Design it. Validate it. Own it.
Nx·Studio converts one defined business process at a time into a digital worker your enterprise owns. Leadership sees each option's estimated running cost and hours saved before committing. The chosen design is validated on synthetic data and delivered as a complete software project your teams run.
Every claim here can be checked: sample export files are available through the Trust Center.
Process brief
“Match supplier invoices to purchase orders and route every exception to finance for a decision.”
Where the enterprise builds owned workers.
A digital worker is software that runs one business process end to end. AI agents handle the repeatable steps, your people approve what needs judgment, and it connects to your systems. Each worker is designed with your teams in Nx·Studio, and every option's run cost is estimated first.
Capability you own, not a platform you rent.
No hosted platform to rent, and no vendor model to bend your operations around. The enterprise receives software built for its own processes, validated before production and handed over as code.
Clear owners from design to operation.
Operations leaders answer for results, architects approve the design and platform teams run it, all in one studio, so accountability is settled before go-live.
Six governed steps from process brief to owned asset.
Describe the process
Set out the process as it operates now, using a guided 10-step form, a pasted brief or a single paragraph; eight industry starter packs offer a head start.
Generate the designs
As many as five candidate designs come back, ranging from cautious to bold, with one recommended. Every option arrives with an estimated running cost and hours saved.
Refine the design
Process owners and architects refine the chosen design together, review it lane by lane, and attach the data and knowledge it relies on.
Validate it
It runs on synthetic data generated for this worker, approval pauses included, before any production system or record is involved.
Export the source code
An ownership screen itemizes what the enterprise receives: the complete software project, its tests, a reviewer inbox to connect, starter connection code and any deployment files you request.
Own and operate it
Your teams run it on your cloud or servers with the model providers you choose. Nxgyle is not required to keep it running: no license check, no per-decision fee.
Built to withstand scrutiny.
As leading AI models converge, the advantage moves to what surrounds them. The Foundation gives every Nx·Studio worker four mechanisms that turn vendor assurances into evidence your organization can inspect.
Validation comes before any live system.
Each worker is exercised on realistic synthetic data generated for its own processes, so it works through real-looking cases before it reaches a live system. Teams can test step by step or end to end, watch approvals pause and resume, and run many workers together with time compressed. Runs can be scored for accuracy, cost and safety. No production data is needed to design or evaluate a worker.
Your policies, enforced as code.
Your organization writes down the rules it must follow. The AI analyst builds them into the worker as deterministic checks wherever it can, and flags any it could not. A deterministic check returns the same result for the same inputs every time and never calls an AI model. Decisions nobody has written down belong to the Cognitive Engine, a separate Nxgyle product that learns from confirmed outcomes.
Nothing hidden in prompts.
Every digital worker is a blueprint rather than a tangle of prompts. It shows each step, who approves what, where cases go to a person and which systems it touches. Every save becomes a version you can compare, branch or merge. Business leaders read the same worker as a swimlane diagram, and the export screen lists exactly what was built. Like everything the platform generates, it belongs to you.
Every component, verifiable.
Before anything is downloaded, an itemized list shows what your enterprise will receive. The package carries a signed bill of materials, a tamper-evident inventory of every software and AI component, which your engineers check offline with the verifier provided. A security threat model and an EU AI Act documentation template complete the set. The worker runs without us: no Nxgyle runtime, no license check, no metering and no calls home.
People stay accountable for judgment calls. Approval steps pause the worker until a person answers. Each export with approval steps comes with a reviewer inbox app, which your engineers connect; Slack approvals are optional. Minimum confidence scores add a second safeguard: below the minimum, the worker warns by default, or sends the case to a person if you prefer.
Built around how the enterprise operates.
There is no fixed catalog to outgrow. The AI analyst examines how your enterprise works, from teams and systems to pain points, objectives and rules, then designs workers to match. Every design defines roles, steps and approval points, and leadership weighs each one's estimated running cost and time saved.
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Four ways to begin
A guided 10-step form, a pasted brief, a single paragraph or one of eight industry starter packs, from month-end close to customer onboarding.
