The AI bottleneck is no longer the model.
It is approval.
AlphaBitCore is the AI Control Plane for regulated financial firms.
Run it standalone to govern your existing AI stack — or deploy the Investment & Wealth Workbench for day-one agents, workflows, skills, and MCP integrations on top. One platform. Two ways to start.
{
"type": "TASK_CREATED",
"eventId": "evt_4e82224440…bc0d39",
"payload": {
"sessionId": "305839826911916032"
}
}Every governed AI action authorized, recorded, replayable. Compliance, Risk, and Audit get an execution record they can verify and replay — not a log they have to interpret.
Worked example. Methodology and full citation live on the paper page.
A horizontal Control Plane. A vertical Workbench on top.
AlphaBitCore is the AI Control Plane. Workbenches are preconfigured vertical packages built on it. The Investment & Wealth Workbench is the first available; banking, insurance, custody, and other editions are on the roadmap.
The AI Control Plane.
Govern your existing AI stack. Policy enforcement, tool access, scope and authority controls, sealed evidence, and proof of execution — across the agents, models, MCPs, and orchestration you already run.
Banks, insurers, custodians, clearing firms, asset owners, quant firms, internal AI platforms, and multi-line financial institutions.
The Investment & Wealth Workbench.
23 agents, 95 workflows, 78 skills, 12 models, 10+ MCP connectors — preconfigured for research, portfolio, advisory, and compliance work, with FINRA 2026 alignment and primary financial-data integrations built in. AI that financial professionals use, and Compliance approves.
Asset managers, wealth platforms, RIAs, TAMPs, advisor platforms, research teams, and investment organizations.
Configured by ABC. Extended by your team.
ABC field engineers configure your first deployment — the Control Plane and Workbench against your firm’s policies, data sources, approval paths, and integrations. From there, your platform team adds its own agents, workflows, skills, MCPs, and controls on the same platform.
Every deployment.
Founded by operators from Morningstar ($800M+ P&L, 4,000+ people), IBM Research (30+ patents), RBC Capital Markets, and TD Securities.
Meet the team →- Runs standaloneComposes with your existing AI stack
- ~2.7 msGateway + sealing overhead per execution
- 4.4%Total overhead on 50-way concurrent batches
- 10,000 / 10,000Attack traces rejected. Zero false positives.
- FINRA 2026Agentic AI oversight, mapped in depth
Performance figures (~2.7 ms, 4.4%, 10,000/10,000): preliminary single-node prototype measurements on commodity hardware (n=1,000 trials per cell, 95% CI). Investment & Wealth Workbench: in active design-partner deployments. FINRA 2026: mapped against the 2026 Regulatory Oversight Report. Methodology and full citation live with the paper.
Your pilots work. Your approval process does not.
Your teams can build AI pilots. The hard part is getting them approved for real use across research, advisory, portfolio, compliance, and client workflows.
Before AI can act in a regulated firm, the firm needs to know: who authorized the action, what data and tools were used, whether it stayed within policy, what changed, and whether Compliance, Risk, and Audit can verify it later.
Prompts, policies, and logs don't answer those questions. A control plane that governs execution before it happens and proves it after it happens does.
Built in active collaboration with regulated-enterprise design partners.
The platform is shaped by four design-partner firms that already operate under real regulatory obligation — so the governance surface, the audit posture, and the operational model are shaped by buyers with real constraints, not by what is easy to demo.
Named references available under NDA during evaluation.
Unify. Govern. Prove.
One runtime. Every model, every agent, every tool.
Stop shipping point-to-point integrations and bespoke orchestration for every new model or agent. One governed runtime, one identity plane, one execution surface across your stack.
How unification worksOne policy. One enforcement point. Zero ambiguity.
Policy is enforced at the runtime, not approximated in prompts or sprinkled across wrappers. Scope, authority, and guardrails become structural — not aspirational.
How governance worksEvery governed action authorized, recorded, replayable.
Compliance, Risk, and Audit get an execution record they can verify, replay, and defend — not a log they have to interpret. Every governed execution can be re-run from its sealed event stream.
