Govern cost and risk. The code stays yours.
Four capabilities turn AI spending into an asset the enterprise owns. Each design is costed before any commitment, validated before go-live, open to audit before it acts and delivered as source code your organization keeps.
Every option costed before you commit.
Your teams describe a process in plain language or start from a sector pack. Nx·Studio's AI analyst then proposes up to five designs, from conservative to ambitious, including compliance-first and cost-optimal variants. Every design states its estimated running cost and hours saved, and the plan adds a payback estimate, so the investment decision comes before the build.
Rules you can rely on
Policy becomes code at this stage. Each rule the analyst can encode turns into a deterministic check: the same input gets the same answer, at no AI cost. Drafted rules wait for your acceptance, and any rule it cannot encode is flagged.
How the Foundation enforces itYour operating model first
Each design starts from your industry, operating model, systems of record, policies and targets rather than a generic workflow.
Begin from existing material
Teams can use the 10-step guided intake, upload the company website and internal notes, or summarize the process in a paragraph. Eight sector starter packs offer another way in, with documents added as they go. Optional modules can also learn from process-mining logs or agent code already in use. Your documents are used only for this design work, under data-processing terms settled before work begins.
Scored options only
At most five designs reach the shortlist, each scored and set out with roles, steps, integrations, estimated running cost and hours saved. The Studio checks every one, repairing or dropping any that fail.
Edit by instruction
Optional: ask for an amendment in plain words, say “require approval above a limit”, and the AI assistant prepares it for your decision.
Risk appetite and budget shape the design, not the vendor, and every option arrives with its estimated costs attached.
No go-live without evidence.
Each worker is exercised on synthetic data (realistic records, none of them yours) modeled on the process it will run, long before live accounts or ledgers are involved. Teams can follow it step by step or end to end, approvals included, run several workers together in fast-forward time and, if they wish, add red-team testing. No production record is involved.
Rehearsal before reality
Simulations, previews and red-team exercises can all run on synthetic data, so the evaluation needs no production data. Scoped from a written process description, it needs no data-sharing agreement either.
How the Foundation enforces itData fitted to each worker
Created to match the fields each worker reads and writes, or imported from your own samples, up to 200,000 rows per dataset.
A scorecard for every run
Each validation run is scored for accuracy, speed, robustness and coverage.
Versions compared
Test as many as three versions against one shared scenario, or trace an earlier run's record to locate a change.
Stress-tested beforehand
Optional red-team testing produces a scorecard of attempted attacks. Before go-live, the simulation lab reveals clashing updates between workers and hand-offs that fail.
Go-live becomes a decision you take on evidence, and security review can begin with the first run.
Audit the logic before it acts.
A versioned blueprint defines what each worker does. Before go-live, your risk and governance functions review, test and approve each version, either as a diagram or as swimlanes that assign every task. Your auditors work from that identical blueprint, delivered as source code the enterprise owns rather than a dashboard it rents.
Blueprints you can read
Actions, approvers and hand-off points are written into a versioned design, never hidden in AI prompts. Your engineers can fork and merge it like any codebase, business owners read it lane by lane, and auditors receive a plain-language account of the build.
How the Foundation enforces itEach version on record
A new version is kept with each save. Versions can be compared, branched or merged, named checkpoints restored, and each test score tied to the version that earned it.
Approval rights by role
Every approval step is owned by a role or a team. When an export contains approval steps, it ships with an approvals inbox for engineering to connect; Slack is an option, and email uses one supported service.
Decisions on the record
Each approval is recorded with who decided and when. Options add a tamper-evident run log and lineage that traces each data field to its source.
Read the audit-trail specificationA floor for AI judgment
Set the lowest confidence you will accept from AI steps. Anything under it is flagged by default, or sent on to a person if you prefer. The Studio also highlights sensitive actions without an approval step, and the worker pauses at each approval step for a decision.
Exposure is understood before a worker acts, and the evidence stays with the enterprise.
An asset that outlasts any contract.
The deliverable is a full software project the enterprise hosts itself, not a slide deck. Integration code is drafted from a catalog of thousands of API definitions, and every export adds tests and optional safeguards and monitoring, plus a reviewer inbox when it has approval steps. No vendor runtime, no per-decision fees from us, nothing sent back to Nxgyle.
Review the code-quality evidenceProof in every export
A signed bill of materials, an inventory of every component, travels with each export, along with an offline verifier your engineers run themselves. It also includes a security threat model and an EU AI Act documentation template, and the Studio issues an itemized ownership report. The Trust Center offers a sanitized sample.
How the Foundation enforces itA catalog of thousands of API definitions
Starter code connects to SAP, Oracle EBS, Workday, Salesforce and ServiceNow, and your engineers complete, credential and test each connection. Any system that offers an MCP server (a public protocol connecting AI tools to outside software) or a standard web API is reachable as well.
Contents your team can verify
A signed inventory of every software and AI component, which your engineers check offline on their own hardware.
Final say stays with people
Approval steps are hard stops: nothing proceeds until someone with authority approves or declines. Your engineers connect the reviewer inbox that ships with exports containing approval steps; Slack approvals are an add-on.
Safeguards the board can require
Every export ships with a security threat model. Optional additions cover masking of personal data, policy checks that block disallowed actions, and drift monitoring that warns when outcomes start to change.
Hosting stays your decision
Your own servers or cloud, with optional deployment templates covering AWS, Google Cloud, Azure, Kubernetes, Databricks and Snowflake. The model provider is yours to pick, no license key is needed, and nothing depends on Nxgyle.
The capability stays with the enterprise whether or not the relationship with us continues, and your engineers can verify it.
Build it, rent it, or own it.
Each path carries trade-offs in cost, control and exit. Here is how we weigh them, our own approach included.
Fund an internal build on open frameworks.
You own the code, and every integration, control and maintenance task before the first worker reaches production lands on your teams.
Subscribe to hosted agents.
Quick to launch, yet usually hosted by the vendor, metered on its terms and hard to inspect while the contract lasts.
Design it, validate it, own it.
Designed around your process, validated on synthetic data and delivered as source code the enterprise owns: the route Nxgyle exists to provide.
Evidence the board can ask for.
Each capability yields a written record for your risk, audit and security reviewers. Two illustrated examples follow.
- Intake access requests from the IT service portal
- Check role, manager and the systems requested
- Approval every request goes to the requester's manager
- Approval privileged access also needs security sign-off
- Escalate conflicting role grants go to the risk team
Illustrative. One access-policy change, approved like a code change and open to auditors afterwards.
How blueprint reviews work-
Validation scenarios on synthetic data
Run step by step or end to end, with no production record involved.
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Red-team attempts, where enabled
Optional adversarial tests against the worker before go-live.
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Human approvals exercised
Each approval step pauses the run until a person approves or rejects.
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Run-to-run comparison
Differences between runs are highlighted, and a past run's record can be reviewed step by step.
Produced from a validation run on synthetic data, ready for your procurement (RFP) file.
Sample evaluation report on the Nx·Studio pageOur delivery record.
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.
Where renting and owning part ways.
An AI workforce you own is an asset; a rented one is a subscription.
On the vendor's infrastructure, in most cases.
Commonly tied to a usage meter.
Typically a dashboard, not the logic.
Access usually stops.
On infrastructure you run.
No Nxgyle meter, and deterministic checks cost nothing in AI fees.
Readable blueprints and the source code.
Operations continue on your code.
We are paid to design and deliver your workforce, never for running it.
Your independence is the goal; Managed Delivery is optional and on your terms.