Resilience Solutions

Resilience9 · Technical evaluation

How Resilience9 is built, and where it is.

The architecture, the connector categories, the security and observability design, and the roadmap with its build states. Everything here describes design and intent; nothing here is a certification, a customer or a production-scale claim.

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Enterprise integrations

What it will connect to

Connector categories on the roadmap, with the mechanisms each is designed for. We do not name a specific connector until it is built and tested — a list of intended integrations is not an integration.

Categories of system, not named products. No connector is named on this site until it is built and tested — a list of intended integrations is not an integration.
Integration categories, what each would contribute, the connection mechanisms designed for, and build status.
CategoryWhat it contributesMechanismsStatus
CRMCustomer-facing service dependencies and commercial criticalityREST API · OAuth / OIDC · Scheduled syncPlanned
ERPBusiness process ownership and supplier recordsREST API · Scheduled syncPlanned
HR systemsRole, entitlement and key-person dependency contextREST API · OAuth / OIDC · Scheduled syncPlanned
ITSMIncidents, changes, problems and service configurationREST API · WebhookPlanned
Cloud platformsConfiguration, capacity, region topology and posture findingsREST API · Webhook · Scheduled syncPlanned
Security toolingVulnerability, identity and control-effectiveness signalsREST API · WebhookPlanned
Communication platformsNotification and approval routing for workflow stepsWebhook · OAuth / OIDCPlanned

Every connector will carry health status, last sync, failure and retry state, rate-limit handling and credential status, because an integration that fails quietly is worse than one that was never built.

Security and governance

Designed for enterprise security and governance requirements

These are architecture and design commitments, not certifications. Where a certification would be required, we say so rather than implying one.

Identity and access

  • SSO through OIDC or SAML
  • Multi-factor authentication
  • Role-based access, moving to attribute-based
  • Permission-aware AI retrieval

Data protection

  • Encryption in transit and at rest
  • Secrets in a managed vault, never in configuration
  • Tenant isolation across APIs, services, database, cache, files and jobs
  • Automated cross-tenant tests that prove isolation rather than assume it

Interface protection

  • Authentication and authorization on every API route
  • Input validation and output encoding
  • Rate limiting and abuse protection
  • Secure session handling

Governance and assurance

  • Audit logging of material actions, approvals and AI recommendations
  • Static analysis, dependency and secret scanning in the delivery pipeline
  • API security testing and penetration testing scoped for general availability
  • Backup restoration and disaster recovery tested, not merely configured

Multi-tenancy, proven rather than assumed

Isolation is enforced across APIs, services, database, cache, files and background jobs, with automated cross-tenant tests in the pipeline. It is specific, it is verifiable, and it is the first thing a security reviewer asks about.

Observability

A platform that produces recommendations has to be able to explain its own behavior as well as the enterprise’s. These are the signals it is built to emit.

Operations telemetry

Illustrative example · synthetic data

  • Structured logs

    Correlated by request, tenant and actor

  • Metrics

    Service, queue and database level

  • Distributed traces

    Ingestion through scoring to response

  • Integration health

    Per connector: last sync, failures, retries, credential state

  • Background jobs

    Queue depth, duration, failure and retry state

  • AI telemetry

    Model, prompt version, latency, cost and evaluation result

  • Errors

    Grouped, fingerprinted and owned

  • Alerts

    Routed to the accountable team, not to a shared inbox

Illustrative. The traces above are drawn, not measured, and this page has no live telemetry of any kind. What the section states is which signals the platform is built to emit.

Product architecture

Nine layers, one engine

Data sources through ingestion, knowledge, the intelligence engine, AI, recommendation, workflow and the operations surface — with observability across all of it.

  1. 01

    Data sources

    Connected enterprise systems and documents, plus the signals the platform generates itself.

    • Enterprise systems
    • Document repositories
    • Platform events

    In development

  2. 02

    Ingestion

    Connectors, scheduled synchronization, webhooks and event intake, each with health and retry state.

    • Connectors
    • Scheduler
    • Event intake

    Planned

  3. 03

    Knowledge and data

    Relational store, cache and vector index. Documents are parsed, chunked and embedded with their access metadata attached.

    • PostgreSQL
    • Redis
    • pgvector

    In development

  4. 04

    Resilience Intelligence Engine

    The shared engine. Rules, analytics, risk and impact calculation, and the scoring model every module reads from.

    • Rules
    • Analytics
    • Scoring

    In development

  5. 05

    AI

    Model abstraction with routing, fallback, prompt versioning, cost tracking and evaluation, over permission-aware retrieval.

    • AI Gateway
    • Retrieval
    • Evaluation

    In development

  6. 06

    Recommendation

    Turns risks and predictions into prioritized recommendations carrying rationale, evidence, confidence and required approvals.

    • Prioritization
    • Rationale
    • Approval model

    Planned

  7. 07

    Workflow

    Triggers, conditions, approvals, tasks, notifications, retries and a complete execution history.

    • Orchestration
    • Approvals
    • Execution history

    Planned

  8. 08

    User and operations

    The console, the assistant and the administration surfaces, under SSO, MFA and role-based access.

    • Console
    • Assistant
    • Administration

    In development

  9. 09

    Observability

    Structured logs, metrics, traces, integration health, background jobs and AI telemetry across every layer above.

