Reactryx
Your dashboards show you numbers. They can't tell you why. Reactryx unifies your live data, your AI, and your institutional knowledge into one reactive graph — so the answer comes to you, in seconds.
Institutional · Real-Time · Governed
Built for data-intensive teams
From Dashboard to Intelligence Engine
Trading desks drown in data but starve for insight. When PnL drops, the answer is buried across prices, vol surfaces, Greeks, and earnings calendars — scattered over a dozen screens and systems. By the time you find it, the market has moved.
Today's tools force a false choice: fast but dumb, or smart but slow. Streamlit and Dash re-run the entire pipeline on every click. Palantir and 3forge take months to integrate and millions to license.
Reactryx is the third option. A signal-driven dependency graph propagates every change automatically, while an AI agent reasons over that graph to answer the questions you actually ask: Why is my PnL down? Which positions face tomorrow's earnings? What happens if rates move 50bp? The answer arrives grounded in your live data — not a stale snapshot.
And it isn't only for trading desks. Every data-intensive organization pays the same tax — data scattered across lakes, warehouses, and vendor APIs; schedules fragmented across cron, Airflow, and dbt; and the reasoning behind half the architecture walking out the door when a senior engineer leaves. Reactryx turns that chaos into one living, queryable graph.
“Not catalogs. Not docs. Real institutional memory connected to runtime.”
Built for Real-Time Decisions
Everything a desk needs to understand its book the moment it changes
Reactive DAG Engine
- Signal-driven dependency graph — every change cascades to its dependents automatically
- No polling, no manual refresh, no full-pipeline reruns
- Sub-second, flicker-free updates with a 500ms coalescing throttle
- Per-widget undo/redo with 20 slots of history — experiment freely, revert instantly
AI Intelligence Engine
- Right-click any cell and ask "Explain this" — get causation, not just a number
- A 48-tool agent controls every chart and grid through natural language
- Graph-aware agents explain lineage, run quality checks, and propose new nodes
- Works with Anthropic Claude, OpenAI, and Google Gemini
Built for Scale
- Handles millions of rows with AG Grid Server-Side Row Model
- Auto-switches from client to server rendering as datasets grow
- Professional financial grids: heatmaps, conditional formatting, OHLC, sigma bands
- Scale that previously demanded an enterprise platform
Connect Any Source
- Snowflake, Databricks, KDB+, Kafka, DuckDB, Delta Lake, and REST APIs
- Push sources stream straight into the graph as reactive inputs
- Optimized polling makes pull sources feel like real-time push
- Pluggable executors run nodes inline, in parallel, or on remote compute
Enterprise Entitlements
- Row-, column-, and node-level access control defined in plain YAML
- Chinese walls and data masking enforced by the graph on every read
- Zero custom security code — compose access from boolean rules
- Two-layer architecture: 50 users share one query, not fifty
Deploy in Minutes
- Runs as a single process — no Docker, no Kubernetes required
- One command on a laptop or commodity hardware; scales to AWS or Cloudflare
- No separate analytics layer burning cloud compute credits
- Full source, standard tooling, no vendor lock-in
Four Layers, One Substrate
Each layer is a product on its own. Together, they're a category nobody else can assemble.
1 · Reactive Graph
- Wrap every data source and scheduler — no migration required
- Refreshes propagate automatically; stale data flags itself
- "If X breaks, what else breaks?" becomes a one-click query
2 · Widgets
- 50+ grid and chart utilities, one right-click away
- AI is a peer that acts through the same typed dispatcher you do
- Compose whole dashboards from a sentence and a five-minute review
3 · Knowledge Context
- The meaning behind every node: methodology, decisions, incidents, owners
- Bi-temporal — the graph remembers what was true on any given day
- Knowledge stops walking out the door when people leave
4 · Skills
- Reusable, governed AI workflows authored by your power users — not engineers
- Pursue a goal and figure out the steps, grounded in your real systems
- Every run is a transcript with full provenance and audit trail
Smart and Fast — No Compromise
The intelligence of Palantir, the simplicity of a script you control
Answers, Not Numbers
- Causation chains grounded in your live dependency graph
- Ask in plain language; get an auditable, multi-step explanation
- Stop hunting across screens — the insight comes to you
Enterprise Power, Lean Cost
- No million-dollar licenses, no months-long integrations
- Runs on hardware you already have
- Deploy in minutes, expand as needs grow
AI You Can Audit
- Every AI action is a structured, logged, reversible tool call
- Read-only by default; new nodes ship as human-approved proposals
- A full audit trail — governance built in, not bolted on
Measurable, Day One
The graph IS the documentation — so the work teams dread becomes a query. Here's what changes when your runtime and your knowledge live in one place.
Onboarding: days → minutes
New joiners self-serve methodology, owners, and examples instead of mining Slack
Triage: 90 min → 5 min
The graph shows the failed dependency and the runbook that fixed it last time
Audits: weeks → same day
Model Cards, lineage, and validation status export straight from graph state
Vendor impact: one screen
"Vendor X changed pricing — what breaks?" is a traversal, not a half-day hunt
Open Stack, No Lock-In
Reactryx is built on a pure, open-source foundation — full Python source code and standard tooling, so your team can customize anything.
Governance Built In, Not Bolted On
The same rules govern your people and your AI — because for regulated desks, control is the product
Entitlements by Design
- Row-, column-, and node-level access defined in plain YAML
- Chinese walls and data masking enforced by the graph on every read
- Zero custom security code to write or maintain
Complete Audit Trail
- Every action — human or AI — is logged with full provenance
- Change-level taxonomy gates risky operations and requires approval
- Environment-aware: dev and prod follow the same rules
Constrained, Auditable AI
- Read-only by default — the AI can't escape into arbitrary code
- Changes ship as human-approved proposals, not silent mutations
- Agents act through the same typed, governed surface your people use
Your Data, Your Infrastructure
- Self-hosted as a single process — data never leaves your environment
- Bi-temporal history: reproduce exactly what was true on any date
- Compliance evidence — Model Cards, lineage — exported straight from the graph
See Reactryx on Your Data
We're partnering with a small group of design partners to put Reactryx to work on real institutional data. Book a walkthrough and we'll show you your stack come alive — the graph, the AI, and the governance, running against data that looks like yours.