Nx·Studio turns routine operations into digital workers your enterprise owns, validated before they go live. Nx IDE adds governed AI capacity to your engineering organization, with a person approving each editor action by default. The Cognitive Engine automates a routine decision only after passing an exam on cases it has never seen.
Digital workers absorb routine volume while your people handle the exceptions, so growth no longer has to mean proportional hiring.
Running cost and hours saved are estimated for every design, and the plan adds a payback estimate. Each option is scored on synthetic data before capital is committed.
The generated source code belongs to your enterprise and runs on your infrastructure. We charge no per-decision fee and no runtime license; your model provider bills AI use directly.
Readable logic, recorded approvals and rules that return the same result every time give a regulated business an explainable record. Finance, healthcare and other regulated sectors are where we concentrate.
Nx IDE gives engineers 31 built-in modes inside VS Code. In Orchestrator mode, a separate tester and reviewer check each change, and a person approves the release.
Teams use the AI providers you already contract with, more than 20 of them, including private and on-device models, and can match a model to each task.
Approval is the default for every editor action, and written rules can allow, hold or block specific actions. Hooks, optional per-task cost caps and one-click rollback complete the controls.
Cortex builds a map of your codebase on your own machine, without network calls, and flags what an edit could break before it is made.
Approval points, confidence thresholds and routes to people can be reviewed before go-live, on the canvas or as business swimlanes, with each approver named.
Every approval is recorded. A tamper-evident run log and field-level data lineage (where each value came from) are optional additions, and auditors can request the audit-trail specification.
Offline, on its own hardware, your security team checks the signed bill of materials, a list of every component. Each export also includes a security threat model.
Validation can run entirely on synthetic data, so an evaluation built from your written description shares no production data and needs no data-sharing agreement.
From a written description to software your enterprise owns, in four governed steps. Each step produces evidence your leadership can inspect.
A paragraph or the guided form is enough to describe a process, with its procedures and policies attached. Optional modules add process-mining logs, existing agent code or BPMN process maps. Anything you share is used only to design your workers, under data-processing terms agreed before material is shared.
A written description alone is enough to begin evaluating, with no production data shared, and validation can run entirely on synthetic data.
An AI business analyst proposes up to five designs, from conservative to cost-optimal, and recommends one. Each option is scored, with an estimated running cost and hours saved. Connections begin from a catalog of thousands of API definitions and starter code for common enterprise systems; your engineers finish and validate each one. A standard web API or an MCP server (an open standard for connecting AI to other systems) puts any other system within reach.
No design is a generic template: each starts from your process or an adapted starter pack, and its blueprint can be read before it runs.
Workers run on realistic synthetic data generated for their own processes, one step at a time or end to end with live approval pauses. Many workers can run together, with time compressed if you wish, using simulated behavior by default or with the real workers instead. As an option, a red-team module attacks them with known techniques.
Every evaluation run receives scores for safety, accuracy and cost, and two versions can sit side by side for comparison before you approve go-live. Step-by-step replay of a past run is an option.
Once you approve, your team exports the complete software project and deploys it on your own cloud or servers. It runs on the AI models you choose, including fully local ones: no vendor runtime, no per-decision fees, no calls home.
What every export containsA signed bill of materials accompanies every export: an inventory of every component that can be verified offline. The Studio adds an itemized ownership report.
Nx·Studio, the flagship, turns routine processes into digital workers you own. Nx IDE adds governed engineering capacity. The Cognitive Engine takes proven routine decisions off the AI bill. The Foundation lets risk and audit verify every worker. Every output, from workers to code and blueprints, remains an asset of your enterprise.
Converts routine work into digital workers your enterprise owns. Running cost and hours saved are estimated for every design before any build; the plan adds a payback estimate.
Governed engineering capacity inside VS Code. Specialist agents plan, write, test and review changes, using the AI model you choose. The editor asks before it acts by default, and shipping waits for your approval at the default setting.
For routine decisions at high volume. It learns only from confirmed outcomes and must pass an exam on unseen cases before it acts; once qualified, it decides in milliseconds with no AI call.
