Financial Intelligence Operating System
Solomon connects institutional systems into a live financial ontology and gives teams a private AI layer to investigate, explain, and act.
Synthetic data. Illustration of the Solomon ontology — not a real institution.
The institutional problem
Every system below already holds part of the answer. The gap is between them: an analyst opens seven tools, re-keys the same identifiers, and reconstructs context by hand before a single decision can be made.
Solomon · Live ontology
Resolved · Governed · Cited
Solomon reads from these systems and resolves them into one live ontology — so the context an investigation needs already exists before anyone asks for it.
How Solomon works
01
Connect existing institutional systems without replacing core infrastructure.
02
Transform fragmented records into a live ontology of financial entities, relationships, events, and actions.
03
Give authorized teams a private AI layer that understands institutional context, permissions, and data lineage.
04
Turn findings into investigations, workflows, decisions, reports, and auditable operational actions.
How Solomon works
01
Connect existing institutional systems without replacing core infrastructure.
02
Transform fragmented records into a live ontology of financial entities, relationships, events, and actions.
03
Give authorized teams a private AI layer that understands institutional context, permissions, and data lineage.
04
Turn findings into investigations, workflows, decisions, reports, and auditable operational actions.
Inside the product
This is a working fragment of the Solomon workspace running on synthetic data. Select entities, run the private AI query, and open a case — the way an analyst would.
Loading workspace…
Platform
A live model of customers, accounts, companies, wallets, devices, and transactions — with relationships that carry provenance.
Deterministic resolution merges records only on hard identifiers; everything less certain becomes a reviewable proposal.
An AI layer that answers inside your perimeter, cites its sources, and respects each user’s permissions.
Every claim links to the record behind it. No citation, no answer.
Signals become structured investigations with evidence, timelines, and supervisor review.
Alert triage, relationship tracing, and case files that stand up to audit.
Findings turn into assigned tasks, approvals, and reports — recorded end to end.
Every consequential action carries who, when, and on what authority.
On-premises, private cloud, or your VPC. Customer data does not leave the deployment.
Same images, same migrations, different configuration — from pilot to production.
Deployment & integration
Solomon deploys inside your perimeter and connects to existing systems through read-only, outbound-only integrations. Nothing connects in.
Your data center, your identity provider, your encryption keys, your backup policy.
Batch files, database reads, REST, and event streams — read-only by default.
Permissions enforced server-side by the service that owns the data.
Append-only record of every consequential action and every AI tool call.
Every attribute records which source produced it and when it arrived.
Which models run, on what data, with which tools — configured and logged.
Consequential decisions require a person. The AI proposes; people dispose.
Use cases
01
From alert to defensible case file in one workspace.
Alert → related entities → transaction history → evidence → supervisor review.
02
One answer to “who are we actually dealing with?”
Search → resolved entity → relationships, documents, and history with provenance.
03
Every product, account, and interaction in one governed view.
Customer → accounts and wallets → devices and channels → open items.
04
Context-rich review instead of queue-clearing.
Signal → counterparties → pattern across accounts → disposition with rationale.
05
Screening, review, and reporting with a complete trail.
Watchlist hit → identity check → decision → auditable report.
06
Networks surfaced, not just individual accounts.
Seed entity → graph expansion → chain of relationships → coordinated case.
07
Questions answered from governed data, with citations.
Question → private AI → cited answer → exportable summary.
08
Repetitive operational steps executed under policy.
Trigger → proposed action → human approval → recorded execution.
Security & governance
Solomon is built on the assumption that it will be examined — by your security team, your auditors, and your supervisor.
01
Runs inside your perimeter. Customer data does not leave the deployment.
02
Every permission decision is made server-side by the service that owns the data.
03
AI answers cite the records they rest on. No citation, no answer.
04
Append-only audit of consequential actions, AI tool calls, and exports.
05
The AI cannot close a case, block an account, or export data on its own.
06
Each attribute records its source, arrival time, and confidence.
07
Models, tools, and data scope are configured, versioned, and logged.
08
Operational actions run under policy, with approvals recorded end to end.
Evidence
Solomon is an early-stage platform working with design partners. We do not publish customer names, testimonials, or performance numbers we cannot verify — on this page or anywhere else.
What a pilot measures
A pilot runs on synthetic or masked data first, inside your perimeter, with success criteria agreed in writing before it starts.
Connect your systems, model your operational reality, and give every authorized team the context required to act.