The AI-native platform to identify the companies building a more resilient world.
From Public Cloud to Private Fortress
A three-phase architecture evolution for maximum security, control, and scalability
ResilienceAI's infrastructure evolves in lockstep with client trust and regulatory requirements - starting with rapid iteration on public cloud, advancing to isolated confidential computing, and culminating in fully dedicated private deployments.
Phase 1: Foundation
Multi-Tenant Public Cloud
Built on GCP, this phase prioritizes speed and flexibility. A scalable multi-tenant architecture enables rapid iteration and continuous deployment — ideal for onboarding and product validation.
- GCP-native infrastructure with auto-scaling
- Shared compute with logical tenant isolation
- Access to cutting-edge ML/AI services
Phase 2: Isolation
Single-Tenant Confidential Computing
Each client workload runs in a fully isolated enclave. Data is encrypted in-use, in-transit, and at-rest — ensuring zero cross-tenant exposure and meeting the strictest institutional compliance requirements.
- Zero-trust network architecture
- SOC 2 Type II & ISO 27001 aligned
- Dedicated key management per tenant
Phase 3: Sovereignty
Dedicated Private Cloud
For clients requiring maximum control, ResilienceAI deploys a fully dedicated instance within the client's own private cloud or on-premises environment, with full data sovereignty and air-gapped options.
- On-premises or private cloud deployment
- Air-gapped network options
- Custom SLA and uptime guarantees
- Deep integration with existing ERP/CRM/portfolio systems
From Public Cloud to Private Fortress
A three-phase architecture evolution for maximum security, control, and scalability
ResilienceAI's infrastructure evolves in lockstep with client trust and regulatory requirements - starting with rapid iteration on public cloud, advancing to isolated confidential computing, and culminating in fully dedicated private deployments.
Phase 1: Foundation
Multi-Tenant Public Cloud
Built on GCP, this phase prioritizes speed and flexibility. A scalable multi-tenant architecture enables rapid iteration and continuous deployment — ideal for onboarding and product validation.
- GCP-native infrastructure with auto-scaling
- Shared compute with logical tenant isolation
- Access to cutting-edge ML/AI services
Phase 2: Isolation
Single-Tenant Confidential Computing
Each client workload runs in a fully isolated enclave. Data is encrypted in-use, in-transit, and at-rest — ensuring zero cross-tenant exposure and meeting the strictest institutional compliance requirements.
- Zero-trust network architecture
- SOC 2 Type II & ISO 27001 aligned
- Dedicated key management per tenant
Phase 3: Sovereignty
Dedicated Private Cloud
For clients requiring maximum control, ResilienceAI deploys a fully dedicated instance within the client's own private cloud or on-premises environment, with full data sovereignty and air-gapped options.
- On-premises or private cloud deployment
- Air-gapped network options
- Custom SLA and uptime guarantees
- Deep integration with existing ERP/CRM/portfolio systems
Platform Architecture
From raw signal to investor-ready output — four layers, fully automated
Data Integrations
S&P 500 · Climate Data · Private Info
Institutional-grade market, ESG, and proprietary feeds. Each maps to a dedicated signal agent monitored on a recurring schedule.
Signal Layer
Market · Climate · Private Data Agents
One agent per integration — continuously parsing, normalizing, and writing structured data into the Intelligence Engine without manual refresh.
Intelligence Engine
Agents · Data Tree · Interfaces
Model-agnostic agentic core. Diligence, Financial Modeling, and Climate Risk agents produce Unified Resilience Scores exposed via MCP, REST API, and CLI.
Applications
UI · MCP · Agentic Skill
Full-Featured UI via REST API · Embedded tools via MCP (Bloomberg, Notion, Slack) · Custom agent pipelines via CLI.
All layers share the same underlying intelligence engine — identical model versions, data pipelines, and Unified Resilience Score methodology.
Phase 1: GCP Infrastructure — Multi-Tenant Public Cloud
A scalable, GCP-native foundation built for rapid iteration, high availability, and institutional-grade reliability
ResilienceAI's Phase 1 infrastructure is fully GCP-native — leveraging managed services for compute, data, AI, and networking to maximize developer velocity while maintaining enterprise-grade security and observability from day one.
Core Services
Cloud Run
Serverless containerized microservices — auto-scales to zero, VPC-native.
