows. Yet most organizations don’t know when outputs are wrong, can’t predict the impact of changes, and lack visibility into how decisions are actually being made.
If your AI system is making decisions, you need to know when it’s wrong, why it’s wrong, and what will break next.
The result of not knowing: hidden risk, inconsistent performance, and a hard ceiling on how far you can scale before something fails publicly.
What we do
Beyond prompt-level testing.
We don’t score individual model outputs in isolation. We evaluate complete agentic systems — decision chains, multi-step reasoning, integration paths, and real production behavior. Four dimensions, measured against your business reality:
Accuracy
Are outputs correct across real-world scenarios — including the edge cases that don't show up in synthetic benchmarks?
Reliability
Does performance hold under variation, scale, and the kinds of inputs production actually sees?
Traceability
Can you understand how decisions are being made — and who or what owns each step in the chain?
Risk Exposure
Where can failures propagate into operations, customer impact, or compliance — and how big is the blast radius?
What you get
Five deliverables.
Every engagement produces the same five artifacts. Walked through with your team. Designed for both your engineers and your executives.
01
AI System Map
A clear visualization of your AI workflows, decision points, model and tool dependencies, prompt structures, and integration boundaries. The system as it actually behaves — not what's on paper.
02
Trust Scorecard
Overall system accuracy. Performance broken down by use case, scenario, and workflow segment. Identification of high-risk failure points. Executive-friendly summary plus the underlying detail.
03
Failure Analysis
Root-cause breakdown across prompt-level issues, data and context gaps, integration failures, incorrect tool usage, and policy or logic breakdowns. Not just what failed — why.
04
Risk & Impact Assessment
Where failures would impact operations, customers, or compliance. Deployment risks. Stability concerns. The places where small changes could create large downstream effects.
05
Remediation Roadmap
Prioritized actions across 30, 60, and 90-day horizons. Quick wins, structural improvements, and the governance practices that should be in place before the system scales further.
How it runs
Three to four weeks. Fixed scope.
No discovery phase that turns into discovery quarter. No scope creep. A clear weekly cadence with concrete artifacts at the end.
Week 1
System discovery and mapping
We work with your team to map the real system end-to-end — LLM calls, agent chains, data sources, decision points, prompt structures, integration boundaries.
Week 2
Evaluation framework setup
We instrument the system. Define real evaluation scenarios. Build test datasets from production logs and synthetic edge cases. Set criteria across accuracy, consistency, reasoning quality, and policy compliance.
Week 3
Execution and analysis
Structured evaluations run across your workflows. Decision chains analyzed end-to-end. Composite accuracy scoring. Multi-step reasoning failures surfaced and categorized.
Week 4
Reporting and recommendations
All deliverables produced and walked through with your team — including a remediation roadmap your engineers and operators can act on the day we hand it over.
Who this is for
Organizations with AI or LLM systems already in production or advanced pilot — copilots, agents, decision-automation workflows. The signal we listen for is some version of: inconsistent outputs, hallucinations, opaque decisions, or a growing nervousness about scaling something that nobody fully owns.
Typical stakeholders we engage with:
- — CIO, CTO, Chief Data & AI Officer
- — Head of AI / ML / Applied Research
- — VP of Operations, Claims, Underwriting, or Customer Ops
- — Heads of Risk, Compliance, and Governance
Why TGAIC
Most firms doing AI evaluation today are either tracing individual model calls (technical platforms) or producing governance frameworks (large consulting). Neither evaluates AI as a decision system — input to outcome, across the full orchestration chain.
That gap is what Trust Engineering exists to close. The Trust Audit is the first time most clients see what their AI is actually doing.
More on Trust Engineering→Start
Run a Trust Audit on one workflow.
We take a limited number of new Trust Audits each quarter. The fastest way to see whether your AI is doing what you think it is — and what to fix first.