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Automation delivered as source code

Automate routine operations on software your organization owns.

Automation delivered as source code

Automate routine operations.Own the software that does it.

Each worker exports as a complete Python project your engineers can read, test and run.The ZIP holds the workflow runtime, tests, a container definition, a threat model and a signed AI bill of materials. You deploy it on hardware or cloud you control, pointed at the AI provider you configure.

Start with one workflow. Compare up to five designs, place approval steps where policy needs them, and rehearse in mock mode, where tool and connector calls are answered without live systems.

Book a technical callSee the security model

Brief to deployable code

  1. 01

    Scope

    Define one process

  2. 02

    Choose

    Pick and tune a design

  3. 03

    Rehearse

    Run synthetic test cases

  4. 04

    Deploy

    Hosted in-house

Scope. Choose. Rehearse. Deploy.
111

Portal builds delivered

Complete applications, engineered end to end and ready for white-label deployment.

333

Worker builds delivered

Each is a standalone service for one defined process, shipped as source with its tests.

17

Sectors in the catalog

Finance, manufacturing and healthcare lead, followed by construction, energy and telecom.

Figures reflect the delivery catalog published on this site. A digital worker is software that carries one business process from intake to outcome, following your rules. You receive full source code, not a repackaged bot or script.

Components

Four components, and where each one runs.

No phone-home in exports. You run what it produces.

01 / 04Exported services

Digital Workers

A standard web service per process, with its workflow runtime and fixed rule checks in the code you receive. Each is rehearsed in mock mode first.

  • Health checks built in
  • Mock mode for all calls
  • Source and tests
  • Container definition

In this example

Three exported workers run side by side: invoice matching, KYC file checks and ticket triage. Each reports its own health and run log, and exceptions route back to the owning team.

  • invoice-match

    Matches invoices to POs

  • kyc-precheck

    Pre-checks KYC files

  • ticket-triage

    Routes support tickets

Your cluster

Exceptions to owners

Inspect the worker runtime
02 / 04Design-time studio

Nx·Studio

Turns a process spec into a versioned workflow. It returns up to five designs scored on six weighted criteria and recommends one.

  • Five design archetypes
  • Synthetic test datasets
  • Versions you can diff
  • Build-checked export

In this example

A single-sentence request turns into three workflow steps with a coded policy rule. It runs against synthetic expense reports, and every edit is versioned.

Validate expense reports against policy

  1. Ingest

    From the expense system

  2. Rule step

    Spend-limit checks

  3. Route

    Approval gate: finance

Version 4 on recordBranch, diff, merge
Go deeper on Nx·Studio
03 / 04VS Code extension

Nx IDE

In VS Code, 31 modes split the work: Plan drafts, Code edits, Tester and Reviewer check. The editor asks first by default; the command line approves unless told to ask.

  • Allow, ask and deny rules
  • One-click checkpoints
  • Local models supported
  • License key checked offline

In this example

Plan maps a payments SDK upgrade, Tester and Reviewer check it independently, and the Shipper commits to a feature branch once an engineer approves.

sdk-update.planAwaiting approval
# Plan: payments SDK update
scope:
why: security notice
model: per-mode setting
steps:
- pin the new release
- patch 3 call sites
- run unit + e2e
ship: feature branch

Modes assigned

Plan

Plans; edits no code

Tester

Read-only on source

Reviewer

Fresh context

Per-task cost meterCommit on approval
Go deeper on Nx IDE
04 / 04Decision layer

Cognitive Engine

A decision-layer prototype that learns rules from verified outcomes. Each rule must pass 400+ held-out cases with six built-in reasoning checks agreeing, then answers with no AI call.

  • A statistical confidence bar
  • Six reasoning checks agree
  • People take unfamiliar inputs; AI route opt-in
  • Each answer cites its rule

In this example

A pump vibration reading arrives. A qualified rule answers in milliseconds with no AI call; inputs outside the trained range go to an engineer for review by default.

Is pump vibration in band?In band

  • Promoted rule

    Answers in milliseconds

  • Optional AI route

    Only if allowed

  • Human review

    Engineers decide

TrainQualifyServe
Go deeper on the engine
What your engineers inherit

Instrument one process before you scale.

Start with a process whose volumes and handling times you already log. Dry-run the worker in mock mode, then compare its traces and model costs with today's numbers.

01 / SPEND

Lower the cost per transaction

Rule steps run as plain code with no model call, so AI spend sits only in the judgment steps. Every run keeps a log of its token and cost totals.

Read the cost model
02 / VOLUME

Absorb growth with your current team

Each worker is a standard web service your platform team builds from the included container definition and scales like any other. Staff take the cases it routes to a sign-off gate.

Follow the build steps
03 / TESTING

Prove it before production

Trials run in mock mode on per-worker synthetic datasets, with coded rules and sign-off gates active. Each trace shows actual model spend before any live connection.

Inspect the controls
04 / PORTABILITY

Run it without vendor lock-in

The export is a complete codebase with tests, a container definition and a README. No license check, no Nxgyle endpoint, and each step points at the AI provider you pick.

