Capability is commoditized. Ownership is not.
Anyone can demo an AI agent. What you can't buy off the shelf: workers designed for your business, proof before they go live, decisions your auditors can read, and code you keep.
Workers and software, built to order.
Start from a description of your business, or adapt a ready-made pack from the Studio's gallery. Either way, and the Nx·Studio AI analyst proposes up to five different designs, from cautious to ambitious. Each shows the steps involved, the integrations needed, its approval points and an expected return. Designed and previewed in minutes, not months.
Rules you can rely on
This is where your rules are built in. The analyst turns the business rules you state into fixed checks wherever it can. These checks run instantly, give the same answer every time, and need no AI call. The Studio flags any rule it couldn’t build in.
How the Foundation worksBuilt from your context
Your industry, team, tools, rules and goals shape every design, rather than a generic template.
Describe it your way
A 10-step guided form, a quick paste of your website and notes, a single paragraph, or one of eight starter packs. The form and quick paste take documents too. Optional modules (off unless switched on) read process logs and existing agent code. What you share is used only to design your workers, with a data-processing agreement in place before anything moves.
A full plan, not a diagram
Up to five scored design options. Each has roles, steps, integrations and an estimated return. The Studio checks and corrects them before you see them.
An AI assistant for changes
Optional: describe a change, such as “add an approval step”. An AI assistant proposes it, and you accept or reject it.
A workforce designed for your business, with an AI-estimated running cost for every design option. You can weigh your own costs before you sign anything.
Proof before production.
Each worker can get realistic synthetic data generated to fit its own processes, so it does real-looking work from day one. Test it step by step or end to end, with real approval pauses. Simulate many workers together on an accelerated clock. Optionally, attack it the way adversaries will. See exactly what changed with every version. All of this happens before a single real record is touched.
Rehearsal before reality
Simulation, preview and attack testing (red-teaming) need no production data. This means an evaluation that starts from a description of your process needs no data-sharing agreement.
How the Foundation worksData for each worker
Synthetic records generated to match the data each worker uses. Or import your own samples, up to 200,000 rows per dataset.
A score for every test run
Each test run is scored for accuracy, speed, robustness and coverage.
Compare & replay
Compare runs side by side and replay a past run step by step to see exactly what changed.
Attack and stress tests
Optional attack testing (red-teaming). A simulation lab also flags conflicting updates and broken hand-offs between workers before go-live.
You decide when it goes live, after you've watched it rehearse. Your security team can say yes on day one.
Decisions your auditors can read.
Every worker is a versioned blueprint. Your governance team can compare, test and review it before it goes live, as a visual diagram or as plain swimlanes (who does what, step by step). The blueprint is the record your auditors review. All of it ships as code you own, not a dashboard you rent.
Blueprints you can read
What a worker does, who approves what, and where it escalates are captured as a versioned design, not a tangle of AI instructions. Your engineers branch and merge it like code. Your business reads it as swimlanes. Your auditors get a summary of exactly what was built.
How the Foundation worksVersioned like code
Every save is a version. You can compare and merge versions, restore a saved checkpoint, and see which version each test score came from.
Human approval, built in
A reviewer inbox is built into the worker. Slack, Microsoft Teams and email are optional export settings. You choose who reviews each approval step, such as a role or a team.
Audit trails & lineage
Every approval is recorded. A tamper-evident run log and lineage that traces each data field to its source are optional.
Audit-trail specification on the Trust CenterEscalation as a control
You set a confidence threshold, and the analyst designs the worker to send low-confidence results to a person. The Studio flags sensitive actions that lack an approval step. Approval steps pause the worker until a person decides.
Your auditors read what a worker will do before it does it, and the record they read is yours.
Software you keep and can defend.
Nx·Studio delivers a complete software project that you host yourself, not slides. Exported workers can draw on a catalog of thousands of APIs, a reviewer inbox for human approvals, tests, and optional safety checks and monitoring. You run them on your own systems. No vendor runtime. No per-decision billing. No calls home.
What production-grade means: the code-quality bundle on the Trust CenterProof in every export
Every export includes a signed list of everything inside (a bill of materials), with a verification tool your engineers run offline. It also has a security threat model and an EU AI Act documentation cross-reference. The Studio gives you an itemised ownership report. A sanitized sample is available on request via the Trust Center.
How the Foundation worksA catalog of thousands of integrations
Catalog entries for SAP, Oracle EBS, Workday, Salesforce and ServiceNow, completed by your engineers with each system’s connection details. Workers can also connect to any system with a standard web API, or to an MCP server (the open standard AI tools use to connect to other systems).
Signed and verifiable
A signed list of the software and AI components inside, which your engineers verify offline on their own hardware.
Human oversight in the box
Approval steps pause the worker until a person approves or rejects. Reviewers use the built-in inbox, or Slack, Teams or email if you switch them on at export.
Trust built in
A threat model in every export. Personal-data redaction, policy checks that block disallowed actions, and drift monitoring (detecting when results shift over time) are optional.
Yours to keep
Full source code you host on AWS, Azure, Google Cloud, Databricks, Snowflake, Kubernetes or your own servers. It runs on the AI models you choose, with no license keys and no need for Nexgile while it runs.
Nothing stops working if you stop paying us, and you don't have to take our word for it.
Three ways to get a digital workforce.
Each lane is a real choice with real trade-offs. Here is how we'd describe them, including ours.
Build it yourself on open frameworks.
You own the code. You also own the years of plumbing, controls, and upkeep you'll build before the first worker ships.
Rent a hosted agent platform.
A fast start, but it runs on their systems, behind their meter and out of your view, for as long as you use it.
Generate it and own it.
Designed for your business, rehearsed on synthetic data, delivered as source code in your hands. This is the lane we built.
Artifacts, not adjectives.
The capabilities above produce documents you can read. Here are two illustrated examples of what your team receives.
- Intake: vendor invoices from email and the ERP queue
- Check: purchase-order match, payment terms, duplicates
- − Approval: mismatches go to one reviewer
- + Approval: mismatches above a limit need two sign-offs
- Escalate: low-confidence cases go to a reviewer
Illustrative. A policy change reviewed like code: read before go-live, traceable after.
How blueprints work, on the platform page- Scenarios rehearsed on synthetic data
- Attack tests (red-team) attempted
- Approval steps paused for a human reviewer
- Runs compared and replayed step by step
Generated from a simulated run on synthetic data, ready for your procurement (RFP) file.
Get the sample evaluation report on Nx·StudioThe delivery record.
All counts are computed from the delivery records published on this site. A digital worker is generated software that does a defined job under rules you set. It is delivered as source code you own, not a rebadged bot or script.
The same capability, two very different endings.
- Their servers.
- A meter that punishes success.
- A dashboard over a system you can't see inside.
- Cancel, and the workforce vanishes.
- Your servers.
- Your rules as fixed checks wherever possible.
- Blueprints your auditors read before go-live.
- Cancel, and nothing stops. It's your code.
We charge to design your workforce, not to run it. Our incentives point toward your independence. Ongoing help is optional, on your terms.