Your agents already read internal systems and act on production data, and your security function owns that risk today without a way to see inside it. Relevant Software finds what those agents can be made to do, engineers the limits into your codebase, and wires the checks into your pipeline, so every AI release ships with its guardrails tested.
Relevant Software produces one agreed direction: what each model, agent, and dataset puts at risk, the architecture that contains it, and the sequence the fixes follow.
Relevant’s red team probes your production models and agents with the same techniques a motivated outsider would use, and every finding lands with the steps to reproduce it. Teams that want the surrounding infrastructure covered add penetration testing for the network, cloud, and API layer in the same engagement.
Relevant Software builds the enforcement layer that runs on every request: validation on the way in, filtering on the way out, and policy rules that decide what your model is allowed to say and do. Guardrails ship as code in your repository, each one paired with the test cases that prove it holds.
Relevant Software hardens the path a request travels from user input to model output to whatever your system does next. Where the gateway, retrieval, and observability layers are still on the roadmap, our LLM development team builds them with the controls already inside.
Relevant Software settles what each agent may reach, which actions wait for a person, and how fast your team can stop one mid-run. The tracing, routing, and cost side of the same fleet sits with our AI agent monitoring service.
Relevant Software deploys and runs your models inside your own network, so data residency rules, client contracts, and regulated workloads stay satisfied while your teams keep the model quality they built for. Our engineers own the serving stack, the isolation design, and the capacity plan.
Relevant Software builds the controls that decide which records reach a model and what leaves it, from classification at the ingestion point through deletion that covers model artifacts.
Relevant Software builds the pipelines that turn your production data into training and test sets your teams use freely, with identities removed and the statistical shape intact.
Relevant Software puts the security checks where your engineers already merge code, so each release proves its own controls on the way through. Our DevSecOps practice carries the secure SDLC, IaC, and cloud security work around it.
Relevant Software builds the detection rules, wires them into the tooling your on-call team already opens, and writes the runbook they follow the first time an alert fires.
Relevant Software turns the controls into the artifacts your auditors, your regulators, and your own risk committee ask to see, each one mapped to the requirement it satisfies.
Your first week produces the list: every model, agent, prompt path, and data flow in scope, with the risk rating each one carries. Scope closes before any testing starts, and you sign off on the list and the price before work begins.
Red teaming runs against your live systems, and each finding lands with severity, business impact, and the exact steps to trigger it. Findings arrive continuously, so remediation starts before the report closes.
Fixes reach you as pull requests your team reviews and merges. Our AI software development engineers write them into the same codebase your features ship from, and every pull request passes senior code review on our side first.
The findings that mattered become test cases, and the pipeline runs them on every release. Our MLOps team wires the same checks into your model promotion path.
Detection rules, alert routing, and the incident runbook land in the tooling your on-call engineers already open. A tabletop exercise walks your team through one scenario end to end.
Each control arrives mapped to the named requirement it satisfies across the CRA, EU AI Act, NIST AI RMF, and ISO/IEC 42001. Handover includes the retest schedule and the commands to run it.
ISO 27001, GDPR, and HIPAA-aligned delivery governs how our team handles your models, your data, and your repository access from day one.
The EU AI Act sorts AI systems into risk tiers and attaches duties to each one, from transparency through technical documentation and human oversight, with high-risk obligations arriving in December 2027. Your controls arrive mapped to those tiers and to NIST AI RMF, ISO/IEC 42001, OWASP Top 10 for LLM Applications, and MITRE ATLAS, alongside our IT compliance programs for ISO 27001 and SOC 2.
Every finding closes as a merged pull request in your repository. The engineers who wrote the test write the fix, so the correction carries the same understanding that produced the finding.
Our AI-assisted development practice gives you up to 50% faster delivery and up to 20% fewer bugs, with every release still passing senior code review.
The same team ships LLM, RAG, and agent systems into production. Controls designed by people who have run these systems fit the architecture your teams already run.
96% employee retention means the team that maps your attack surface in month one is the team running your retest in month six.
Relevant Software finds what your models and agents can be made to do, closes it in code, and leaves the tests that keep it closed.
Scope sets the price, and a few variables decide your scope: how many AI systems and agents fall inside it, how many teams own them, which compliance regimes apply, and how much of the remediation our engineers merge into your repository. A single system tested and hardened by two senior engineers sits at one end, and a dedicated team running continuous testing across several products sits at the other. Relevant Software walks each variable with you on the first call and itemizes it in the proposal, and most engagements reach a documented threat model and a prioritized roadmap within 14 days.
Findings reach your team as the red team produces them, so remediation starts in week one, while testing continues. The documented threat model and the prioritized roadmap land inside the first two weeks, and the full control set follows over the weeks after, scaled to how many systems the program covers. Relevant’s delivery record on those milestones runs at 99% on time.
Access sets the pace more than anything else. One check you can run this week: ask whether a developer on each team can grant read-only environment and repository access inside two days. Where that is straightforward, testing starts immediately. Where approvals take longer, we run the inventory and threat modeling in parallel while access clears.
Yes, we can secure a system our engineers did not build. The engagement starts by mapping what actually runs: the models, the prompts, the tools each agent can reach, and the data moving through them. From there the threat model and the remediation sequence work around the systems that stay online, and fixes arrive as pull requests your team reviews.
A rebuild enters the conversation only where an access model makes safe control impossible to express in code, and that case is rare. Inherited systems are common in this work, and the original vendor’s involvement stays optional throughout. If you are unsure what your agents can currently reach, we trace it with you before proposing a single change.
Your controls map to the EU AI Act risk tiers, the NIST AI Risk Management Framework, ISO/IEC 42001 for AI management systems, and the OWASP Top 10 for LLM Applications with MITRE ATLAS for the technical attack surface. The Digital Omnibus agreement moved high-risk obligations to December 2027, while prohibited practices have applied since February 2025 and general-purpose AI model obligations since August 2025. Internal audit cycles and regulator requests arrive on their own schedule regardless.
Each finding and control comes mapped to the named requirement it satisfies, so the output drops into an internal audit, a regulator’s request, or a third-party risk review without translation. If you are unsure which tier your use case falls into, contact us and we classify it with you before any commitment.
Monitoring answers what your agents did, and this work answers what someone could make them do. Observability tooling shows traces, latency, cost, and quality regressions, which keeps a fleet reliable. Security work adds the adversary: an outsider crafting input to reach a tool the agent should decline, or to pull data the agent should hold back.
The two fit together, and the sequence matters. Teams with tracing already in place get faster results here, because the red team reads those traces to pick which paths to probe first. Teams without tracing receive the detection rules as part of this engagement, and the reliability side follows after.
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