Your process already has rules, systems, and sign-offs. Relevant Software builds a custom AI agent for one regulated workflow at a time: it handles the routine work inside the systems you use, hands decisions to your people where policy requires, and is measured by the metrics your process already tracks.
The right first workflow has high volume, clear rules, and a cost you can measure today. We help you pick it and record the baseline before any build starts.
Every step of the workflow gets an owner: the agent, a person, or nobody, because some steps should never be automated.
Most regulated workflows start with documents: forms, statements, reports, and correspondence. The agent reads them, extracts what matters, and checks it against your records.
The agent works where your teams already work, not in a separate tool they have to learn.
Each decision the agent prepares arrives with its reasoning and evidence, so the person approving it can check the work quickly.
The agent proves itself on real cases before it handles any on its own.
The agent collects documents, verifies identity and company data against registries and screening lists, and prepares the onboarding file. A compliance analyst approves or rejects the customer.
The agent gathers transaction history and customer context for each alert, closes clear false positives under your rules, and prepares the rest for investigators. Decisions to escalate or file a report stay with your team.
The agent reads first notice of loss, policy terms, and supporting documents, checks coverage, and routes each claim with a summary. Adjusters decide on payment or denial.
The agent assembles clinical documentation and checks it against payer criteria before submission. A clinician confirms the request, and the agent tracks it until a decision comes back.
The agent extracts adverse event details from emails, call notes, and literature, and fills the case form. A safety specialist assesses seriousness and causality.
The agent pulls figures from several systems, reconciles them, and drafts the report for review. Our AI agents for compliance and audit automation cover compliance workflows in depth.
We choose the first workflow with your process owner and record current handling time, backlog, and error rate.
The step-by-step map of what the agent does and where people sign off reaches your process owner and compliance team for approval.
The agent is built against the approved map and connected to your systems of record in a staging environment.
The agent works on real cases in parallel with your team, and results are compared case by case.
Once results match your targets, the agent handles part of the live volume with sign-off at every required point.
The agent scales to full volume, and code, runbooks, and training transfer to your team as part of a wider enterprise AI agent deployment.
Open Banking, HL7 FHIR, and PSD2 delivery experience, so the agent’s integrations and controls come from projects already shipped.
Core banking, EHR, policy administration, and legacy systems. We connect agents to the systems your teams use, without a platform swap.
ISO 27001, GDPR, and HIPAA-aligned delivery. Access controls and audit trails are built to the standards your reviewers already accept.
96% employee retention. The engineers who build your first agent stay on for the workflows that follow.
Our engineers use AI-assisted development to deliver up to 50% faster, and ship up to 20% fewer bugs, with senior review documented on every change.
200+ companies have built and scaled products with Relevant, and the case studies below are the checkable subset.
Good candidates have high volume, documented rules, and inputs that are hard for traditional automation, such as documents, emails, or free text. Poor candidates are low-volume processes, workflows where every case needs deep expert judgment, and processes with no written rules at all.
A quick check: if your team can explain to a new hire how to handle 80% of cases, an agent can likely handle those same cases and send the rest to a person.
RPA follows fixed steps on fixed screens and breaks when inputs change. SaaS agents work well inside their own platform but rarely reach your core or legacy systems. A custom agent reads unstructured input, works across your systems, and follows the approval rules of your specific process.
Many enterprises keep RPA for stable, structured tasks and add an agent where documents and exceptions slow things down. For a broader view of agent types, see our AI agent development services.
Wherever your policy requires a decision by a person, and wherever the agent isn’t confident enough. The agent prepares the case, states its reasoning, and waits for approval before any regulated action.
Start from your current approval matrix: every sign-off in it becomes a sign-off in the agent’s workflow unless your compliance team decides otherwise.
It depends on what the agent decides. Uses such as creditworthiness assessment and risk pricing in life and health insurance are listed as high-risk. After the Digital Omnibus, those obligations apply from 2 December 2027. Many workflow agents that prepare cases for human decision fall outside that list, but your legal team should confirm the classification.
We build agents with documentation, logging, and human oversight from the start, so the design holds up whichever classification applies. For obligations beyond engineering, our AI compliance services can help.
Yes. Where a system has an API, we use it. Where it doesn’t, we connect through adapters, database views, or file exchanges that your IT team approves. The agent never needs a platform replacement to go live, and it can run in your own cloud through our cloud AI agent deployment work.
List the systems your workflow touches today, with how each one is accessed. That list sets most of the integration effort.
We compare the agent against the baseline recorded before the build: handling time per case, backlog size, error or rework rate, and the share of cases handled without escalation. The shadow run gives the first real numbers before the agent handles any live cases on its own.
If you want to model the payoff for a specific workflow before committing, contact us and we’ll work through it with you.
We map the process, build the agent into your systems, and prove it on real cases before it handles any on its own.
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