How proof worksNot observability with extra logging. A verifiable record of what every AI workflow did.
- Formal, not heuristic.
- Five execution guarantees with a soundness proof behind them. Not guidelines. Not best-effort. Methodology and citations on the paper page.
- Runtime, not post-hoc.
- Evidence is produced as work happens. Not reconstructed from logs after something breaks.
- Replayable, not retrospective.
- Any governed execution can be re-run from its sealed event stream and produce the same result. Audit becomes repeatable.
- Enforcement at a single choke point.
- One Gateway. One place policy is evaluated. No side channels. No parallel paths.
A full agent platform. Every upper layer swappable.
AlphaBitCore ships Planner, Orchestrator, Agent Runtime, and a Skills & Tool Registry. Use ours, bring your own LangGraph, Bedrock, Temporal, or MCP, or mix. The Gateway and Event Stream are the non-negotiable control plane every effectful action passes through.
The Investment & Wealth Workbench. A working front office on day one.
The Investment & Wealth Workbench is an optional accelerator that ships on the Control Plane — 23 agents, 95 workflows, 78 skills, 12 models, 10+ MCP connectors, preconfigured for active-equity research, portfolio, advisory, and compliance work. Your CAIO signs the procurement; your advisors, analysts, PMs, and reviewers use working AI on day one. Field engineers configure to your firm; firms extend it over time. Banking, insurance, custody, and back-office firms can run the Control Plane standalone — vertical workbenches for those subsegments are on the roadmap.
Company Profile
Pull entity, business segments, ownership, and key metrics for a single ticker. Foundation for any research agent.
Equity Screen
Describe what you're looking for in plain language. Get a ranked, filterable universe back.
Exposure Decompose
Decompose a portfolio across factor, sector, geography, and characteristic exposures.
Performance Attribution
Decompose returns into allocation, selection, and interaction effects with narrative.
Household Consolidate
Group accounts into household views with ownership, registration, and tax-status detail.
Client Comms Draft
Draft client-facing communication with tone, length, and disclosure constraints honored.
Compliance Scan
Scan content against firm and regulatory policies with rule-level citations.
Approval Queue
Stage items into a tiered supervisory approval queue with role-aware routing.
The Control Plane runs against any AI workload. Without the Workbench.
For firms that already have agents, models, MCPs, orchestration, or internal AI platforms — or whose AI use cases sit outside front-office investment and wealth work — the Control Plane is sold standalone and runs against the stack you already have.
Bring your own stack.
LangGraph, Bedrock Agents, Azure AI Foundry, CrewAI, AutoGen, Temporal, Step Functions, MCP servers, custom agent code — the Control Plane sits beside them, in front of every effectful action. Use ABC’s Planner / Orchestrator / Runtime, or your own. Any layer is swappable.
In your VPC. Or on-prem.
Sidecar to your existing agent stack. No model traffic leaves your perimeter. Integrates with your IdP (Okta, Entra, SPIFFE), SIEM (Splunk, Sentinel, OTel), and PAM (CyberArk, HashiCorp Vault). Customer-VPC and on-premise deployment models for BAA / SR 11-7 boundaries.
Built to govern, not to specialize.
Banking, insurance, custody, clearing, asset owners, quant firms, internal AI platforms, and multi-line institutions run the Control Plane standalone today. Vertical workbenches for those subsegments are on the roadmap; the Control Plane does not require any of them to deliver value.
The Control Plane is designed for regulated AI oversight across SR 11-7, OCC and FFIEC expectations, NAIC model AI rules, NIST AI RMF, ISO 42001, and internal model-risk-management programs. FINRA 2026 mapping is detailed today; other detailed mappings publish as they mature.
Auditable by architecture. Not by afterthought.
FINRA's 2026 Regulatory Oversight Report identifies four categories of risk for agentic AI. AlphaBitCore addresses all four — architecturally, not procedurally. Parallel regulatory mappings — HIPAA, EU AI Act, NIST AI RMF, NAIC model AI rules, ISO 42001, SR 11-7 — are in preparation, targeting Q3 2026 publication.
