Hidde automates the repeatable work inside AML, sanctions, fraud, and compliance operations: enrichment, evidence gathering, case assembly, reviewer routing, and audit-ready documentation.
Give your team queue-level throughput gains while preserving human sign-off for sensitive decisions, external communications, and policy exceptions.
Move repeatable alert, EDD, case-prep, QA, and response work forward before analyst review begins.
Orchestrates work across case management, monitoring, KYC/KYB, internal data, email, and Slack.
Reviewer hand-offs, approval matrices, and decision traceability are embedded from the first workflow.
Source evidence, disposition rationale, reviewer hand-off, and complete audit chronology travel with every output.
Hidde is built for the repetitive, policy-driven layers of financial crime operations: the work that is high-volume, documentation-heavy, and still too manual.
Institutions do not buy another co-pilot pane. They buy workflow coverage, control evidence, and cycle-time reduction.
Handle first-pass review across AML, sanctions, fraud, and compliance queues with policy-based enrichment and clear reviewer routing.
Assemble KYC/KYB context, documentation gaps, transaction history, and review packs before the analyst or relationship team steps in.
Pull counterparties, prior decisions, customer context, and supporting evidence into a single investigation pack for downstream review.
Draft investigation notes, escalation write-ups, SAR support, QA responses, and committee-ready briefs with source evidence attached.
Organize information requests, trace approval history, and prepare regulator-facing response packs without rebuilding the chronology by hand.
For defined queues, pair the product with an operated model so throughput improves without relaxing control design.
The best first workflows are operationally painful, policy-driven, and easy to evaluate: teams can see whether more cases move, whether exceptions route cleanly, and whether reviewers trust the evidence trail.
There is enough recurring queue work for automation to change throughput, not just save a few minutes on isolated cases.
The workflow already has typologies, evidence requirements, escalation rules, QA standards, or disposition criteria that can be encoded.
Analysts spend meaningful time gathering context, reconstructing chronology, and attaching source material before judgment can happen.
The output needs a clear maker-checker path, auditable rationale, and human-owned decisions for sensitive or non-routine scenarios.
Hidde works through the queue before anyone opens a laptop, then posts the state of play with triage, exception routing, and recommended next actions.
Morning operating brief:
Due today: periodic review pack for Merchant 48721 ready by 09:30
Waiting on you: 2 external responses need final sign-off
Want me to walk you through the L2 escalations first?
That is the gap we are targeting. Analysts still enrich alerts, collect evidence, prepare EDD packs, draft disposition rationale, and manage reviewer hand-offs.
Alert volumes, EDD refresh cycles, QA requests, and ad hoc escalations expand faster than hiring plans, leaving teams in permanent reactive mode.
Case management, transaction monitoring, sanctions screening, KYC/KYB records, internal notes, and email threads still have to be stitched together by hand.
Disposition memos, escalation write-ups, periodic review packs, and regulator-facing drafts consume capacity that should be reserved for judgment calls.
Some teams want Hidde as the AI operating layer inside their existing function. Others want defined workflow coverage for alerts, EDD, QA, or regulatory-response support. The platform is built to support both.
Deploy Hidde across your existing financial-crime stack to automate L1 review, case assembly, and documentation inside current workflows.
Scope defined queues such as alert handling, EDD preparation, QA support, or regulatory response and pair the product with operated delivery where it makes sense.
Private environments, custom integrations, approval matrices, and deployment design for teams with tighter governance and data requirements.
Engagements are scoped around workflow volume, exception rates, control design, integration depth, and deployment requirements, not a generic seat count.
Hidde does not jump from zero to autonomy. Reviewer sign-off, approval matrices, and exception routing are configured lane by lane so the control model matches the workflow.
Control evidence, data handling, and execution boundaries sit inside the product architecture from day one. This is not security theater layered over a demo.
Maker-checker boundaries, reviewer queues, and explicit approval points are enforced at the workflow level, not bolted on after the fact.
Each output retains its source evidence, approval history, and action log so teams can reconstruct the chronology of a case on demand.
PII masking, scoped access, and system-bound data rehydration keep the model from becoming the widest access layer in the stack.
External content is isolated, action scopes are bounded, and non-routine scenarios route back to human reviewers by default.
The question is not whether the model can answer a prompt. The question is whether the workflow progresses with control evidence, reviewer hand-off, and auditability attached.
We work with financial crime and compliance teams that want a more ambitious model than another AI tool. We will work out whether the right first step is platform deployment, bounded workflow coverage, or both.
No generic demo flow. Just a conversation about queue depth, controls, and workflow coverage.