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Run costs compared side by side
Conservative, balanced, ambitious, compliance-first and cost-optimal designs, each scored on fit, simplicity, safety, cost, compliance and scalability, with estimated hours saved.
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Weak designs filtered out
Designs that fail validation are repaired or dropped before leadership reviews them, and the chosen one can be deepened step by step on request.
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Policies made enforceable
Stated rules and uploaded policies become deterministic checks wherever possible, with no AI call, and none takes effect until you approve it.
Workflows leadership can inspect.
Every automation is laid out as a process map that business owners read by team, role and system, so governance never depends on reading code.
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Adjust it without coding
Forty step types for agents, rules, approvals, integrations and loops, plus ready-made patterns, so a design changes without programming.
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Read by the business
Lanes show who or what performs each step, by person, team and system.
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Readiness, measured
The design is checked as it is built, so errors and policy gaps surface early; a readiness score gauges the distance to hand-over.
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Every version retained
Saves are versioned, so teams can compare them, merge changes or reinstate a named checkpoint.
Evidence before any production exposure.
Each worker receives synthetic data generated for its own processes, so leadership sees it handle realistic work before it meets a live system. Evaluation requires no production data to leave your control.
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Realistic data, none of it yours
Records are generated to fit the worker's data model, and teams can also bring sample datasets, with a ceiling of 200,000 rows.
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Testing at every step
Step through the worker, or restart it from a chosen point, then review each input, output and log.
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Approval steps exercised
Test runs pause for sign-off and resume once approved; reviewers accept or reject each simulated action, and reversible ones roll back.
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Whole-operation simulation
A simulation lab runs many workers together on compressed time, with simulated or real behavior, and flags record conflicts and mismatched hand-offs.
Test runs are scored for accuracy, speed, robustness and coverage, and the scores feed an evaluation report for procurement, risk and security review. A sample from a simulated run is available on request.
Request a sample evaluation packOwned outright, run in-house.
Nx·Studio delivers a complete software project, not a set of diagrams. Designs draw on a catalog of thousands of API definitions, which your engineers complete and test. Every exported worker runs on your cloud with the model providers you choose.
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Links to your core systems
Thousands of catalogued API definitions and starter integration code for common enterprise systems, which your engineering team completes and tests. Systems with a standard web API or an MCP server, the open standard AI tools use to connect, are reachable too.
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People decide what matters
Approval steps pause the worker until a reviewer, assigned by role or team, decides. The reviewer inbox ships with the export for your engineers to connect; Slack approvals are optional.
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No single-vendor dependency
Optional infrastructure code for AWS, Azure, Google Cloud, Databricks, Snowflake or Kubernetes, and a choice of eleven model providers, fully local models included.
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Oversight once live
Optional dashboards and alerts, drift warnings when a worker's behavior shifts, and generated runbooks (operating guides) for your teams, each enabled at export.
See the economics before you invest.
“Every decision a rule settles is an AI cost the enterprise never incurs.”
Cost estimated before approval
Each design option carries an AI estimate of its monthly operating cost and of the staff hours saved, and the generation plan adds a payback estimate. Leadership compares options on economics, not features.
Rules with no AI bill
Stated business rules become deterministic checks wherever possible. Nx·Studio also reports how much of each worker returns the same result every time, with ways to raise that share.
No meter once it is yours
The worker runs on your infrastructure under your own model accounts. With no Nxgyle meter, higher volumes never raise what you pay Nxgyle; your model provider bills usage directly.
Decision logic your auditors can read.
Each digital worker is defined by a blueprint: its agents and roles, integrations, approval points, routing and rules, set out for any reviewer to inspect. Nothing sits hidden in AI instructions or inside a vendor's platform.
Saving creates a new version; your engineers compare and combine them just as they do with code, while business owners read the same worker lane by lane. The export screen itemizes exactly what was built: an asset the enterprise owns outright, rather than settings leased inside someone else's platform.
