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Services

Projects and systems, built for the AI-native age.

We design, build, and onboard the projects, solutions, and systems your business runs on — from the first scoping conversation to the platform your team uses every day.

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How it works

A delivery model built around outcomes

We join at the point of uncertainty, create a clear path forward, and stay close enough to make adoption part of the build.

AI-native project sprints

Find the leverage before you fund the roadmap.

  1. 01Map decisions, workflows, data, and constraints
  2. 02Prioritize use cases by value, feasibility, and risk
  3. 03Prototype with real users and real operational context

Embedded expert teams

Senior capability without a long hiring detour.

  1. 01Map decisions, workflows, data, and constraints
  2. 02Prioritize use cases by value, feasibility, and risk
  3. 03Prototype with real users and real operational context

Capabilities

The capability to make change stick

We connect strategy to the technical and human systems that make transformation durable.

01

Finding the right AI opportunities

02

AI assistants for your team

03

Workflow automation

04

Digital product development

05

Platform modernization

06

Connecting your systems and data

07

Design for AI-powered products

08

Quality and testing

09

Evolving legacy systems

Explore the opportunity

What could your application team do with more room to move?

Explore how AI-assisted engineering could free capacity for the changes your business needs. Start with a research-informed example, then adjust it to your situation.

Choose your priority to put the numbers in the context of your decision.

Your inputs

These are example values. You can replace them; nothing is sent or saved.

Fixes or bounded improvements; major modernization needs its own scope.
Include analysis, implementation, testing and review.
Optional. Use your own blended cost, not an Aleph price.
20% means a 5-day change could take 4 days. It is an editable example informed by a 2026 software-delivery case below.
Typical delay outside engineering work, such as an approval or release window. We use the same delay in both estimates.
Optional. Add extra people, tools or operations during a transition; leave blank if unknown.

Potential change capacity

Research-informed illustration — not based on your system.

Current change work60 engineering days / year€54,000 / year7 working days per change, including waiting
AI-assisted illustration48 engineering days / year€43,200 / year6 working days per change, including waiting

12 engineering days of potential capacity per year. Those days could go toward safer releases, reducing a backlog, or delivering a needed capability. They become a cash saving only if actual spending falls.

Running both approaches

No extra parallel cost entered. The figure above covers engineering work only; include extra old-system and new-approach costs if both must run together.

How the estimate works

Current work = 12 changes × 5 engineering days = 60 days per year.

AI-assisted illustration = 60 days × (100% − 20% possible time saving) = 48 days per year. This covers the whole change, including review.

Engineering cost = annual engineering days × €900 loaded day cost.

Elapsed time per change = engineering days for one engineer + 2 waiting days in both approaches. Release windows and external dependencies are outside this estimate.

Published measures to explore

Recent results show different ways AI can help teams deliver and support software. Each figure belongs to its own setting and measure.

  • 20–25% less IT development effortVodafone Idea and IBM, 2026 report this across a delivery model serving more than 150 applications; they also report 15% shorter end-to-end development time. The widget starts at an editable 20% illustration, not a Vodafone result for your application.
  • 26% more completed tasks2026 field experiments combined results from 4,867 developers at Microsoft, Accenture and another large company given access to an AI coding assistant. Completed-task volume is not the same as time or cost saved.
  • 75% shorter incident resolutionIn a recent Cognizant managed application support case, reported resolution time fell from eight to two hours after AI-assisted engineering and service changes. It measures incidents, not annual AMS cost.

These are published case results and a research study, not Aleph customer outcomes. For your application, a useful baseline would track change effort, incidents, service continuity and costs separately.

Turn an illustration into a useful plan

An Application Managed Services (AMS) engagement would define ongoing support, coverage, incident handling and small controlled changes for your application. Its value may be continuity and capacity, not a lower bill. The calculation excludes transition, monitoring, coverage, hosting and service fees.

Standalone Application Modernization is separately scoped and can address a capability gap or a material technical risk. The existing system may need to run during a staged transition, under a named operating owner. Discovery can establish your baseline, identify suitable work, and test where AI assistance helps. Engineers still review AI-assisted work, and no customer data is sent from this widget to a model or external service.

Discuss your application

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