S8 Intelligence

One workspace for the whole company.

Company as code.
Design. Validate. Launch.

One machine-readable model connecting strategy and customer journeys with processes, architecture and working systems.

↓ 47% T2DeliveryT2Delivery (Time to Delivery): time from business task to working software. Reduced by 47% in synthetic tests.What the metrics mean ↓
S8 Intelligence / WorkspaceConcept diagram / order payment

01 / business need

Complete a payment

Customer goal, business rules and success metrics.

02 / shared model

Keep the context

Capabilities, requirements, systems and data connected.

03 / architecture & artifacts

  • C4 architecture
  • API & data schemas
  • Access policies
  • Outcome metrics
Business intent → machine-readable model → executable artifact

The platform

A shared model for every change.

Bring business knowledge into engineering decisions. S8 links what the organisation needs with the systems and artifacts that implement it.

01

Find the context

Trace requirements back to business capabilities and customer journeys.

02

Understand the impact

Explore the systems, data and dependencies affected by a change.

03

Generate the foundations

Turn structured descriptions into related technical artifacts.

Practice in large organisations

Where the approach creates value.

Top-5 retail bank

↓ 20%

Epic lead time across Discovery and Refinement.

Largest bank in Russia

65%

Of service code generated from domain models.

Largest bank in Russia / pilot teams

↓ 40%

Development time for typical services in pilot teams.

Cases under NDA. Figures apply to the specified stages and pilot teams. S8 synthetic-test results are shown separately below.

Synthetic test results

What we have achieved.

Product / T2Delivery

47%

T2Delivery is the time from a business task to working software. Relative scale: baseline 100 → result 53; this illustrates the reported 47% reduction, not measured hours.

Operations / CFR

5%

CFR (Change Failure Rate) is the proportion of deployments requiring an urgent fix or rollback. 5% is a test result, not a 95% reduction in risk.

Engineering / application scaffold

10-15 min

Time to generate an application scaffold from a prepared model. Integration, business-specific code and production validation are outside this interval.

Results achieved in synthetic tests. Production outcomes depend on the scenario, integrations and operating environment.

From process to deployment

Describe the logic. Give it a runtime.

A business analyst models order fulfilment in BPMN. S8 maps the process to a microservice template and prepares the container and Kubernetes deployment artifacts.

S8 / Process to runtimeAnimated example · 20 sec

0:00 / 0:20

Visual simulation. No image build or cluster deployment is performed.

Business rule: reserve an order only if all items are in stock. Otherwise return a rejection.
01 / Process model
order.bpmn · fragment
<serviceTask id="checkStock" name="Check stock" />
<exclusiveGateway id="available" default="reject" />
<sequenceFlow sourceRef="checkStock" targetRef="available" />
<sequenceFlow id="reserve" sourceRef="available" targetRef="reserveStock">
  <conditionExpression>allItemsAvailable</conditionExpression>
</sequenceFlow>
<sequenceFlow id="reject" sourceRef="available" targetRef="rejectOrder" />

The task becomes a handler; the gateway becomes a branch. This is a BPMN fragment, not a complete executable process.

02 / Microservice template
order-service / application / process-order.ts
// POST /orders → processOrder → inventory adapter
export async function processOrder(order, inventory) {
  const available = await inventory.check(order.items);
  if (!available.allItemsAvailable) {
    return { orderId: order.id, status: "rejected" };
  }
  await inventory.reserve(order.id, order.items);
  return { orderId: order.id, status: "reserved" };
}
// Template: API · validation · handlers · adapters · tests

The template defines the service structure. Developers connect inventory adapters and add transaction, retry and idempotency handling.

03 / Container and Kubernetes
Dockerfile + kubernetes.yaml · fragments
# Dockerfile (compiled service with no external runtime dependencies)
FROM node:22-alpine
WORKDIR /app
COPY dist/ ./dist/
USER node
CMD ["node", "dist/server.js"]

# Kubernetes Deployment specification fragment
spec:
  replicas: 2
  template:
    spec:
      containers:
        - name: order-service
          image: YOUR_REGISTRY/order-service:1.0.0
          ports:
            - containerPort: 8080
          readinessProbe:
            httpGet: { path: /health/ready, port: 8080 }

CI builds and pushes the image. Deployment runs the containers; Service routes traffic. Configure the registry, secrets, resource limits and health endpoints before deploying.

Explore API, data and SQL generation

One scenario, end to end

Follow a payment from need to code.

Explore how one requirement connects to an API, a data schema and an access rule. Then generate a SQL migration below.

REQ-PAY-01 / The customer can pay for their own order.
payments.openapi.yaml

A model you can edit. An artifact you can use.

Edit the JSON model and generate a PostgreSQL migration. This browser demo uses a template and does not connect to a database.

SQL / migration.sql

Business before technology

See what the organisation can do.

Business Capability Map describes what the business does. C4 shows the systems that support it.

Customer experience

Commerce

Enterprise management

Explore the connections

Explore the C4 container diagram

Illustrative architecture for CAP-PAY. Technologies are examples, not a prescribed stack.

Customer[Person]

Pays for an order

Order system [Software System boundary]
Web application[Container: JavaScript / SPA]

Displays the payment form

Payment service[Container: Node.js]

Validates the order and initiates payment

Payments database[Container: PostgreSQL]

Stores payments and statuses

Payment provider[External Software System]

Processes the payment

Blue: internal containers. Grey: external system. Dashed line: system boundary.

Deployment

Fit your operating environment.

Choose the deployment approach around your requirements for data, AI and access.

SaaS

Platform and data in a cloud environment.

Hybrid

Cloud management with data and AI in your infrastructure.

On-premise

Private deployment within your organisation.

Deployment options reflect the product vision. Availability and configuration are defined during pilot scoping.

Questions

Before you start.

What is S8 Intelligence?

S8 Intelligence connects business requirements, architecture and implementation artifacts in a shared enterprise model. Our mission is to help organisations become AI Native companies.

What can I try here?

Watch the BPMN-to-Kubernetes animation or open the technical example below to explore API, data and access rules and generate a SQL migration in your browser.

Watch the animationOpen SQL demo
How is a pilot evaluated?

Select one journey and agree on the baseline, scope and measurement period. Track delivery time, failed deployments and the work needed to prepare artifacts.

How do we assess value, cost and data access?

Before a pilot, define baseline delivery time, engineering effort, integrations and permitted data. Compare time savings with pilot and operating costs. Agree on hosting and access boundaries before connecting company systems.

Start with one change

Define your first pilot.

Choose a customer journey and a measurable problem. Build the model around that scope, then evaluate the result.

  1. Scope: one business process and its requirements.
  2. Deliverables to agree: process model, architecture, service artifacts and a measurement report.
  3. Before starting: confirm duration, price, integrations, baseline and acceptance criteria in the pilot proposal.