Compare hosting models
Illustrative example

One process, modeled end to end.

Consider an accounts payable queue of 2,000 supplier invoices a month, each matched to its PO by hand in about six minutes. The queue absorbs 200 hours of manual work a month.

INPUTS USED
2,000invoices / month
6 minmanual match
200 hmanual hours
01 / RULE PATH
1,500

Invoices settled on the rule path each month

Assumes 75% clear the coded matching checks.

Cleared on the rule path75%
02 / QUEUE
500

Exceptions held at a sign-off gate

The remaining 25% wait for a person to decide.

Held at a gate25%
03 / HOURS
150

Manual hours a month taken off the queue

Gross, before build, review, hosting and model costs.

Portion of 200 hoursup to 75%

Illustrative arithmetic, not a client result. It assumes three of four invoices pass the matching checks and each saves six minutes. Build, review, hosting and model costs are left out; check every input against your own logs.

How the build runs

From process spec to a container you build.

Every stage leaves an artifact your engineers can inspect: a spec, a versioned workflow, test logs, then source.

  1. Step 1 / Scope

    Capture inputs, outputs and policy.

    Define what comes in and what must come out, then attach the PDFs or spreadsheets that govern the work. Thresholds in those files become draft rules for your team to accept or amend.

  2. Step 2 / Choose

    Pick a design, then edit its steps.

    Up to five designs arrive, each scored on six weighted dimensions with an estimated monthly run cost. Edit steps, rules and approval points on a versioned canvas.

  3. Step 3 / Rehearse

    Run it in mock mode first.

    Test runs use synthetic datasets, and mock mode answers every outside call with normal, empty, error or timeout responses. Each run leaves a log with its AI cost.

  4. Step 4 / Deploy

    Build the container and run it.

    A build check compiles the workflow before any ZIP is written. Your engineers wire credentials from your secrets backend, build the image from the container definition and run it without Nxgyle.

Why agentic AI projects fail
Analyst prediction / 2027

Gartner predicts that over 40% of agentic AI projects will be canceled before the end of 2027. Its reasons: costs that escalate, business value that stays unclear, or risk controls that fall short.

— Gartner press release, June 2025 (paraphrased)

Three failure modes, three mechanisms.

01
Escalating costs

Test runs meter AI cost per step, before you commit.

02
Unclear business value

Score designs with golden-dataset and A/B runs first.

03
Inadequate risk controls

Rule steps and approval gates pass a build check before export.

No license check, no phone-home: the exported code runs without Nxgyle.

Hosted service or owned code

A vendor's runtime, or your own repository.

Compare runtime location, cost scaling, inspection and exit before you commit.

Vendor-hosted
Runtime

Hosted on the vendor's stack.

Cost scaling

Usually metered per seat or call.

Inspection

Logs the vendor chooses to expose.

Exit

Access may stop with the contract.

Self-hosted
Runtime

Your container, your cluster.

Cost scaling

Your compute, model tokens and upkeep.

Inspection

Source, run logs and tests in hand.

Exit

No license check; it keeps running.

Own the repository, and change control is yours.

In exchange, your platform team runs hosting, keys and patching.

Review the pricing model

Complete source. Your own runtime. Every file hash signed.

Each Nx·Studio export carries a signed AI bill of materials: an inventory of models, agents, connectors and dependencies. Its signature verifies offline and proves integrity, not compliance.

What your stack receives

Export workers. Govern agents. Serve decisions.

Digital workers are standard web services exported from Nx·Studio after mock-backed dry runs. Nx IDE runs rule-bound modes inside VS Code. Cognitive Engine answers repeat decisions over its Studio’s API once qualified. Your platform team owns every integration.

01ExportFlagship

Nx·Studio

Your engineers receive a complete Python project: the workflow runtime, a test suite, a container definition, a README and a STRIDE threat analysis, generated from an approved design.

  • A scored design shortlist
  • Four mock response packs
  • One streamed ZIP archive
Inspect Nx·Studio
02Govern

Nx IDE

For your engineering organization: allow, ask and deny rules, hooks on eight task events, and project MCP servers that start only after workspace approval.

  • Parallel tasks, one branch each
  • Optional draft pull requests
  • Offline Cortex code graph
Inspect Nx IDE
03Serve

Cognitive Engine

Applications ask the Cognitive Studio for a decision. Each answer is a plain outcome: a ruling, a referral to a person, a request for missing facts, or a hold until confirmed.

  • Zero model calls once qualified
  • AI help, opt-in per caller
  • Runs on standard CPUs
Inspect the Cognitive Engine
04Audit & Verify

The Foundation

What security can check before deployment: the workflow definition, dry-run logs, the build-check result, a STRIDE threat analysis and a signed AI bill of materials.

  • Run log and cost for every run
  • Optional monitoring export
  • AI-BOM signed by default
Inspect the Foundation
Inspect the architecture
Connectors

Connectors start as code,
finished by your engineers.

