Your LLM and agent applications are live and carrying real work, and every prompt and response now needs to stay inside the rules your business runs on. Relevant Software builds the control layer that enforces those rules at runtime, then tunes it against your own traffic, so you get guardrails tuned to your risk tolerance and the evidence trail your auditors ask for.
Our engineers place a policy layer between your application and the model, so every prompt and every response meets your rules before a user sees it. The wider model, data, and pipeline protection around that layer comes through AI security services.
Attacks arrive inside ordinary user text and inside retrieved documents, so the control layer classifies both before the model reads them.
Responses pass category classifiers and your own policy rules on the way out. Where answers drift because retrieval returns the wrong passages, our RAG development team corrects grounding and citations at the source.
Detection and masking run before a prompt leaves your network, so customer identifiers stay inside your perimeter while the model still receives usable context.
Agents act through tools, so the control layer scopes what each agent may call, with which parameters, and which actions wait for a human. Once the fleet runs in production, AI agent monitoring and orchestration covers tracing, routing, and incident response across every agent.
Thresholds earn their settings from measurement: our team scores the control layer against labelled traffic and adversarial sets, then moves each threshold until blocked attacks stay high and legitimate requests keep flowing.
Discovery covers your endpoints, data classes, tool permissions, and the rules your business already applies to people doing the same work. Teams arriving without a gap analysis start with AI security consulting. You sign off the policy set before our engineers write code.
Every policy receives an enforcement point. Your architects review the design together with the latency budget it carries.
Implementation lands behind feature flags, one endpoint at a time, so your team watches real behaviour at each step. Gateway routing, application code, and tracing arrive through LLM development services where your stack still needs them.
Labelled traffic and adversarial sets produce per-control rates, and each threshold moves until both numbers sit where your risk owner wants them. You receive the evaluation report alongside the settings that produced it.
Policy specs, test suites, threshold sheets, and runbooks transfer to your engineers, with the regression suite runs in the CI pipelines delivered through MLOps services.
Every threshold ships with the false-positive and false-negative rate that produced it, measured on labelled samples of your own traffic before the rollout widens beyond the first endpoint.
Controls land behind feature flags, one endpoint at a time, so your product keeps serving users while coverage grows, including on codebases another vendor wrote.
Your rules live in the control layer, outside any single vendor’s console, so a switch from one model provider to another leaves the enforcement in place.
Each control arrives mapped to its enforcement point and the log that proves it ran, in the table format auditors request for EU AI Act, CRA, NIST AI RMF, and ISO/IEC 42001 reviews.
Up to 50% faster delivery and up to 20% fewer bugs through AI-assisted development, including the adversarial suites that ship with your controls, with every release still passing senior code review.
96% employee retention. The engineers who set your thresholds stay reachable when your traffic pattern shifts, and the threshold sheet, runbook, and regression suite let your team move settings on their own.
Budgets follow three variables you can count today: how many endpoints call a model, how many distinct policies those endpoints need, and whether agents hold tool permissions that reach production systems. A single chat endpoint with content and PII policies sits at the small end, and a fleet of agents with write access to CRM, billing, and ticketing sits at the large end.
Count your model-calling endpoints and mark which ones can write to another system, and you hold the two numbers that move an estimate most. If that inventory is unclear, our team builds it with you during discovery, with nothing to prepare beforehand. You can contact us with the endpoint count and get a range on the first call.
You receive a roadmap within 14 days of the first call, covering the control set, the enforcement points, and the rollout order. First controls reach a live endpoint in three to five weeks, starting with the highest-exposure path behind a feature flag, and Relevant delivers 99% of milestones on time.
Tuning continues after launch, because thresholds sharpen as real traffic accumulates. A review at one week and one month of live traffic is part of the plan.
Yes, we can. Our engineers read the existing application and gateway code, map where model calls leave your network, and place controls at those points while the product keeps its shape. Retrofits cost less than rebuilds because the enforcement points already sit in your request path.
Run this check today: open your gateway config and count how many model calls pass through it and how many the application makes directly. A high share going through the gateway means most controls land in one place. If your team runs without a gateway today, our engineers introduce one during the build, and that path is well trodden.
Platform guardrails cover a real slice of the work: category filters, basic PII detection, and denied-topic lists arrive configured and maintained by your provider. Teams on a single provider, with one content policy and agents that hold read-only access, often get everything they need there, and our team says so on the first call.
A custom layer earns its cost where enterprise and regulated constraints enter:
Calibration answers that, and calibration runs on measurement. Our team assembles a labelled set from your own traffic, scores every control against it, and reports the false-positive rate per control before the rollout widens. Thresholds then move until the rate sits inside what your risk owner accepts.
A self-check you can run now: sample 200 recent production requests and mark how many would trip a strict content filter. Anything above a few percent tells you the default settings need tuning work ahead of launch. If sampling is impractical right now, our team pulls the sample during discovery and shares the numbers with you.
Named engineers work in your repo, and you choose the shape. A guardrail engineer with an ML security specialist covers most retrofits onto an existing application, sitting in your sprints and your code review. Larger programmes, several endpoints, several providers, and a compliance deadline, run better with a dedicated team carrying architecture, implementation, evaluation, and documentation together.
Our engineers map every endpoint, set the controls each one needs, and tune them to your risk tolerance.
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