AI workflows selecting intermediate actions that substitute for human supervisory review (Rules 3110 & 3120).
Every intermediate action requires explicit authorization and Gateway traversal. The event stream records the complete decision chain — supervisory review becomes structurally enforceable, not procedurally aspirational.
Gap between complexity of AI decision processes and the trace logs firms retain. “Outputs alone are insufficient.” (Rule 4511 / SEC 17a-4).
Every event carries identity, scope, authority, decision logic, result, and state delta. The trace is append-only and sealed. Not just the output — the full causal chain Compliance and Audit can replay.
Firms must reconstruct the full chain of AI agent activity, not just final outputs.
Any AlphaBitCore-governed execution can be deterministically replayed from its event stream. Audit moves from log interpretation to execution replay — probabilistic to deterministic.
AI agents acting beyond user's intended scope; regulators require guardrails to limit agent behaviors and decisions.
Authority-separated architecture enforces scope at the runtime level. Denied executions produce zero state change — the formal guarantee that makes guardrails more than a policy document.
Working against a FINRA 2026 exam cycle? The full financial services vertical — the Investment & Wealth Workbench (23 agents, 95 workflows, 78 skills, 12 models, 10+ MCP connectors) ready on day one, with the deep regulatory mapping — lives on the site.
Proof at production speed.
Gateway + sealing overhead per execution. Constant cost: 1.1 ms decision, 0.8 ms recorder, 0.8 ms event construction.
Total overhead on 50-way concurrent batches. Amortizes from 65.9% on single-capability flows as Gateway and Recorder costs distribute.
Compressed per standard 8-event trace. ~330 MB event-stream storage at 10,000 executions/day over 30-day retention.
Injected T2 (Gateway bypass) and T4 (trace mutation) traces rejected. Zero false positives in control set.
Preliminary single-node prototype on commodity hardware (Intel Core i7-12700K, 32 GB DDR5, NVMe SSD, Node.js 20). These measurements validate mechanism feasibility, not production deployment performance. Multi-node overhead and long-context LLM captured-input overhead reported in a companion paper. Read the paper →
One runtime. Five stakeholders. One primary buyer.
Move AI from pilot to production.
Reusable controls across models, agents, tools, and workflows. One invocation contract, one identity plane, one event surface — with per-workflow cost attribution enforced at the Gateway, not estimated from invoices.
For Architecture & CIOShip the pilots your team is waiting on.
For the Head of Research, Head of Wealth, Head of Advisory — the person whose P&L the stalled pilots are supposed to move. The bottleneck is approval; the Control Plane makes approval routine.
For the business sponsorProve what your AI did.
Every governed execution sealed and replayable. Audit becomes repetition, not interpretation — with an architecture aligned to FINRA 2026 agentic AI oversight.
For ComplianceVerify without reconstructing.
For the CRO, model risk officer, and head of internal audit. Test what AI workflows actually did from a sealed execution record — not from screenshots, log fragments, and human explanations. Built for SR 11-7 review, three-lines-of-defense testing, and supervisory examination.
For Risk & AuditGovern your AI infrastructure.
Policy enforced at the runtime, not in prompts. Scope-bound agents. Denied actions produce zero state change — and leave a sealed denial behind.
For SecurityProof of Execution: Runtime Verification for Governed AI Agent Actions.
Rhodes & Kang (AlphaBitCore, April 2026). In press, arXiv publication 26 April 2026. Formal architecture, the invariant set and Prime Execution Model, soundness theorems, the worked example shown above, and preliminary single-node prototype measurements validating mechanism feasibility on commodity hardware.
Most enterprises don't have an AI problem. They have an AI sprawl problem. Models everywhere. Agents nobody approved. Tools without governance. Workflows that can't be audited. We built AlphaBitCore to answer that — not with a stronger log format, but with a verifiable record of what every AI workflow did, who authorized it, and whether it stayed within policy.
Ready to see a provable runtime?
Fifteen minutes. One call. We’ll show you a replayable execution, a FINRA mapping walkthrough, and the benchmark methodology in a private briefing. Then you decide.