Cloud SQL
PostgreSQL 15 with HA failover, private IP, and row-level multi-tenant isolation.
Cloud Pub/Sub
Durable event streaming bus decoupling data producers from the analysis pipeline.
AI, Networking & Security
Vertex AI
Managed model serving, MLOps, and version-pinned endpoints for all analytical models.
Confidential Computing
Intel TDX / AMD SEV-SNP hardware encryption — data encrypted in-use.
VPC
Private IP-only service mesh with VPC Service Controls and data exfiltration prevention.
Cloud Armor & IAP
WAF, DDoS mitigation, zero-trust Identity-Aware Proxy, and distributed tracing.
All Phase 1 services are deployed within a dedicated GCP project with org-level policy constraints — no public IPs, enforced encryption, Confidential Computing enclaves, and mandatory audit logging across all resources.
Integration Without Disruption
ResilienceAI exposes a composable integration surface — from full-stack UI to headless API — designed to embed into any institutional workflow without re-platforming.
Full-Featured UI
A purpose-built, browser-native platform delivering the complete ResilienceAI stack — data ingestion, agentic analysis, and report generation — through a single, role-aware interface. Optimized for investment analysts, risk officers, and portfolio managers operating at institutional scale.
- Configurable portfolio dashboards with real-time Resilience Score overlays
- Role-based access controls with granular permission scoping
- SAML 2.0 / OIDC SSO and enterprise IdP integration
MCP Integration
ResilienceAI implements the Model Context Protocol (MCP) as a first-class integration primitive — enabling bidirectional intelligence exchange with Bloomberg Terminal, Notion, Slack, and any MCP-compliant host environment. Zero workflow disruption; full analytical depth.
- Pre-certified MCP connectors for Bloomberg, Slack, Notion, and SharePoint
- Real-time signal push via server-sent events (SSE) to existing dashboards
- No-code connector setup for standard integrations
Agentic Skill — Native Workflow Integration
Embed ResilienceAI natively into existing agentic workflows via skill (REST API or as an MCP), consumable by any MCP-compatible orchestrator. Invoke climate risk scoring, financial modeling, and report generation as discrete, auditable operations within your existing agent topology — no re-architecture required.
- MCP-native tool exposure — consumable by any MCP-compatible agent or orchestrator
- REST API with OpenAPI 3.1 spec and webhook event delivery
- Immutable audit log per invocation: inputs, model versions, outputs, latency
All integration surfaces share the same underlying intelligence engine — identical model versions, data pipelines, and Unified Resilience Score methodology. No capability degradation across deployment modes.
Always-On Intelligence
ResilienceAI operates as a persistent, autonomous intelligence layer — continuously ingesting signals, re-scoring positions, and surfacing material changes before your team opens the platform.
Signal Detection
Continuous ingestion across 500+ data sources — earnings releases, SEC filings, climate events, supply chain disruptions, central bank communications, and macro shifts. Every event is timestamped, sourced, and queued for downstream processing in real time.
Autonomous Re-Analysis
Each inbound signal triggers a targeted, model-specific re-run across only the affected portfolio positions and risk dimensions. Portfolio Resilience Scores, climate exposure flags, and scenario outputs refresh automatically — zero manual prompting, zero latency accumulation.
Threshold-Based Delta Alerting
Noise-filtered alerting surfaces only material changes. Analysts receive push notifications when a Resilience Score crosses a configured threshold, a new ESG risk factor emerges, or a counterparty's regulatory status changes.
Immutable Audit Trail
Every re-analysis cycle is logged with full provenance — input data snapshot, model version, inference timestamp, and output delta. Compliance teams receive a cryptographically verifiable, explainable history of every score change.
Autonomous monitoring across all portfolio positions
Median latency from signal ingestion to updated Resilience Score
Manual triggers required — fully autonomous re-analysis loop
Configurable Alert Thresholds
Set per-position, per-sector, or portfolio-wide sensitivity levels. Route alerts to Slack, email, or any webhook endpoint.
Model Version Pinning
Lock analysis to a specific model version for regulatory reproducibility. Compare outputs across model versions on demand.
Your edge isn't just better analysis — it's faster analysis. ResilienceAI ensures your intelligence is always current, never stale.
Accelerating Capital.
Securing the Future.
We look forward to meeting you: todd@resilienceai.ai
Accelerating Capital.
Securing the Future.