Synaptiq¶

The AI‑native application platform where conversation becomes computation — and data becomes UI.

Synaptiq is a chat-native, data-driven application platform where businesses define their data, and the system dynamically generates dashboards, workflows, reports, and entire UI experiences at runtime. No manual UI building. No dashboard design. No workflow coding. Just natural language.

Think: Retool for app building + ChatGPT for conversation + Tableau for data visualization — unified into a single AI-native platform where the UI assembles itself.

Why Synaptiq?¶

Modern business software forces users through static screens, rigid dashboards, and complex navigation. Even with AI bolted on, most tools remain fundamentally manual and brittle.

Synaptiq flips this model entirely.

Problem	Synaptiq Solution
Users drown in static dashboards	Dynamic UI generation — ask in natural language, get the exact interface you need
Building internal tools takes weeks	AI-generated applications — describe what you want, Synaptiq assembles it in seconds
Data is scattered across systems	Semantic data layer — define entities, metrics, and relationships; AI reasons over them
Workflows are rigid and coded	Multi-agent orchestration — create complex workflows through natural language
Every user gets the same experience	Personalized, context-aware UX — the UI adapts to the user, not the other way around

Architecture¶

Synaptiq is built as a modular monolith using Spring Modulith with a reactive API layer, event-driven module communication, and a declarative Component DSL that bridges the AI backend to a rich Angular rendering engine.

graph TB
    subgraph Frontend["Angular 21 — Dynamic UI Renderer"]
        UI["Chat Shell<br/>Signals · SSR"]
        DSL["Component DSL Renderer<br/>20+ component types"]
        WFD["Workflow Designer<br/>Visual flow editor"]
    end

    subgraph Backend["Spring Boot 4.0 — Java 21, WebFlux"]
        API["REST API Layer<br/>OpenAPI Contract-First"]
        Auth["Spring Security<br/>JWT · RBAC"]

        subgraph Modules["Spring Modulith Modules"]
            SR["Schema Registry<br/>Semantic data model"]
            CH["Chat Engine<br/>LLM → Component DSL"]
            WF["Workflow Engine<br/>Multi-agent orchestration"]
            TC["Tenant Config<br/>Persona · Guardrails"]
            BR["Branding<br/>Themes · Colors"]
            KB["Knowledge Base<br/>RAG · Vector Search"]
            AC["Actions<br/>CRUD · Enquiries"]
        end

        SpringAI["Spring AI<br/>Prompt assembly · Tool calling"]
    end

    subgraph Data["Data Layer"]
        Mongo[("MongoDB Atlas<br/>+ Vector Search")]
    end

    subgraph LLMs["LLM Providers"]
        Gemini["Google Gemini"]
        OpenAI["OpenAI (BYOK)"]
        Ollama["Ollama (Local)"]
    end

    UI --> API
    DSL --> UI
    WFD --> UI
    API --> Auth --> Modules
    CH & WF --> SpringAI
    SpringAI --> LLMs
    Modules --> Mongo
    KB --> Mongo

How Dynamic UI Generation Works¶

sequenceDiagram
    participant User
    participant Chat as Chat Shell
    participant LLM as LLM (Gemini/OpenAI)
    participant Backend as Spring Boot
    participant DB as MongoDB

    User->>Chat: "Show me a sales dashboard for Q1"
    Chat->>Backend: SSE stream request
    Backend->>LLM: System prompt + schema context + user query
    LLM->>Backend: Component DSL JSON (kpi_card, chart, data_table)
    Backend->>DB: Hydrate item IDs → real data
    Backend->>Chat: Stream hydrated Component DSL via SSE
    Chat->>Chat: Render components (Angular Material 3)
    Chat->>User: Interactive dashboard with suggestion chips

