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Business capabilityAI-Native Engineering

AI-Native Engineering

Build with AIMake applications work together

From software delivery to business applications and cross-tool connections, ActelosAI helps teams turn AI-created work into applications they can test, maintain and take responsibility for.

Explore use casesDiscuss your engineering and business needs

Business scenario preview

Requirements and acceptance criteriaCode and project guidanceReview and release records

Business question

Code is written faster. Why are releases still delayed

Connect requirements, project context, testing and review to find what actually slows delivery.

Metrics to watch

Time from agreed requirement to release · Rework rate

Existing spreadsheets and workflowsAuthoritative data sourcesOwners and handover rules

Business question

Every department has a small tool. Why are people still copying spreadsheets and chasing updates

Choose a repeatable workflow and agree the data, task states and human checkpoints so business and engineering work from the same record.

Metrics to watch

Duplicate-entry hours · Workflow completion time

Application and interface inventoryIdentities and access permissionsRun and incident records

Business question

As applications multiply, who can access data, resolve failures and manage upgrades

Map applications and interfaces, then define access, versions and ownership so cross-tool work can be traced and recovered.

Metrics to watch

Task success rate · Recovery time

Efficiency gains in AI engineering

External cases illustrate efficiency gains across application building, development cycles and feature delivery.

Application building at a software services firm

−65%

Proof-of-concept application build time

A software services company reported 65% less time to build proofs of concept using reusable AI application scaffolding.

Study scope

Proof-of-concept applications built with reusable AI scaffolding, alongside training and process changes. A company-reported project result.

Engineering at a cloud infrastructure company

−89%

Pull request cycle time

At a cloud infrastructure company, developers making extensive use of AI coding tools saw an 89% reduction in pull request cycle time.

Study scope

Measures the cycle time of pull requests among developers making extensive use of AI coding tools. A company-reported result, not an 89% reduction in end-to-end project delivery time.

Engineering at a large retail platform

4.5×

Feature deployment output relative to baseline

A large retail platform reported median feature deployment output of 4.5 times its historical baseline after introducing AI tools and reshaping development workflows.

Study scope

Measured as features deployed per sprint relative to historical baselines. A company-reported pilot result combining AI tools with changes to development practices.

Figures are drawn from external cases and research for reference. Actual improvements depend on each client’s business context, existing capabilities and implementation approach.

Business value

Three ways to make AI useful in daily work

Align business and engineering goals across development, collaboration and ongoing operation.

AI-native software delivery

Deliver usable software with less friction

Connect requirements, project knowledge, coding, testing and review to reduce lost context and repeated revisions.

Quality

Measurement notes
Measure the full path to release and defects afterwards. Code volume or generation speed alone does not establish better delivery.

Business application co-building

Turn individual tools into shared applications

Business teams define rules and acceptance; engineering provides data connections, reusable components and operational support.

Collaboration

Measurement notes
Keep useful spreadsheets and existing systems. Start with one defined workflow rather than separate copies of core business data.

Cross-tool integration & governance

Connect applications that teams can maintain

Align essential data definitions, task states and access. Establish application ownership, version records and failure recovery.

Control

Measurement notes
Connections depend on available interfaces and permissions. Chat products do not automatically share conversations. Match controls to the impact of the work.

These are improvement directions, not achieved results. Establish a baseline, then assess changes alongside quality and total effort.

Use cases

Start with a concrete problem

Combine these six use cases around your existing systems, data and teams. Agree implementation scope and deliverables at the start of the engagement.

Keep project context with the work

Why must the project be explained again whenever a person or AI tool changes

Organize requirements, acceptance criteria, code conventions and key decisions into traceable project guidance. Break development into checkable outcomes and give coding assistants the information each task needs.

Data & conditions
Permitted code and documents, a requirements owner, current decisions and versions; clarity on what each tool may receive.
Deliverables
Project context guide, task and acceptance checklist, and ownership for keeping information current.
Improvement direction
  • Clarification and waiting time
  • Rework caused by missing context
  • Time from agreed requirement to release

Connect AI-assisted coding with tests and review

The generated code runs. How do we know existing behavior still works

Establish before-and-after tests and regression checks around real requirements. Use AI to prepare tests, explain changes and investigate failures; the team confirms risk, acceptance and release conditions.

Data & conditions
A repeatable test environment, baseline behavior, changes and review records; appropriate code and test access.
Deliverables
Test and review plan, regression cases, issue list and release-check records.
Improvement direction
  • Change rework rate
  • Post-release defect rate
  • Time from review submission to acceptance

Turn repetitive spreadsheet work into a small application

Which daily spreadsheet task is worth turning into a shared tool first

Choose a task with clear inputs and outcomes. Define validation rules, use AI for extraction, classification or drafts, then save confirmed results to a shared record. Pilot before deciding whether to retain a spreadsheet or build an application.

Data & conditions
Sample spreadsheets, authoritative data, business rules, usage frequency and owners; a usable data-access route.
Deliverables
Workflow sketch, interaction prototype, field and validation rules, and a scoped application pilot plan.
Improvement direction
  • Duplicate-entry hours
  • Record error rate
  • Time to complete one task

Give exceptions and handovers a shared status

An issue has crossed several chat groups. Who owns it, and is it actually resolved

Create identified tasks from incoming reports and define assignment, missing-information requests, approval and closure. AI helps summarize and classify; changes to official business records follow the relevant authorization.

