產品

Describe your business need in natural language and get a production-ready system in 60 seconds: database tables, CRUD APIs, admin UI, filtering, pagination, and validation—all at once. Traditionally a new business object takes 10-18 days with 4-5 people; with OMS it is one sentence, one minute, zero code. Includes a state-machine engine for lifecycle management, cross-object navigation, and built-in import/export.

Once modeled, hand it to Loom for scheduling and DBChat for questions — a complete chain

Connect a database and ask questions in natural language—no SQL, no analyst queue. The system understands your schema, generates and executes queries, and returns charts ready to read. Permissions, data masking, and audit are built in: people see what they should see, and every question leaves a trail. Business users self-serve reporting; IT stops drowning in ad-hoc requests.

Pair with Loom to turn ad-hoc queries into scheduled reports; model structures in OMS first

A unified LLM gateway that lets every application on the platform share the same model capabilities: intelligent analysis, report generation, content understanding, and assisted review. Multi-model automatic failover keeps business running when a provider is slow or down. Prompts are versioned and centrally managed; usage is metered in one place. AI-assisted AML screening and due-diligence reports all run on this layer.

All AI capabilities flow from it: embedded in Loom steps, used by DBChat answers

Complex business workflows without writing code: extraction, cleansing, model batch runs, reports, notifications—composed like building blocks. Failed batches resume from checkpoints; every step reconciles. Heavy lifting like scheduling, parameter simulation, and AI-assisted review runs on it. Change a flow by editing config, never waiting on developers.

Once your data is modeled,scheduled batches produce reports and conclusions reach the right people automatically

Describe your business task in one sentence—"analyze transactions for suspicious fund flows" or "QC this batch of medical records against every rule"—and AI proposes the domain, scenario, and tables within seconds. You confirm, the agent is generated, then verified by dual runs on real data with consistent conclusions before release. AI proposes; humans decide. Two agent archetypes (data-analysis and document-review) cover government, enterprise, education, and healthcare. 42 battle-tested agents out of the box; new customers swap rule packs, not code.

Generated agents run on Loom scenarios with Cortex models, complementing Agent Platform governance

Agents as production digital workers, not chat toys. Cross-session memory remembers past investigation conclusions; a benchmark suite makes success rates measurable; tool calls are gated by role with full audit trail; a sandbox caps resource usage; every agent's calls and token usage are metered separately. Fully private deployment.

The factory builds agents; the platform governs them—memory, benchmarks, ACL, sandbox

Say it once, the agent investigates: alert triage, sanctions screening, UBO tracing, fund-network graphs, report reconciliation, rating migration — 14 AML agents covering monitor-triage-report-review. Each agent queries live databases (46M transactions / 780K alerts / 2.86M rating tracks at bank scale) and files structured, auditable conclusions with real numbers.

A next-generation AML operations platform built around AI-assisted triage with human sign-off. Five role workbenches cover the full alert lifecycle; 14 AML agents collaborate like staff. Triage replay makes every AI reasoning step reviewable; coverage matrix and regulation ledger turn compliance into daily routine. Proven on bank-scale data: 46M transactions, 780K alerts.

Heterogeneous database relocation: full-database migration across Oracle, MySQL, and PostgreSQL with automatic schema translation, batched data movement, and initial-plus-incremental continuity. Dialect type differences are adapted automatically; row counts and content reconcile before and after. When replacing a legacy system, historical data moves over in one pass—without stopping the business.

Migrated DB is queryable by DBChat and runnable by Loom the same day

A multi-algorithm entity matching engine built for sanctions screening, KYC, and watchlist monitoring. Exact, transliteration, and fuzzy recall run in parallel and complement each other—maximize recall first, then reduce false positives. Validated at 100M-scale lists with 10K-scale throughput; every match carries explainable evidence for regulatory audit trails.

Hits become OMS tickets automatically, dispatched through Loom flows

Turns the whole internal-control chain—policy, risk, controls, inspection, findings, remediation, assessment, reporting—into a configurable delivery: an in-database check engine runs 71 baseline rules (covering all 18 application guidelines) plus industry packs for government, enterprise, education and healthcare, each hit carrying its regulatory clause and source evidence. 33 storage-layer triggers (across 20 core tables, via 5 trigger functions) enforce segregation of duties, one-way state machines and audit trails at the storage layer (even TRUNCATE leaves a trace), with audit events landing in an immutable, undeletable ic_audit_log. Onboarding a new customer means swapping rule packs and mapping their data to ic_ business tables, not writing code; the management console and scheduled inspections are in production, with tenants isolated per database.

Reuses the family-wide JWT and protocol layer; oversight-report agents are produced by the Agent Factory and run on Loom scenarios