RailixAI is a real time enforcement layer for AI agent actions. It checks every decision against policy before it executes in regulated financial workflows. AI agents are moving into production. Governance is not. We fix that gap in real time.
Why It Matters
Financial institutions are putting AI agents into lending, payments, fraud, and KYC workflows faster than they can govern them, and the gap is where the risk lives.
Agents increasingly act, not just advise. In regulated workflows, those actions execute with no explicit runtime enforcement standing between intent and impact.
of financial firms now give AI agents some autonomy
Compliance checks are mostly post-event or manual. By the time a review happens, the non-compliant action has already reached production systems.
say AI adoption has outpaced their governance
Evidence is fragmented across logs, tools, and teams, while penalties for unauthorised AI decisions climb. Reconstructing why an agent acted, for a regulator, is slow.
maximum EU AI Act penalty, or 7% of global turnover
The result is unacceptable operational and regulatory risk in financial environments.
What RailixAI Does
RailixAI sits between your AI agents and your execution systems. Before any AI-driven action runs, RailixAI evaluates it, decides, and records the outcome.
Proposes an action
Evaluates in real time
Allow, block, escalate
Only compliant actions run
Regulatory, business, and organisational rules are checked before the action executes, not after.
Each action gets a risk and behavioural-drift score against the agent's approved intent.
Every action is governed and logged with full reasoning before it reaches production systems.
How It Works
RailixAI evaluates behavioural drift, regulatory constraints, organisational policy, and transaction-level risk, then enforces a decision and logs it.
Insert RailixAI between agent and execution via SDK, OpenTelemetry, proxy, sidecar, or event stream. No or minimal rebuild required.
Check behavioural drift, regulatory constraints (FCA, EU AI Act, DORA, RBI), organisational rules, and transaction risk thresholds.
Allow compliant actions, block non-compliant ones, or escalate for human review, all before the action executes.
Every decision is recorded for audit and compliance reporting, with full reasoning and regulatory mapping.
Use Cases
RailixAI governs the workflows where an autonomous decision carries real regulatory and financial consequence.
Prevent AI from approving loans outside policy thresholds or fair-lending rules.
Validate AI-triggered transactions against limits and controls before execution.
Control automated actions triggered by detection agents, with a full decision trail.
Ensure AI-driven KYC decisions stay compliant with identity and jurisdiction rules.
Govern enterprise copilots executing operational and back-office tasks.
Catch agents that exceed approved intent before they cross a regulatory boundary.
Why RailixAI
Existing tools watch AI systems after actions occur. RailixAI is a real-time enforcement layer for AI agent actions, a fundamentally different category.
Data catalogues govern your data. They do not control what an agent does at runtime.
Model monitors track accuracy and performance, not whether a specific action is permitted.
Observability explains what happened after the fact. By then the action already executed.
It decides allow, block, or escalate before the action reaches your systems.
Integration
Drop RailixAI into the stack you already run. No rebuild required.
LangGraph, CrewAI, custom agent systems, and cloud-native workflows.
Lightweight hooks that evaluate actions inline within your agent loop.
Govern from the telemetry you already emit, with no code changes.
Deploy as a sidecar or proxy to govern agents you cannot modify.
Evaluate actions from event streams across distributed workflows.
Start governing in minutes without re-architecting your agents.
Governance Output
Designed for internal audit, risk and compliance, and regulatory reporting.
A clear allow, block, or escalate outcome for every action.
Why the decision was made, in plain language and structured data.
An immutable record with full lineage for every governed action.
Each decision linked to the regulation and policy it satisfies.
Regulatory Coverage
One platform, mapped to the regulations that matter across the UK, EU, US, and India, plus the global standards that span them.
Who We Are
RailixAI is built by people who have spent their careers at the intersection of banking, regulation, and engineering. We've sat on both sides of the audit, shipped production systems inside regulated institutions, and felt first-hand the tension between moving fast with AI and staying compliant.
That experience is wired into every part of the product, from the regulatory policy library to the runtime controls and audit evidence. Our team brings hands-on expertise across financial regulation (FCA, PRA, EU AI Act, DORA, RBI), cloud architecture, and machine-learning operations, so the platform reflects how compliance actually works, not how a spec imagines it.
Make autonomous AI safe for regulated industries, without slowing innovation.
Runtime-first. Policy as code. Evidence by telemetry. Zero friction for engineering teams.
Tri-jurisdictional from day one: UK, EU, and India. Built for cross-border compliance.
The Bottom Line
As financial institutions deploy autonomous AI agents, control must evolve from monitoring outputs to enforcing decisions in real time. RailixAI provides that missing control layer.