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Proof from work that crosses the enterprise.

Across runs where work moves between teams, carrying context, handoffs, and governed actions through Financial Operations, Software Delivery, and Revenue Execution.A Fortune 100 technology company is our flagship enterprise proof. The customers and case studies below show each use case in practice.

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proof network

Proof behind each use case.

The named customers here carry the revenue and delivery proof; the compliance and payables case studies are below.

Enterprise proof

Fortune 100 process acceleration with Across.

Our largest deployment shows Across operating at scale, under governance, and across functions.
Revenue Execution

Deal context that follows every account.

Helps revenue teams carry context across accounts, conversations, and next actions.

Revenue Execution

Context that carries across customer programs.

Keeps signals connected as teams coordinate events, accounts, and complex customer programs.

Salesforce ecosystem

Agentic work for Salesforce-native teams.

Brings reasoning and memory to teams building and shipping in the Salesforce-native systems where the work already happens.

case studies

A closer look at three workflows.

Compliance and payables for financial operations, plus an AI teammate for software delivery. Pick a deck to open it. For revenue execution, see the Fortune 100 proof above.

Financial Operations · Compliance use case

Could you show an examiner the lineage behind every decision?

Across AI brings the regulatory context (data lineage, control logic, policy semantics, audit trails) that makes agentic AI defensible inside a regulated bank.

For US regional banks · $20B–$100B in assets

The problem

Every compliance system was built in isolation.

And now examiners expect risk-based judgment that legacy rules engines can't deliver.

  • Signals that never meet

    Legacy bots, screening engines, KYC vendors, and mainframe logic each generate their own.

  • Horizontal AI widens the gap

    An LLM-only tool can't show deterministic execution or an examiner-ready audit trail.

  • The missing piece isn't more AI

    It's the regulatory context: lineage, control logic, policy semantics, and audit trails.

The approach

Deterministic where it must be. Reasoning where an analyst would.

Regulated steps run on fixed, auditable logic. Reasoning is applied only where an analyst would use judgment.

Unifies a fragmented estate

Pulls signals from core, KYC, monitoring, screening, and case management into one model.

Tracks how every value changed

Every update is logged: see what changed, why, and which source drove it.

Produces examiner-ready lineage

The path from source to disposition is reconstructed on demand, not rebuilt by hand each audit.

Lets you replay history

If a feed arrives late or missing, wind the graph back and rebuild the affected conclusions.

Pre-built for the highest-value workflows

Agents that fit a regulated environment.

KYC

KYC Onboarding

Doc ingestion, identity verification, screening, CDD/EDD narrative.

ALT

Alert Triage

Alert enrichment, contextual reasoning, L1 disposition.

SAR

SAR Drafting

Evidence assembly, narrative drafting, quality check, human approval.

Why it fits a regulated environment

Traceability isn't bolted on: the system knows the source of every conclusion, agents inherit each user's permissions, and accuracy is tunable to the bank's controls.

How we approach delivery

Start with a POC on synthetic data.

We prove value on synthetic compliance data first: where automation stops today, and how the reasoning graph clears it. You judge the dispositions and the fit with your controls.

One layer, many compliance processes

The same layer extends across KYC onboarding, transaction monitoring, regulatory change, and model governance.

Make your agentic AI defensible.

Visit across.ai or reach out to the team for a demo.

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This describes Across AI's approach and intended outcomes for regulatory compliance; it is not legal or regulatory advice and is not a guarantee of results. Proof-of-concept work is run on synthetic data. Agents and connectors are scoped per engagement and configured to the bank's controls.

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Get started

Start with one workflow where context loss is expensive.

Bring Across into one workflow where memory matters, review matters, and the next action cannot be guessed from a single prompt: payables, a sprint team, a book of accounts.

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