    • Logging
    • Metrics
    • Tracing

    In development

Build state is stated per layer, using the same four states as the roadmap. Nothing on this diagram is claimed as production-proven.

Technical foundation

Application
  • Next.js
  • React
  • TypeScript
  • Node.js
  • NestJS
Data
  • PostgreSQL
  • Redis
  • pgvector
AI and analytics
  • Python
  • FastAPI
  • OpenAI / Azure OpenAI
  • scikit-learn
  • XGBoost
  • Retrieval-augmented generation
Workflow and integration
  • Temporal
  • REST APIs
  • Webhooks
  • OAuth 2.0
  • OIDC
Platform
  • Azure
  • AWS-compatible architecture
  • Docker
  • Kubernetes / AKS
Delivery and operations
  • GitHub Actions
  • Azure DevOps
  • OpenTelemetry
  • Grafana
  • Sentry
Quality
  • Jest
  • Playwright
  • PyTest
  • k6

This is the stack behind our own platform. Product engineering experience is not the same as client delivery experience, and we do not present it as such — our capability on each technology is stated separately on its technology page.

Where we are

An honest roadmap

Resilience9 is in development. Stating that plainly is more useful to you than a launch page that implies otherwise, and any serious evaluation would establish it in the first conversation anyway. No dates are given, because a date we have not committed to internally is not information.

  1. Current

    Foundation and architecture

    Architecture audit of the existing Next.js and Node.js application, data model, multi-tenancy, identity and access. The immediate objective is a stable, secure foundation rather than new features.

  2. In development

    Core modules and intelligence engine

    Completing the nine core modules end to end — user interface, APIs, database, workflows, notifications, reports, permissions, audit and tests — and building the shared Resilience Intelligence Engine and Resilience Score beneath them.

  3. Planned

    AI capability

    AI Gateway, retrieval-augmented enterprise knowledge, the assistant, predictive models and the recommendation engine — each with evaluation datasets and safety monitoring before it ships.

  4. Planned

    Enterprise integration and hardening

    Priority connectors with health monitoring, security hardening, performance testing and production readiness review.

  5. Exploring

    Design partner pilot

    A pilot with a small number of design partners, integrating approved data, configuring users and workflows, and establishing baseline measurements. We are talking to organizations interested in shaping the product at this stage.

Twelve capability areas

Each states its own build status. The Resilience Intelligence Engine sits beneath all of them.

  • Resilience Intelligence Engine

    In development

    The shared engine beneath every module. It consumes signals from across the platform and connected systems, applies rules and analytics, calculates risk and impact, and produces the insights the other modules act on. Building it once and sharing it is what makes the platform a system rather than a suite.

    Step 9

  • Resilience Score

    In development

    A scored view across defined dimensions with weights, thresholds, supporting evidence, trend history and an explanation of how the number was reached. Scores are versioned and reproducible — a score you cannot reproduce or explain is not a measurement.

    Step 10

  • AI Gateway

    In development

    A model abstraction layer across providers, with routing, fallback, prompt versioning, cost tracking, safety controls and evaluation. It exists so the platform is not coupled to one model provider and so AI behavior can be versioned and tested like any other dependency.

    Step 11

  • Enterprise Knowledge & RAG

    In development

    Document ingestion, parsing, chunking, embedding and permission-aware retrieval with citations and freshness tracking. Retrieval evaluates the requesting user's entitlements before content reaches the model — never after.

    Step 12

  • AI Assistant

    Planned

    Lets executives and operators query platform data and approved enterprise knowledge in natural language. Responses respect the user's permissions, cite their evidence and state their uncertainty.

    Step 13

  • Predictive Analytics

    Planned

    Anomaly detection and predictive models for the highest-value use cases, with model version, confidence, accuracy, calibration and drift tracked as first-class attributes rather than as a footnote.

    Step 14

  • AI Recommendations

    Planned

    Turns risks and predictions into prioritized recommendations carrying rationale, supporting evidence, confidence, expected outcome and the approvals each would require before action.

    Step 15

  • Workflow Automation

    Planned

    Triggers, conditions, approvals, tasks, notifications, retries and complete execution history — with approved AI recommendations able to initiate workflows under explicit human authorization.

    Step 16

  • Enterprise Integrations

    Planned

    Connectivity to CRM, ERP, HR, ITSM, cloud and security systems over REST APIs, OAuth and webhooks, with scheduled synchronization. Every connector carries health status, last sync, failure and retry state, rate-limit handling and credential status.

    Steps 17–18

  • Security

    In development

    Encryption in transit and at rest, managed secrets, secure sessions, API protection, input validation, rate limiting, tenant isolation with automated cross-tenant testing, and vulnerability management.

    Step 19

  • Governance & Audit

    In development

    Material user actions, configuration changes, AI recommendations, approvals, workflow executions and administrative events are logged. SSO via OIDC or SAML, MFA, and role-based access moving to attribute-based.

    Steps 7, 20

  • Observability

    In development

    Structured logging, metrics, distributed tracing, error monitoring, uptime, database, background job, integration and AI telemetry.

    Step 21

The other half

What it actually does

The Resilience Score and how it decomposes, how a signal becomes a prioritized risk, how a recommendation carries its justification, and how an approved recommendation becomes work.

See the product experience