MODEL 01
AI-assisted development
Support implementation without giving up engineering review.
- Production-aware
- Reviewed with evidence
AI software engineering
AI-assisted delivery across the software lifecycle.
AI Software Engineering applies AI to testing, code generation and development tooling to improve delivery speed and code quality.
Testing
Core capability
Code
Applied to the workflow
VERIFIED DELIVERY
The service scope, implementation and production evidence stay connected throughout delivery.
What we build
Practical ai software engineering systems.
MODEL 02
Automated testing
Generate, prioritize and maintain useful test coverage.
- Production-aware
- Reviewed with evidence
MODEL 03
Delivery intelligence
Use evidence from the workflow to improve reliability and throughput.
- Production-aware
- Reviewed with evidence
How it is delivered
From scoped problem to reviewed production system.
Implementation, evaluation and operational handover stay connected throughout the delivery workflow.
WHAT THIS MAKES PRACTICAL
TestingCodeDeliveryAI-assisted developmentAutomated testingDelivery intelligence
FLOW - assess - build - embed
01
Scope
Problem, users, data and measurable success criteria
02
Build
Working implementation integrated with the target workflow
03
Review
Quality, risk and operational evidence
04
Deploy
Handover, monitoring and ownership
Where it fits
Teams putting ai software engineering into real operations.
New product builds
Create a focused system around a validated user and business need.
Existing operations
Add intelligence to a workflow without losing review and accountability.
Modernization
Replace fragile experiments with observable production engineering.
Internal capability
Leave teams with reusable code, documentation and operating knowledge.
A simplified process, powered by Umaku
Explore the platform A visible path from discovery to delivery.
01
Discover
Confirm the problem, data, users and constraints.
02
Design
Choose the architecture, evaluation plan and integration path.
03
Build
Implement the system in reviewable delivery increments.
04
Validate
Test quality and operational fit against agreed criteria.
05
Deploy
Ship with documentation, monitoring and clear ownership.
Outcomes
Capability that stays in the building.
Applied
People practice on real AI workflows instead of passive slideware.
1 project
A real deliverable your team owns, produced during the program itself.
In-house
The workflow, tooling and judgement to keep building after the engagement ends.
Related case studies
All
Geospatial AI · EnergyIdentifying Optimal Microgrid Sites with AI & GIS
Combining satellite imagery, demographic, and infrastructure data to prioritize rural electrification investments.
Read case study
Computer Vision · HealthBattling Malaria in Liberia with AI
Smart diagnostic and surveillance tools supporting field workers in low-connectivity environments.
Read case study
NLP · Public SectorAI to Combat Misinformation & Disinformation
Multilingual NLP pipelines that detect coordinated narratives and surface verifiable signals.
Read case study
Predictive ML · MobilityShort-Term Traffic Congestion Prediction
Spatio-temporal models that forecast congestion and inform city operations in near real time.
Read case studyHow is this different from an online course?
Courses teach concepts; this builds capability. Your team works on a real project with your data and senior mentorship.
Who should join a cohort?
We tailor the curriculum to the audience, from data scientists and engineers to analysts and leaders who need informed AI decisions.
Can the project be on our own data and use case?
Yes. Building on a problem your organization cares about makes the learning stick and produces something useful.
Is this useful for setting up an AI Centre of Excellence?
Often it is the foundation. Capacity building seeds the skills, standards and tooling a Centre of Excellence runs on.
Ready to apply ai software engineering?
Talk to a solutions architect about your workflow, data and production requirements.
Discuss a project