- Supplier invoices — three-way match against the purchase order
- Standard approvals — fixed rule, same outcome on every run
- Payment release — one approver, any amount
- Uncertain cases — passed to a human reviewer
- Supplier invoices — three-way match against the purchase order
- Standard approvals — fixed rule, same outcome on every run
- Payment release — treasury sign-off above $50,000
- Uncertain cases — passed to a human reviewer
Illustrative: one policy change between two versions of a worker, in plain language. In the Studio, version history shows the same change on the worker's design.
Reuse what you already paid for.
Earlier investments carry forward: process logs, existing automation code, process diagrams and the RPA bots already in production.
Process data you already hold
Optional module: upload event logs from process-mining tools such as Celonis and receive a ranked list of automation opportunities.
Prior designs carried over
Optional module: point it at existing agent code or a standard process diagram (BPMN) and receive an editable digital worker.
RPA estates kept in service
Mark the steps your RPA bots already handle, in tools such as UiPath, Automation Anywhere or Power Automate. At those steps a hand-off goes out and your team routes it to the bot; no bot runtime is included.
Process-log discovery and code or BPMN import are optional modules; what data to share is always the enterprise's decision.
Improvements that accrue to you, not to us.
Gains stay in the designs and code the enterprise keeps, and every change is versioned, reviewable and reversible.
Expert judgment, retained
Optional: what reviewers correct during test runs can become curated training data that your organization owns.
Behavior under change control
Each change to a worker is open to comparison, merging or reversal to an earlier checkpoint, and every test score points to the version that earned it.
Upgrades that respect your edits
Optional: an export can carry an upgrade baseline, so a later regeneration merges with your team's changes and flags any conflict.
Savings proposed, not imposed
The Studio studies test runs and proposes changes, such as a lower-cost model or parallel steps; nothing changes until you approve it.
Every stage, in one accountable studio.
Every stage is covered and recorded, from the first process brief to the software your teams run.
AI-assisted design
Designs workers around how the enterprise actually runs.
- Form, brief, paragraph or a starter pack
- Scored, cost-estimated designs, five at most
- Self-checked designs, deepened on request
- Business rules as deterministic checks where possible
Visual design and review
A process map every stakeholder can follow.
- Forty step types on a no-code canvas
- A swimlane per team, role and system
- Checks as you build, scored for readiness
- Versions to compare, merge and restore
Data and knowledge
The information each worker needs, under your control.
- A defined data model per worker
- Generated data, or samples you import
- Documents, knowledge graph and seeded memory
- Included in the export when used
Validation and evaluation
Proof gathered before production.
- Step-level testing from any point
- Approval pauses that approve, reject or roll back
- Many workers simulated together
- Scores, comparisons and an evaluation report
Enterprise integration
Catalogued APIs by the thousand, plus starter code.
- Starter code for common enterprise systems
- Any standard web API or MCP server
- RPA hand-off where no API exists
- Starter triggers for schedules, webhooks and events
People in charge
Accountability stays with your people.
- An approval inbox, wired up by your engineers
- Reviewers by role or team, never self-approval
- Confidence floors that flag or route uncertain cases
- Approval designed in before irreversible actions
Security and compliance
Controls designed in from the first step.
- Redaction of personal data, as an option
- STRIDE threat modeling with each export
- Optional policy steps that block prohibited actions
- An optional governance console with compliance rules
Deployment options
Delivered in the form your teams operate.
- Available as an API, a chat widget or an MCP server
- Infrastructure code for AWS, Azure, Google Cloud, Databricks, Snowflake, Kubernetes
- Eleven model providers, local ones included
- Dashboards, alerts and runbooks on request
Ownership and operation
Software the enterprise owns and can run anywhere.
- Outright ownership of the generated code
- An itemized ownership report before export
- A signed bill of materials, checkable offline
- No Nxgyle runtime, license check or meter
Where Nx·Studio differs.
Each point can be demonstrated to your executive team, not merely asserted.