The assurance under every Nx·Studio worker: four mechanisms for predictable behavior, explainable decisions and provable ownership.
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.
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 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.
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.
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.
Many AI agents consult a model on every decision, even routine ones they have made many times, and each call adds cost. The Cognitive Engine sits beside your agent systems and can take those decisions over. It learns only from outcomes your systems or people have confirmed, and must pass an exam on unseen cases before it may take a single decision. After it qualifies, it decides in milliseconds without an AI call, and each rule it learns reads in plain words.
It connects to your agent systems as a separate product, through the decision API of its Studio. So far it has been measured only on simulated businesses with synthetic data.
A model's suggestion is never accepted as evidence.
Every one of 400-plus unseen cases must be answered correctly.
Decisions take milliseconds, and a given release answers consistently.
Unfamiliar cases go to a person, or to an AI model only if you allow it.
Each digital worker is governed by a blueprint, a versioned and readable statement of your business logic: agents and roles, connections, approval points, rules and routing. No part of it lives in prompts or in a vendor's runtime.
Illustrative: a single policy change between two versions, in plain words. One blueprint serves every stakeholder. Engineers compare and merge versions like code, business owners read swimlanes by team, person and system, and auditors receive a summary of exactly what was built. The Studio checks the blueprint on every save and exports it as code for the infrastructure you choose. It is an asset your enterprise owns, not a configuration you rent.
A metered platform earns more every time a worker decides, so its revenue rises with your volume. Nxgyle does not meter decisions. Stated business rules become deterministic checks wherever possible, and those checks never call an AI model. AI estimates put a running cost on each design before any build begins, while the Studio flags steps that could move from AI to a rule.
The AI judgment that remains runs on model accounts you choose, including fully local models, and optional step and time limits apply to every run. Agent systems that see the same decisions all day can let the Cognitive Engine settle the proven routine ones with no AI charge. It is a separate product, not built into Nx·Studio workers.
Your team operates the exported software, so no runtime license and no per-decision meter apply, and decision volume never raises what you pay us. Your model provider bills separately for the AI calls your workers make, so plan that line at expected volume.
Judge the business model, not the pitch. We are paid to design and deliver your workforce, not to run it. A metered vendor earns more as your dependence deepens; our incentives favor your independence. Ongoing support is optional, on your terms.
When a rule runs as a deterministic check, no AI model is called at all, by design rather than by discount.
Every design carries an AI-estimated running cost and the hours it should save, and the plan adds a payback estimate. Test runs then show the actual cost of each run.
Your team operates the software. There is no runtime license, and no Nxgyle meter grows with your workforce.
Take this list into every vendor review. We publish it because we can answer each question.
Before anything runs, can our auditors see what a worker will do, or only dashboards afterwards?
What still runs on the day our contract ends?
Do we own all of the source code, or only a configuration?
If decision volume doubles, does our bill double too?
If the AI model misbehaves, what protects our routine decisions?
Can a cleverly phrased input push a worker outside our policy?
Is the code we evaluate exactly the code we deploy?
Can it be tested on realistic cases before it touches a live system?
Must we share production data to evaluate it?
Who approves what, and does the code itself enforce that approval?
Can our own people verify the no-lock-in claim on our own hardware?
Is every figure in the proposal backed by a stated method?
Our answer to each appears on this page.
In everything we publish about the platform, three words have a fixed meaning:
A software asset your enterprise owns, running one defined business process end to end; not a hosted bot.
The readable, versioned record of a worker's business logic, open to audit before it runs.
The complete source code and its tests, delivered to run on your infrastructure without Nxgyle.
Example export contentsComplete business applications, delivered to run under your own brand.
Owned software assets, each running one defined process by your rules.
Across financial services, healthcare, energy, manufacturing, logistics, legal and automotive.
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.
Nxgyle designs and delivers the workforce end to end, with upgrades available afterward; every deliverable belongs to your enterprise.
Specialist teams design, build and extend agent systems and the software around them, scoped to your technology strategy.
Senior engineers and AI specialists embedded with your teams, adding capacity where your strategy needs it, on your terms.