Data & conditions
Reports and handling records, task categories, owners and escalation rules; permitted notification and business-system interfaces.
Deliverables
Task states and ownership, human checkpoints, handover records and pilot acceptance checklist.
Improvement direction
  • Time from intake to closure
  • Handover return rate
  • Share of overdue unresolved tasks

Connect applications around shared business records

How can a business-built application exchange data with engineering systems without conflicting definitions

Identify authoritative sources for customers, products or orders, then align identifiers, fields and states. Connect available interfaces and design duplicate-request handling, retries and state reconciliation.

Data & conditions
System and interface inventory, data definitions, identities, access and business owners; verified interface capabilities and limits.
Deliverables
Data and interface mapping, integration plan, access specification, and failure and retry design.
Improvement direction
  • End-to-end task success rate
  • Share of inconsistent records
  • Duplicate-operation rate

Make shared applications maintainable

What needs to change before a successful trial can become a tool the team relies on

Map use and users, then match testing, access, versions and operational checks to impact. Define incident handling, cost review and retirement so the team can continue working when an application fails.

Data & conditions
Application inventory, run logs, version and cost records, data-access scope; named business and technical owners.
Deliverables
Application inventory, maintenance and release guidance, operational measures, and recovery and retirement plan.
Improvement direction
  • Recovery time
  • Total cost per successful task
  • Share of applications with named owners and version records

These are application directions, not delivered client cases. Check AI outputs for their intended use; actions affecting payments, stock, schedules or other official records retain appropriate permissions and human checkpoints.

How to start

Choose one real problem and a change you can verify

Bring an existing workflow, a tool in use or a recurring delivery problem. Agree the outcome, data and ownership before defining a pilot.

  1. Understand the problem

    Identify who is affected, where work gets stuck, how it is handled today and which outcomes matter.

  2. Scope a small pilot

    Select the data, existing systems and human checkpoints. Agree acceptance, cost and access.

  3. Co-build and verify

    Prepare a prototype or scoped application and connections. Check outcomes, failures and regressions using representative tasks.

  4. Handover and maintain

    Confirm business and technical owners. Provide usage, version, operation and recovery guidance, then adjust from actual feedback.

Deliverables to agree for your project

  • Requirements and workflow map
  • Prototype or scoped application
  • Data and interface specifications
  • Test and acceptance records
  • Usage and maintenance guidance
  • Baseline and improvement assessment

Agree scope, operating environment and support for each engagement. Expand only after reviewing pilot quality, cost and maintenance needs.

Explore Actelos Delta

Data & metrics

Establish a baseline before claiming improvement

Compare similar tasks and complexity under consistent quality criteria. Include waiting, rework and human effort, and distinguish targets from achieved results.

Delivery speed & quality

Connect requirements, commits, reviews, releases and defects. Fix start and end points and the observation window; compare task types and separate active work from waiting.

See whether delivery flows better while quality is maintained

Time from agreed requirement to release · Rework rate · Post-release defect rate

Workflow efficiency & collaboration

Record intake, assignment, approval and closure, together with duplicate entry, returns and human checks. Measure the whole workflow, including steps around AI generation.

See whether duplication, delays and handover losses decrease

Workflow completion time · Duplicate-entry hours · Handover return rate

Reliability & total effort

Assess success against task acceptance criteria. Keep failures, retries, human corrections and environment faults in view. Define the period and which tool, runtime and maintenance costs are included.

Understand reliability, operating cost and maintenance ownership

Task success rate · Recovery time · Total cost per successful task

Expert support

Expertise across engineering and business

Xiaofeng Zhang

Xiaofeng Zhang

Core Partner, AI Technology & Architecture Lead

Xiaofeng Zhang focuses on AI systems architecture and engineering, bringing application design, deployment, performance and cost into one engineering view. The project team can be shaped around the technical and business needs of the work.

Meet Xiaofeng

Connect the capability to delivery

Actelos Delta · Direction

Clarifies the business problem, the capability worth building and the evidence for acceptance; shapes pilots and team adoption. AI-Native Engineering contributes methods for delivery, applications and integration.

Explore Delta

Actelos Forge · Engineering

Turns a defined need into an application, workflow, integration or bounded agent, with the assets needed for testing, operation and handover.

Explore Forge

AI Model Access · Model access

Compare model capabilities for the workload and confirm availability and applicable commercial terms. Applications also need data connections, quality checks and maintenance arrangements.

Explore model access

Before we begin

Agree what ships and who will own it

AI-Native Engineering is the capability area; Actelos Delta helps define the engagement, while Forge provides engineering and handover where the agreed work requires it.

What distinguishes a working demo from accepted delivery

Agree acceptance criteria for the actual workflow, including data access, testing, failure handling and human approval. A demo can show a bounded possibility; it does not establish production readiness. Evaluate the application and its operating conditions rather than using generated code volume as the acceptance measure.

What must be clear at handover

Identify what is delivered, how it is configured and tested, who can release changes and who handles failures and maintenance. Source, usage rights, dependencies and support are agreed per engagement. Client handover does not automatically include ongoing operations or future integrations.

Which part of your work should AI improve first

Bring a real problem, the tools you use and the outcome you need. We can assess whether to improve software delivery, co-build an application or connect data and tasks first.

Discuss your needs

Define the direction through Actelos Delta, with engineering through Forge where the project requires it.

Explore how we work