Nx·Studio draws on thousands of catalogued API entries and drafts each connection as starter code with its auth flow. Your engineers finish the call and test it; credentials resolve at run time from your own secrets backend.

Microsoft Teams
Slack
SAP
Salesforce
Oracle EBS
Workday
ServiceNow
Email
Nxgyle

Logos show common systems, not partners or customers. Maturity varies by connector; test each in your environment.

The Foundation

What your engineers can inspect.

Models are interchangeable; the engineering around them is not. Four mechanisms make every worker testable, traceable and verifiable by your own security team.

  1. 01
    Rehearsal before reality

    Mock mode before live systems.

    Mock mode answers every tool, connector and sub-worker call with success, empty, error and timeout responses, keeping live systems out of the test. Sign-off gates pause as they would live.

  2. 02
    Rules you can rely on

    Policy expressed as code checks.

    Thresholds and policy limits become fixed rule checks that run without a model call. Each drafted rule records its source and waits for your acceptance.

  3. 03
    Blueprints you can read

    A readable graph, fully versioned.

    The workflow definition reads as a blueprint and ships with the code. Versions can be tagged, compared, branched or merged before you export.

  4. 04
    Proof in every export

    Verify the signed AI-BOM offline.

    Every export carries a signed AI bill of materials that covers every file in the package. The bundled verifier lets your security team check it offline, without us.

Read the architecture What ships in each export
Release gates

Policy as code. Approval as a gate.

Four gates your platform and security teams can test before any live traffic.

Audit the safeguards
Release gate checklist
  • 01 / 04
    MOCK

    Dry-run in mock mode

    Mock mode answers tool, connector and sub-worker calls from happy, empty, error and timeout packs; AI steps still call your model provider. It ships in the export; tests against real systems stay with you.

    4 mock packs, no live systems
  • 02 / 04
    RULES

    Model-free rules

    Rule steps decide without a model call: pass, halt, warn or branch. A lint catches inverted rules; AI steps still need their own tests.

    Zero-token rules
  • 03 / 04
    GATES

    Durable approval gates

    Approval gates hold the run, with its state saved in your database. Resume executes once, and requesters cannot approve themselves.

    No self-approval
  • 04 / 04
    TRACES

    Trace every run

    Every run writes a run log with cost totals, and versions can be diffed. Export to your monitoring stack is optional.

    A log per run
For platform engineering

Your stack,
your network,
your run logs.

What engineering, security and platform teams take on at handover.

Review the Trust Center
  1. 01
    REPOSITORY

    Your repository from day one

    The export is a ZIP of the full project source, ready to commit to your own version control. Access to each worker sits behind your own network controls.

  2. 02
    EGRESS

    No egress to Nxgyle

    Trials run on synthetic or imported sample data, so production records are not required. Deployed workers only call the endpoints you configure, and the optional export to your monitoring stack is off by default.

  3. 03
    SIGN-OFFS

    Gates that survive restarts

    Sign-off gates hold each run until a named role answers, and paused runs survive a restart. Your engineers connect the bundled reviewer inbox before launch; Slack works.

  4. 04
    OBSERVABILITY

    Traces and manifests you keep

    Each run keeps a log with token and cost totals, and Studio keeps every workflow version. The signed AI bill of materials proves package integrity, not certification.

Sector workloads

Designed for sectors with audit-heavy workloads.

Regulated workloads need systems your engineers can trace end to end. Each worker ships with its workflow definition, tests and a STRIDE threat model, runs mock-mode trials before launch and deploys inside your own perimeter.

Financial Services

KYC pipelines, alert queues and rule-driven credit checks.

Healthcare

Authorization packets, records ingestion and roster planning.

Supply Chain

Carrier APIs, replenishment feeds and exception queues.

Manufacturing

Vision-based defect triage, sensor alerts and line schedules.

Illustrative
  • Credit checks
  • KYC document matching
  • Sanctions screening
Sector workloads
Illustrative
  • Encounter note drafts
  • Referral routing
  • Claim edit checks
Sector workloads
Illustrative
  • Carrier rate checks
  • Reorder triggers
  • Forecast drift
Sector workloads
Illustrative
  • Image defect triage
  • Sensor alerts
  • Line scheduling
Sector workloads

Each example shows the kind of worker the platform can generate for that sector. All are illustrations, not client deployments.

Explore all 17 sectors
Common questions

Answers for architecture review.

Bring us your question

A standard web service, delivered as complete source code, that runs one defined workflow. Rule steps decide without a model call, AI steps call the provider you configure, and approval steps pause the run for a person. Health, readiness and metrics endpoints come built in.

Code review

Your process. Your rules.Your code. Your call.

Bring one workflow and its constraints.
Your engineers get a sample export to inspect.

See a live worker demo Read the Trust Center
How our pricing works

  • Buildable source

    Service code, workflow runtime and tests, ready to build.

  • Signed file by file

    Every exported file is hashed into the signed record.

  • Your runtime, your keys

    Your hosting, model provider and secrets store.