One platform.
Describe it, build it, own it.
Nx·Studio generates the digital workers that run your business. Nx IDE is there when you want to build or extend the software around them. The Foundation makes both predictable, explainable, and yours.
Three pillars. One owner: you.
Nx·Studio is the flagship; Nx IDE is the leverage; the Foundation makes both provable. Everything they produce — digital workers, their workflows, and the software your engineers write with Nx IDE — stays with you as code you keep.
Nx·Studio
Describe a business process in plain language. An AI business analyst proposes five worker designs; you refine one on a visual canvas, test it on sample data, and download it as a Python project you keep.
- Generated from your own business description
- Testable on sample data before anything real
- Exported as code you own — no per-run fees
Nx IDE
An AI coding agent for your editor and terminal. It plans, writes and tests code, runs a team of agents on bigger jobs, and by default asks before it acts — on the AI model you choose, including local ones.
- 31 built-in modes, from planning to shipping
- Independent tester and reviewer agents
- Undo any step with automatic checkpoints
The Foundation
Four principles every Nx·Studio worker is built on — tested on sample data, rules before AI, readable workflows, and code you run yourself.
- Rule steps give the same answer every run
- Workflows your auditors read before deployment
- A signed bill of materials in every export
Describe → Generate → Preview → Own
Your business description becomes a working digital workforce — and you own the code. Every step of the loop carries its own proof, in the same breath.
Start from a plain-language description.
Describe how a process works — with a guided wizard, pasted notes or a single prompt. Add documents if you like: they are design-time inputs you choose to share, used only to design your workers, with a data-processing agreement in place before anything moves.
An evaluation that starts from a description needs none of your data — previews run on sample data generated for each worker.
Five designs, made for your context.
An AI business analyst proposes five alternative designs for your process, checks them against around 70 validation rules, scores those that pass and recommends one — in minutes, not months. No worker catalog: every design is generated from your context, with approval steps and tests built in.
Every design is generated fresh from your context, and its workflow is readable — and scored — before it ever runs.
Test it before it touches anything real.
Preview runs the worker with a real AI model on sample data generated for it, showing every step, tool call and cost as it happens. Runs pause at approval steps, and evaluation suites, A/B tests and regression checks catch problems before go-live.
Rerun your saved scenarios after every change and compare the results side by side — rule steps return the same answer every time.
Export the code. Run it without us.
When you're satisfied, export a complete Python project — workflow, runtime, tests and deployment files — and run it on your own infrastructure. No vendor runtime. No per-run fees. No calls home.
See what's in an export on the Trust CenterEvery export ships a threat model and an AI bill of materials with a signed attestation you can verify offline — so you can confirm the package you deploy is the one you reviewed.
The Nexgile difference.
The Foundation is four principles built into every digital worker Nx·Studio generates — so you can read it, test it and run it yourself, instead of taking it on trust.
Prove it on sample data before anything real.
Nx·Studio generates realistic sample data for each worker, or imports the files you choose, and runs the worker on it with a real AI model. You watch every step, tool call and cost as it happens, approve or reject at each approval step, and rerun the same scenarios after every change. Studio doesn't need your production systems or your production data.
Fixed rules stay fixed. AI only where judgment is needed.
A digital worker mixes rule steps that apply your business rules — written in plain English — with no AI call and the same answer every run; AI steps for the genuine judgment calls; gates that check each case automatically for policy, confidence, risk, cost or deadline; and human-approval steps that hand a decision to a person. Every design's readiness score shows how much of it runs on fixed rules.
Your auditors can read it before it runs.
Every digital worker is a flowchart your teams can read — its agents, tools, rules and approval steps — with a swimlane view written for the business. Each change is saved as a version you can compare, fork, merge or return to, and a health check flags gaps before anything is exported. The same workflow file ships inside the export, so what was reviewed is what runs.
Download it. Run it without us.
An export is a complete, self-contained Python project: the workflow, its prompts and code, the runtime that executes it, sample scenarios and tests, and a Dockerfile to run it. Every export also includes a security threat model and an AI bill of materials with a signed attestation your team can verify offline. The code carries no licence keys and makes no calls to Nexgile.
Route low-confidence cases to a human-approval step and they wait for a person instead of proceeding on their own. Approval steps are assigned by role, team or variable — and every approval rehearses in the preview before anything is exported.
Watch a digital worker decide.
Most of what a worker does all day isn't judgment — it's policy. In Nx·Studio you design the two apart: policy becomes rule steps that never call an AI model, genuine judgment goes to AI steps, and anything uncertain goes to your people through an approval gate.
Nine decisions queued from the worker's inbox.
Rule steps
Instant. Identical. No AI call.
AI judgment
Genuinely new cases — on the model you choose.
Human review
Routed for approval — a person decides.
Illustrative scenario — one accounts-payable worker. Your mix depends on the worker you design; what doesn't change is the construction: rule steps never call a model, and a human-approval step puts uncertain cases in front of a person.
Rule steps cost no AI tokens — the model is simply never called. AI spend comes only from the steps that need judgment, billed by the model provider you choose, and every design shows an estimated cost per run before you export it.
How our pricing worksRule steps don't drift: the same input takes the same path to the same answer. When policy changes, you update the rule, rerun your saved scenarios and compare the results — before anything is exported.
A rule step can't be talked out of policy by a clever prompt. Exported workers can keep a tamper-evident audit log, and uncertain cases can be routed to a person for approval.
Read the Trust CenterThe workflow is the audit artifact.
