Product
How NeuSyn works. Orchestration you can trust.
NeuSyn is an AI-native workflow orchestration platform. Model a multi-service process once, connect any number of backend services through configuration, and every run is resilient, streamed in real time, and fully audited — deployed in your environment, on your own AI.
Not batch automation
A different layer entirely.
Scheduled, self-healing jobs keep infrastructure running. This is a different layer. When a single request has to touch many services in the right order — verify a document, screen against a watchlist, score risk, call a model, return a decision — you need an engine that coordinates the whole flow, recovers from failures along the way, and returns one authoritative answer.
- —Coordinates a multi-step process across many services, not a single scheduled task
- —Built for requests that need a decision and an outcome, not just a job that runs to completion
- —The process diagram is the spec — readable by the business, runnable in production
How it works
Model it, connect it, run it.
Model the process.
Define the workflow as a standard, readable diagram in BPMN 2.0 — the open industry standard for process diagrams: the steps, the branches, the decision points. The diagram the business reviews is the one that runs in production.
Connect the services.
Point each step at the backend services and decision rules it needs through configuration, not custom application code. Adding or changing a service call is a configuration change.
Run it.
The engine coordinates the flow across every service, retries and isolates failures automatically, streams progress back as each stage completes, and records every step — returning one clear business outcome at the end.
What it does
Built around five properties.
Change at configuration speed
New integrations and process changes are configuration, not custom code. A new service call, a new routing rule, a changed decision threshold ship in hours, not sprints. Decisioning, routing, and validation live in a configurable rules layer — and business and compliance teams read the same diagram engineers run.
Orchestrate the whole service landscape
Compose calls across any number of backend services and return one clean result from one call. Run steps in parallel, branch on outcomes, merge results, and iterate over collections — all declared in the diagram. Because regulated processes can run long, interim results stream back as each stage completes, so progress reaches the user instead of waiting on the slowest dependency.
Resilience configured per workflow
Retries, timeouts, and circuit breakers — automatic switches that stop calling a failing dependency before it drags down the whole process — are set per workflow, per tenant, and platform-wide, never hardcoded. A failing dependency is isolated, not catastrophic, and the exact retry and timeout policy for every step is inspectable.
Traceability built in
Audit is a structural part of every execution, not a bolt-on. Every request, run, and step is recorded with structured failure attribution — a complete, time-ordered account of what happened, when, and why something failed. Configuration is validated before the platform accepts traffic, so malformed mappings and schemas are caught at startup, not in production.
One platform, many tenants
A single instance serves many tenants with full isolation — separate configuration, service levels, routing, and audit trails. Common processes are defined once as shared workflows and governed centrally, then reused across tenants instead of forked, so a single update reaches every tenant at once.
Why this
Not a generic orchestrator.
- Configuration, not code
Engineers add and change workflows through structured configuration — not a developer SDK, and not a visual builder aimed at non-technical users. Onboarding a new workflow is measured in hours.
- Multi-tenancy as a first-class property
Isolation of configuration, service levels, routing, and audit per tenant — designed in, not bolted on after the fact.
- Audit as a structural outcome
A complete, time-ordered record captured as the process runs, with explicit failure attribution — not reconstructed afterward from scattered logs.
- Standards-based and portable
An open, vendor-neutral process notation, not a vendor-locked DSL — built for long platform horizons.
AI in production
A model is a step, not the system.
Putting a model into a decision is the easy part; making that decision dependable is the hard part. Here, model inference is a configured step inside a governed workflow — surrounded by the same retry logic, audit trail, and streaming the rest of the process uses. Low-confidence outputs route to human review, and the audit trail records which model version and confidence drove each outcome. AI is one governed, reversible step inside the flow, not an autonomous black box.
Where AI is headed
AI that builds, tests, and connects your workflows.
NeuSyn aims AI at the two slowest parts of enterprise integration — testing workflows and building connectors — with every generated artifact validated and human-approved.
Any AI/ML model served over gRPC or as a queue worker plugs into a workflow like any other step. Add scoring, classification, extraction, or risk models into a flow and route on their output — no platform code changes.
A domain-specialized assistant that knows the platform's conventions and your live configuration. Phase 1 (read-only): ask why a transaction failed or what a workflow does, and get grounded answers from your own data. Phase 2 (draft, human-approved): describe a process in plain language and get a starting workflow, decision logic, and suggested mappings — every output reviewed before it goes live.
NeuSyn reads a workflow and generates an automated test suite — success path, branch conditions, and failure modes — so every workflow ships with coverage no one hand-wrote. Tests regenerate as the workflow changes, so coverage never drifts.
Downstream systems each speak a different language — REST, SOAP, gRPC, queues, file drops, bespoke vendor APIs. Describe the interface and AI generates the connector that bridges to it, turning weeks of integration into a reviewed, generated worker. New systems onboard in days, not sprints.
- Tenant-scoped — only ever your own data.
- Never fabricates — queries live platform data through tools instead of guessing.
- Human-in-the-loop — generated artifacts are validated against platform schemas and approved by a person before going live.
- Fully audited — every AI-assisted change is logged distinctly from human edits.
Deploy in your environment
Runs where your data lives. Uses the AI you already trust.
NeuSyn deploys inside your boundary and runs against your own AI infrastructure — so sensitive data and model inference never leave your environment.
In your boundary
Deploy to your own cloud or VPC, your on-prem data center, or a fully air-gapped network. The platform meets your data where it has to stay.
Bring your own AI
Your models — served in your environment over gRPC or as queue workers — plug in as workflow steps. Data and inference never leave your boundary.
Ships as containers
A containerized, Kubernetes deployment your team operates and controls. No vendor round-trips; nothing phones home.
Boundary-aware
Deploys within your FedRAMP or DoD IL authorization boundary, so your data and audit trail stay in-boundary — a direct fit for federal, defense, healthcare, and finance.
For regulated work
Accreditation-aware from day one.
The properties accreditors ask about — a complete audit trail captured as each step runs, tenant isolation by design, inspectable resilience policy, and configuration validated before anything runs — are structural here, not bolt-ons. Consistent with how we deliver everything else: the engineers who design the system stand behind it.
Federal delivery →Get in touch
Tell us about your environment.
We take engagements selectively and go deep on each. Reach out and we'll set up a working session.