Your compliance and audit teams spend most of their time collecting evidence and testing controls by hand. Relevant builds custom AI agents for compliance and audit that take on that work continuously, ground every finding in your source records, and keep the sign-off with your team, so you automate the effort without automating away the accountability.
Alert generation. Suspicious activity surfaces with the supporting evidence already attached to the case.
Filing drafts. First-draft SARs and CTRs arrive prepared for a human to review, adjust, and file.
Document assembly. Source records pulled together into one complete verification pack per applicant.
Conflict detection. Records cross-referenced, with gaps and inconsistencies flagged upfront.
AI audit automation that tests your controls every day instead of once a quarter, so a failure surfaces when it happens.
Agents that gather and assemble the evidence pack an auditor would otherwise build by hand.
Agents that read your activity against the regulations and internal policies that actually apply to you.
Agents that draft the memo, the observation, and the report, then route them for a human decision.
A coordinated system of specialized agents, not a single bot asked to do everything.
The connective layer that lets agents work from your real data instead of guessing.
Discovery maps your compliance and audit workflows, then picks the tasks where an agent earns its keep and the decisions that must stay human. You get a scoped plan naming which agents get built, what they touch, and where the sign-off gates sit.
Agents are only as reliable as the data behind them, so the connectors and retrieval come first: ERP, email, and document sources wired in through proper AI-powered data engineering so every finding traces to a real record. The deliverable is a grounding layer you own.
Each agent gets built and then tested against your actual scenarios and known failure modes, drawing on our AI-powered testing practice, so it reaches production having already faced the cases that break weaker systems. Nothing goes live on a demo.
Handover puts your compliance officers in command of the approval gates, the escalation paths, and the thresholds, delivered as training and documentation. Your team holds the sign-off from the first day in production.
Regulations shift and so does your business, so the system ships with monitoring and update paths built in: policy mappings kept current, agent behavior tracked, and retraining on a cadence you set.
Our agentic engineering work means the people building your agents have shipped production multi-agent systems.
13+ years of FinTech software development means you skip the education phase. Banks, payment providers, and regulated platforms have already put our engineers through it.
92% of our team is senior-level, so the governance in your agents is designed by people who have run systems in production.
The Relevant AI Delivery Framework™ delivers up to 50% faster AI-assisted development with up to 20% fewer errors, while every line of code still passes senior review.
ISO 27001:2022 certification and GDPR coverage govern how we handle your transaction data throughout development.
You don’t extend trust. You engineer it out of the equation. Reliable AI-powered audit automation is built on retrieval-grounded architecture: the agent cannot assert anything it cannot bind to a source record. Every finding it produces carries its evidence with it. An AML alert arrives with the transaction identifiers, the rule logic that fired, and the underlying documents attached. Your analyst opens the source and verifies in one click. Where the agent cannot locate a record, it reports the gap.
The second layer is the approval architecture. Consequential actions sit behind human gates by design. The agent drafts the SAR; filing requires a named officer. It detects the control failure; closing the finding requires review.
No, AI agents for compliance and audit reallocate your team’s hours from collection to judgment. Audit and compliance functions lose most of their capacity to mechanical work: extracting evidence from source systems, reconciling document versions, capturing control configurations, drafting memos that senior officers rewrite. Agents absorb that layer entirely, and they do it continuously rather than in quarterly bursts.
What remains human is precisely what regulators require to be human. Disposition of ambiguous alerts. Materiality judgments on exceptions. Interpretation where the rule text and your business model diverge. Final sign-off on anything that leaves the organization. The accountability chain does not change; the preparation underneath it does.
The operational result, in our experience delivering these systems, is backlog reduction, faster case cycle times, and senior staff returned to the work their titles describe.
Through an audit trail designed for examination from day one. Every agent action writes to an append-only, tamper-evident log: the data it retrieved, the policy or rule version it applied, the conclusion it reached, the confidence basis for that conclusion, the human who approved it, and the timestamp of that approval. Log integrity is verifiable, so no one can revise history ahead of an exam.
One standard to hold any AI audit automation to, ours included: the agent must log its own actions to the same evidentiary standard your human processes are audited against. A system that cannot produce that record is not an efficiency gain.
Yes. Integration is the first phase of the build, not an implementation detail at the end. Agent reliability depends on grounding in your systems of record, so the engagement opens with the connector layer: your ERP, GRC platform, document repositories, and email, wired in with scoped read permissions, service accounts under your control, and query logging on every retrieval. The grounding layer is delivered as owned infrastructure, and it serves every agent you add later.
This is also the correct pressure point for evaluating any provider. A demonstration on synthetic data proves the model works; it says nothing about whether the system will work in your environment. Ask how they handle your specific stack, how permissions are scoped, and what the agent does when a required record sits in a system it cannot reach. The correct answer is that it flags the gap and routes to a human. Any answer involving the agent working around missing data describes the failure mode you are trying to avoid.
With Relevant, ownership is guaranteed in the contract: you hold it in full. At handover you receive the source code, agent and orchestration logic, prompt and policy configurations, the grounding layer, and the complete audit log history. No license fees, no proprietary runtime you are locked to, no dependency on us for future modification.
The test worth applying to any provider is continuity under separation. If the engagement ended tomorrow: does the system keep running, can your engineers or another vendor modify it, and does the full record of agent activity remain in your possession? With Relevant, all three are yes by contract. Anything less means you are renting a compliance function, and the switching cost comes due at exactly the moment, mid-exam or mid-migration, when you can least absorb it.
Daily testing. Each control checked against real operational activity as it happens.
Failure surfacing. A broken control shows up the day it breaks, with the evidence trail intact.
Report drafting. Filings built directly from transaction records, not reconstructed at deadline.
Full traceability. Every figure links back to the record it came from.
Regime tracking. Regulatory changes monitored across every market you operate in.
Activity mapping. Your operations matched against the regimes that actually apply to them.
Satisfaction shows up in reviews and renewals. 4.9 on Clutch and 98% client satisfaction.
Trust in automation starts with the delivery record behind it. See what our FinTech software development work has already shipped for finance teams.
That is the work agents take first. Bring us the workflow, and you’ll leave knowing what AI-powered compliance automation would change and where your team still signs off.
Fintech
Fintech
Fintech