Tech Stack¶

Layer	Technology
Frontend	Angular 21 · TypeScript 5.9 · Angular Material 3 · Signals · SSR
Component DSL	20+ declarative component types · ECharts · dynamic form engine
Backend	Java 21 · Spring Boot 4 · Spring Framework 7 · WebFlux
AI / LLM	Spring AI (Google Gemini · OpenAI BYOK · Ollama) · tool calling
Modularity	Spring Modulith (module boundaries, event-driven, hexagonal)
Database	MongoDB Atlas + Vector Search (reactive driver)
Auth	JWT (built-in) + Firebase Auth (multi-tenant, custom claims)
API Spec	OpenAPI 3.0 · openapi-generator for Java + TypeScript codegen
Build	Nx 22 monorepo · Maven (backend) · pnpm (frontend)
Containers	Docker · Docker Compose

Platform Capabilities¶

Module	Description	Status
Dynamic UI Engine	20+ component types rendered at runtime from AI-generated declarative JSON specs	✅ Stable
Semantic Schema Registry	Auto-inference from document sampling, field-level type/cardinality analysis	✅ Stable
Per-Tenant Branding	Logos, color palettes, fonts, named theme presets, WCAG AA validation	✅ Stable
AI Chat Engine	Streaming SSE responses, Gemini & OpenAI adapters, BYOK support	🔶 Beta
Vector Search	MongoDB Atlas Vector Search with embedding models, RAG integration	🔶 Beta
Agent Workflow Engine	Multi-agent orchestration (sequential, parallel, supervisor, dynamic)	🔶 Beta
Knowledge Base	Document ingestion, vector embeddings, contextual RAG for chat	🔶 Beta
Multi-Tenant Architecture	Subdomain-based isolation, RBAC, per-tenant configuration	🔶 Beta
Auth & RBAC	Built-in JWT + Firebase Auth, scope-based authorization manager	🔶 Beta
Actions Engine	Save items, contact enquiries, CRUD operations — audit-logged	🔶 Beta

Design Principles¶

Principle	Implementation
AI generates the UI	LLM emits declarative Component DSL JSON; frontend renders natively
Secure by design	Backend hydration — LLM never sees sensitive data; no executable code in UI specs
API-First	OpenAPI spec → generated Java interfaces + Angular SDK
Hexagonal Architecture	Domain core is pure POJOs — no framework annotations
Event-Driven	Modules communicate via `@ApplicationModuleListener` events only
Reactive End-to-End	WebFlux + Reactive MongoDB for non-blocking I/O
RFC 9457 Errors	Standardized `application/problem+json` responses across all APIs

Quick Start¶

# 1. Clone & install
git clone https://github.com/spectrayan/synaptiq.git && cd synaptiq
pnpm install

# 2. Start infrastructure
docker compose up -d

# 3. Start backend & frontend
GOOGLE_API_KEY="your-key" AUTH_PROVIDER="builtin" \
  mvn spring-boot:run -f apps/backend/spring-apis/pom.xml -Dspring-boot.run.profiles=dev
pnpm nx serve synaptiq

# 4. Open http://localhost:4200
# Login: admin@synaptiq.dev / admin123

→ Full Quick Start Guide

Use Cases¶

Synaptiq serves organizations across industries that want to replace static dashboards and rigid workflows with AI-native, conversational interfaces.

🏥 Healthcare¶

ABA Therapy Goal Generation — Multi-agent workflow where a supervisor agent coordinates ABA, Speech Therapy, OT, and CBT specialists to generate consolidated 12-month therapy goals with quarterly milestones.

→ Read the Healthcare use case

💰 Financial Services¶

Portfolio Advisory Dashboards — Natural language queries like "Show me my risk exposure" generate KPI cards, allocation charts, and compliance reports dynamically.

→ Read the Financial Services use case

🛒 E-Retail¶

Conversational Catalog — Product discovery through natural language: "Find summer dresses under $50" renders item cards, comparison tables, and personalized recommendations.

→ Read the E-Retail use case

Community¶

License¶

This project is licensed under the MIT License — see the LICENSE file for details.

Built with ❤️ by Spectrayan