Regulations change weekly. Your compliance team can't read fast enough. AI can.

We maintain regulatory knowledge bases that update with new rules, guidance, and enforcement actions. When regulations change, the AI's review criteria update accordingly. Your compliance team validates major changes, but they're reviewing AI-flagged updates instead of reading every regulatory bulletin themselves.

Yes. Every flag, score, and decision includes a full audit trail — what data was analyzed, what rules applied, confidence score, and reasoning. In financial services, a decision you can't explain is a decision you can't defend. Explainability is built into every system.

No — and it shouldn't. AI handles the volume work: screening, monitoring, document review, report generation. Your compliance team handles judgment, strategy, and escalated cases. The result is a team that's smaller but more effective, focused on risk management instead of data processing.

We integrate with or layer on top of existing compliance platforms. If you're using NICE Actimize, Thomson Reuters, LexisNexis, or similar tools, AI enhances them rather than replacing them. For firms without existing platforms, we build the compliance layer from scratch.

12-16 weeks for the first use case, including compliance review and security validation. KYC/AML automation tends to ship fastest (8-10 weeks). Transaction monitoring and regulatory reporting follow. We work with your compliance and IT teams throughout to ensure every requirement is met.

Workiva, AuditBoard, ServiceNow GRC and similar platforms are workflow systems — they orchestrate evidence collection, route reviewer approvals, and template the final narrative. They do not pull the raw data, normalize it across systems, or write the underlying analysis. For a mid-market SOC 2 Type II, EU AI Act Article 12 logging report, or LGPD impact assessment, that workflow layer is roughly 30-40% of the actual work; the rest is engineering a pipeline that ingests logs from your data warehouse, IAM, identity provider, model serving layer, and ticket system, deduplicates and time-aligns them, and produces an evidence pack that an auditor can replay. An AI-driven pipeline operates one layer below the SaaS: it owns the data lineage and the narrative generation, and then optionally hands the final artifact to Workiva or AuditBoard for sign-off. The rule of thumb we use with clients: if your compliance team is spending more than 40 hours per audit cycle reformatting evidence into the SaaS, you have outgrown the SaaS and need the pipeline. If you are still under that threshold, the SaaS is cheaper and faster to operate.

Article 12 of Regulation (EU) 2024/1689 requires that high-risk AI systems automatically record events ('logs') over the lifetime of the system to a degree that enables traceability of functioning. The European Commission's February 2026 implementation guidance clarifies the minimum fields: the period of each use (start and end timestamps), the reference database against which the input was checked (e.g., the model version and the retrieval corpus snapshot for a RAG system), the input data that led to a match or classification, identification of the natural persons involved in result verification (Article 14 human-oversight roles), and any system error or anomaly the model surfaced. Logs must be retained for at least six months unless sectoral law (financial services under DORA, healthcare under the MDR) requires longer. The practical implication for a build is that your inference path needs structured logging at the model-call boundary — not at the application layer — and your retention policy needs to be explicit and auditable. Logging at the application layer alone fails Article 12 because it cannot prove what the model actually saw.

Yes, provided the pipeline is built with the operating model in mind from day one. The break-even point we see at clients with $5M-$50M revenue is around three sustained operators: a compliance lead who owns the regulatory interpretation and signs off on reports, an IT or security lead who owns access reviews and change management, and a finance or operations lead who owns the data sources the pipeline depends on. None of them need to be a full-time compliance engineer. What makes this possible is the way the pipeline is structured: regulatory rules are encoded as versioned configuration (not custom code), evidence collection runs on managed schedules with retry and self-healing, and the AI narrative layer drafts the report with citation-grounded reasoning that the compliance lead reviews rather than writes. The teams that fail at this are the ones who treat the pipeline like a build-once-and-forget project. The teams that succeed treat it like a quarterly product, with a clear backlog of regulatory updates, evidence-source changes, and reviewer feedback feeding the next iteration.