Large Language Models
Relay is model-agnostic, selecting the best model for each task. The better the models get, the more ambitious the experiences we can build.
Audit automation, built by auditors
Relay does the keying, the tying out, the rebuild every time the figures change — so your time goes to the numbers that don't look right.
Latest from the blogThe Manager Who Already KnowsMost AI tools make you re-brief them every session. Relay works like the manager who already knows the client — carrying the whole engagement, your firm's methodology, and every decision so far in one connected knowledge graph.Under the hood
Relay is built across seven layers, from the AI models up to the audit interface, so the work it produces is accurate, reviewable, and safe to use on a real engagement.
Relay is model-agnostic, selecting the best model for each task. The better the models get, the more ambitious the experiences we can build.
The agentic harness is the orchestration engine turning general-purpose LLMs into audit-specific agents. LLMs are necessary but insufficient for audit work.
Native integrations with your audit methodology, engagement software, email, Excel, and more. The aOS operates as part of the firm's ecosystem, not alongside it.
The agent is constantly evolving, learning from current-year and prior-year workbooks in real time while referencing client emails and submitted source documents.
Audit-specific domain knowledge with reference to local and international standards such as IFRS and GAAP, ISA methodology, and Office and Google suite workflows.
Purpose-built surfaces for every audit workflow: drafting, research, preparation, client delivery, and workpaper finalisation. Built from the ground up for audit.
The foundation that makes the aOS enterprise-ready: ethical walls, cross-matter isolation, audit trails, and secure data storage according to your organisation's requirements.
How it works
You run the audit. Relay's agents do the execution — planning prep, evidence requests, population testing, exception analysis, and reviewer-ready write-ups — and bring every step back to you to sign off.
Please prepare an analytical procedure on the client's expenses.
Inspecting client's financial statements
Gaining understanding from dynamic knowledge base on the client
Checking materiality of the client
Inspecting audit methodology for sample collection
Creating workbook and selecting sample from the population
Awaiting review before sending to client
Audit Bench
Audit Bench is our AI evaluation framework for real-world audit work. It uses lifelike audit scenarios to benchmark how different models plan, inspect evidence, apply methodology, and produce reviewer-ready output.
Synthetic-but-grounded workpapers, source docs, populations, exceptions, and reviewer prompts.
Cases are shaped with practising audit judgement so models are tested against how audit work actually breaks.
Benchmark planning quality, evidence use, sampling decisions, exception handling, and final write-up quality.
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