Workers built from your own business context, not picked from a catalog
Up to five cost-estimated design options, checked before leadership reviews them
Stated rules run as deterministic checks wherever possible, at no AI cost
Synthetic data for each worker, so evaluation exposes no production data
Approval steps that hold the worker until a person decides, tested before go-live
A full version history per worker, to compare, merge and restore
Prior investments reused: process logs, agent code, diagrams and RPA bots (some optional)
Your infrastructure, the model providers you pick, local models included
A signed bill of materials and itemized ownership report in every export
New in Nx·Studio.
- Data
Data each worker can rely on
A data model, generated synthetic data and imported samples, plus documents, a knowledge graph and seeded memories that can ship with the worker.
- Design
Self-repairing designs
A rules-and-decisions step at intake, automatic checking and repair of designs, more detail on request and a readiness score.
- Approvals
Approvals exercised in testing
Test runs that stop for approval and continue afterward, a list of paused runs in the Studio, and simulated actions held for sign-off that can be reversed.
- Business view
A view built for business owners
Lanes by role, team and system, and an export screen that itemizes exactly what the enterprise will own.
- Integrations
Connections inside every worker
Integration code inside exported workers, calls to any standard web API or MCP server, and enterprise authentication from OAuth client credentials to mutual TLS.
- Deployment
Deployment on your terms
Infrastructure code for AWS, Azure, Google Cloud, Databricks and Snowflake, and delivery as an API, a chat widget or an MCP server. Starter triggers cover schedules, webhooks and events.
Switched on by choice.
Modules switched on deployment by deployment
- Simulated attacks (red-team testing) before go-live
- Voice input and an AI intake assistant
- An AI assistant for design changes
- A governance console with compliance rule sets
- Process-log discovery and code or BPMN import
- Formal verification of workflow safety properties, and data lineage tracking
Export options, off unless selected
- Approval delivery in Slack, or by email via a supported service
- Personal-data redaction in workers
- An upgrade baseline for merging later regenerations
Exported workers are Python services today.
Who works in the studio.
From the executive sponsor to the teams who run it.
Executive Sponsor
COO, CFO, unit heads
Set priorities by enterprise value and risk
Architecture Authority
CTO, enterprise architects
Approve designs against enterprise standards
AI Engineers
Data scientists, ML teams
Refine and evaluate each digital worker
Platform Owners
DevOps, IT operations
Deploy, monitor and run workers in production
Portfolio Owner
Program leads, PMs
Deliver the automation roadmap
Department Lead
Business analysts, team leads
Automate routine work in their function
“Who really owns it?” Check for yourself.
Before export, a screen itemizes everything the enterprise receives, with a PDF summary for the board and other stakeholders. The package includes a signed bill of materials, a tamper-evident inventory of every software and AI component, which your engineers can verify offline on their own hardware. It also carries a security threat model and an EU AI Act documentation template (a starting point, not a conformity assessment).
Once exported, the worker owes nothing to Nxgyle. Nothing in it relies on our services, and nothing checks a license, meters usage or reports back. If the relationship with Nxgyle ends, nothing stops working.
Does Nxgyle retain access?
No. Exported workers hold no Nxgyle keys and make no license or phone-home calls. The enterprise holds the credentials and the code, on its own infrastructure.
Are we locked in?
No. The enterprise receives the complete software project: code, tests, deployment files and optional runbooks. It starts with one command or deploys to your cloud, with no proprietary platform required.
What if Nxgyle exits the picture?
Operations continue. Exported workers need nothing from Nxgyle and run on your infrastructure for as long as you choose. A self-hosted Studio, including air-gapped sites with private model endpoints, is offered under license on request.
To judge production readiness independently, request a sanitized export file tree, its README and a test report through the Trust Center.
Ownership, in summary.
Totals are derived from the delivery records this site publishes. Every digital worker is owned outright by your enterprise: software for one defined business process, run within the rules you set, with the source code in your hands.
Nx·Studio creates the digital workers your enterprise owns. Nx IDE adds governed capacity to the engineers who extend them. The Cognitive Engine decides routine cases without consulting AI once it has proved, on unseen cases, that it gets them right.