Every digital worker is a readable, versioned workflow — its agents, tools, approval gates, routing and decision rules — not buried in prompts and not trapped in a vendor's runtime. The same workflow file ships in the export.
- Intake: refund requests from email and chat
- Check: order history, payment record, prior refunds
- −Approval: every refund routes to one reviewer
- Low confidence: routed to human approval
- Intake: refund requests from email and chat
- Check: order history, payment record, prior refunds
- +Approval: refunds above a limit route to finance
- Low confidence: routed to human approval
Illustrative. One artifact serves three audiences at once: your engineers version and compare it like code, your governance team reads it before deployment, and your auditors check against it after. Workflows can be tested in preview before they ever run, and export as a Python project for the servers or cloud you choose — an asset you own, not a tenant configuration you rent.
Whichever seat you're in.
Move from managing people to orchestrating outcomes.
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Outcomes, not infrastructure
Describe the work in plain language and get a team of digital workers — and the software to run them — built for your business.
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Proof before production
Watch workers run on sample data and score them in evaluation suites before a single real record is touched.
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Own what you run
100% ownership, self-hosted, no per-decision billing. The economics of software you own — not metered SaaS.
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Built for regulated industries
Explainable, auditable, repeatable decisions — our work concentrates where the bar is highest: finance, healthcare, insurance, and the public sector.
Ship faster with an agent that thinks in your codebase.
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An agent in your editor
Nx IDE plans, builds, tests, reviews and ships — 31 built-in modes, with teams of agents for bigger jobs.
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Any model, your choice
Bring the AI providers you already trust — 26 of them — including local models on your own hardware. Right model for each job, no lock-in.
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Control you can defend
Approval by default in the editor, allow/ask/deny rules, cost limits, protected files and checkpoints you can restore — speed inside boundaries you set.
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Workers as readable workflows
Digital workers are versioned workflows, not brittle prompts — tested in preview and exported as Python your engineers can extend.
Read what a worker will do before it does it.
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Read the workflow before deployment
Every approval gate, threshold and escalation path is legible in advance — and Studio flags any sensitive action left without a human gate.
- Read the audit-trail specification entry
An audit trail you can check
Exported workers can keep a tamper-evident audit log of what they did, and every export ships a threat model and a signed AI bill of materials.
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The package your auditors reviewed
Each export carries a signed hash of its contents, so you can confirm the package you deploy is the one your auditors reviewed.
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Evaluation without data sharing
Previews run on sample data generated for each worker — an evaluation that starts from a description needs none of your data.
Your AI bill shouldn't be a weather forecast.
Metered platforms bill you every time your worker thinks. Ours lets you design the thinking once. You watched it happen above: decisions that follow fixed rules run as rule steps that never call a model, so AI spend goes only to the work that genuinely needs judgment.
That judgment runs on the AI provider you choose, at your rates. Each design shows its estimated cost per run, and every preview run shows what each step cost — before you deploy anything.
Because you export and run the code yourself, there is no runtime license and no per-decision meter — as your workforce scales, there is no Nexgile meter scaling with it. Success never raises what you pay us.
Our business model is the proof. We charge to design your workforce, not to run it. A vendor with a meter profits when you depend on it; our incentives point at your independence — ongoing help is optional, on your terms.
AI tokens for rule steps
By design, not by discount — a rule step never calls a model.
Cost visible up front
Every design shows an estimated cost per run, and every preview run shows what each step cost.
Success never raises what you pay us
You export and run the code yourself: no runtime license, no per-decision meter, no Nexgile meter scaling with your workforce.
Twelve questions to ask any digital-workforce vendor — including us.
Take this list into every evaluation. We wrote it because we're comfortable answering all twelve.
Can your auditors read what the worker will do before it runs — or only watch dashboards after?
If you cancel, what keeps working?
Can you export the source code — all of it?
Is there a per-decision meter, and what happens to your bill when volume doubles?
What happens to routine decisions when the model misbehaves?
Can a clever prompt talk the worker out of policy?
Is the demo the same code as the deployment?
Can you rehearse the worker against realistic work before connecting a live system?
Does evaluation require sharing production data?
Who approves what — and is approval enforced in the code, or in a setting?
Can you verify the no-lock-in claim yourself, on your own hardware?
Does every number in the pitch come with how it was measured?
Our answers are on this page.
What the words mean.
Nexgile is the owned digital workforce platform. Three terms carry precise meaning here:
Digital worker
A complete, owned software system that staffs a process — not a hosted bot.
Workflow
Your business logic as a readable, versioned flowchart your auditors can review before it runs.
Export
A complete Python project — workflow, runtime and tests — that runs on your infrastructure, without us.
Sample repo tree, runbook page, and test-and-coverage reportWhitelabel-ready applications, generated and delivered.
Generated, governed software workers — delivered as owned source code.
Delivered across finance, healthcare, manufacturing, energy, legal, logistics, automotive and more.
Portal and worker counts are computed from our delivery records. A digital worker is a generated, governed software worker delivered as owned source code — not a rebadged bot or script.
Three ways to work with us.
Managed Delivery
End-to-end design and delivery of your digital workforce — we run the build, you own everything we ship.
Bespoke Agentic Engineering
Specialist teams that design, build, and extend agentic systems — and the software around them — scoped to your roadmap.
Strategic Staffing
Senior engineers and AI specialists embedded with your teams, augmenting capacity on your terms.
You generate it. You preview it.
You own it. You run it.
See a digital worker designed, tested on sample data and exported — live, in one session.