Private Reasoning Engine blocks
Frontier-class models running privately for your firm. Your data never trains someone else's model. Each block exposes a clean interface so it can be swapped or upgraded without touching anything downstream.
Got-Data Inc. · r.team · Private AI for fiduciary operations
A Reasoning Engine for wealth management. Decisions you can defend.
A private Reasoning Engine works over your custodian, fee, and trust data — inside your boundary. It produces answers your team can act on, and shows its work rather than asserting a result. Reasoning Engine blocks and agent pods compose under your governance, one block at a time.
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Intelligence innovation
When the stakes are fiduciary, AI has to be operational — not experimental.
Mission-critical operations depend on fusing fragmented signals into decisions operators can defend in real time. In defense, that means integrating sensors, logistics, and intelligence into a trusted ontology — a connected model of entities and relationships — then analyzing it under strict governance. Wealth management runs the same architecture on different data: custodian positions, fee schedules, trust conventions, beneficiary structures, and examiner-ready evidence.
r.team applies that discipline to fiduciary operations. Not a chatbot bolted onto legacy terminals — a composable intelligence layer that integrates with the CRM, custodians, and reporting you already run, runs private AI models over your firm's data model, and triggers agent pods when events demand action.
Software now has the questions and answers intrinsic.
For decades, software was an empty vessel. It executed logic, but the intelligence lived outside it — a programmer had to anticipate every question, and an advisor, operator, or client had to supply every answer. Every workflow was really a conversation between people, with software merely passing messages between them.
That architecture is obsolete. AI-native software carries both sides of the conversation within itself. It knows what to ask because it understands the domain, and it can answer because it reasons over your data directly. The question and the answer no longer live in two different heads — they coexist inside the system.
This is the first principle r.team is built on: design for intelligence in the core, not for a human patching the gaps. Every model block and agent pod carries its own questions and its own answers — which is exactly what makes each one independently useful, and what makes them compose. You set the intent, the standards, and the boundaries. The system does the asking, the answering, and the acting — inside them.
Frontier intelligence, harnessed — not bolted on.
Composable AI
Blocks, not bundles.
Build your intelligence stack the way you build a portfolio — piece by piece, swappable, yours. A library of AI modules you connect to the systems you already run.
Agent pod blocks
Task-focused agents packaged as independent pods: research, drafting, reconciliation, monitoring. Each pod does one job well and hands off cleanly to the next through defined interfaces.
Event-triggerable by design
Blocks and pods wake on events — a custodian file lands, a client record changes, a threshold trips — run their job, and go quiet. No polling, no batch windows, no babysitting.
Why composable AI.
Most advisor platforms are all-or-nothing — you adopt the whole suite, migrate everything, and bend your workflow to fit theirs. r.team is built the opposite way. Each capability is an independent block that talks to the others through clean interfaces. Start with one module, prove it out, then compose the rest around it on your timeline. No forced migration, no vendor lock-in, no paying for shelfware.
Think of a monolithic platform as a packaged fund — you take the whole thing as designed, including the parts you don't want. Composable is the SMA, build-your-own approach — you select the components that fit your mandate, swap holdings as conditions change, and you're never locked into someone else's bundle. Your intelligence infrastructure can finally work the way you already think about portfolios.
The intelligence stack
Integrate. Analyze. Act.
Three layers — the same pattern used to turn operational noise into mission-ready decisions, applied to wealth management signals and fiduciary governance.
Integrate
Connect custodians, CRM, trust accounting, and reporting into a unified ontology — accounts, entities, fees, beneficiaries, and conventions your operators already trust.
Analyze
Private Reasoning Engine blocks query and analyze that connected data in plain language — surfacing exceptions, concentrations, and variances before they reach a client statement.
Act
Agent pods execute governed workflows on events — overnight reconciliation, fee monitoring, exception surveillance — with full audit trails and human-in-the-loop approvals.
Explore the product blocks that implement each layer. View products →
Forty years of turning signals into decisions.
Got-Data's discipline began with pattern recognition in operational data — visual matching systems, scanner-driven inventory, and the first online SQL on mainframes — decades before "AI" was the word for it. That same thread runs through enterprise trust accounting platforms and, today, graph intelligence over fiduciary accounts.
r.team productizes it as composable AI blocks for wealth management: frontier-class capability running privately, under your controls, with the auditability examiners expect. Intelligence innovation — not intelligence rented.
Trust accounting core
Foundation blocks: the trust accounting core, ready on day one.
Every engagement starts from proven foundation blocks that implement the conventions legacy platforms took decades to accumulate — so the parallel run reconciles against real fiduciary behavior, not a prototype.
Statement Freezing
Lock a statement cycle's data at cut-off so reprints, audits, and regulator requests reproduce exactly what the client received.
Accruals & As-Of Dating
Interest and dividend accruals with day-count and convention parity, plus as-of transaction processing that correctly restates affected balances, accruals, and fees.
Custody Services
Custodial feed ingestion, position and cash reconciliation against the custodian, and corporate-action processing.
Fee Calculation
Tiered schedules, prorations, minimums, and split fees, reconciled to the penny against the legacy fee cycle.
Principal & Income Accounting
Dual cash-bucket tracking for fiduciary accounts, with income sweeps and distributions.
Tax-Lot Accounting
Lot-level cost basis with configurable relief order and full disposition history.
Pricing Hierarchy
Multi-source pricing with fallback rules and stale-price handling, matched to the legacy hierarchy during validation.
Statement & Reporting Engine
Statement-level totals with rounding parity, plus examiner- and audit-ready output.
Document Abstraction
Reads trust instruments and proposes governing terms for human review — cited, versioned, and approved before anything reaches the system of record.
Every foundation block carries its own parity specification — the documented legacy behavior it matches, and the signed decisions where it deliberately improves on it. How validation works →
See AI in action on your data.
Request a focused demo — tailored to your platform, use cases, and governance requirements.
Request a demo