01 / Service
Data migration
Changing systems should not mean losing your records.
Where does the data live now, and where must it go?
Typical output: Source-to-target map, migration plan, validation checks, and handover.
Explore the service & demoFor Organizations
Data & AI systems built around the work your team needs to do.
Plan the right investment, build reliable data and AI systems, and see how the pieces work together. Advisory, engineering, and solution patterns sit within one organization offering, shaped around your people and operating context.
How an engagement moves
Audit
Map where AI can help, where data is exposed, and which workflows are ready.
Roadmap
Prioritize use cases, owners, risk controls, and the first practical adoption sprint.
Build
Turn selected workflows into reporting automation, agents, or decision-support systems.
Train
Equip managers, analysts, and operators to use, review, and improve the system.
Measure
Track adoption, time saved, error reduction, customer experience, and decision quality.
For your organization
Plan it. Build it. See it in practice.
Start with the question you have today. These paths connect; you do not need to buy all three to begin.
Plan it
Advisory
Where should we start?
Assess readiness, prioritise problems, define governance, and agree a practical roadmap.
Explore AdvisoryBuild it
Engineering
We know the problem. Who can build it?
Move data, connect systems, create trusted metrics, and deliver tested AI workflows.
Explore EngineeringSee it in practice
Solutions
What could this look like in our team?
Explore reporting automation, knowledge assistants, and other ways the services work together.
Explore SolutionsEngineering consultancy
What is holding your team back?
Start with the problem your team knows. We shape the work around your existing tools, budget, connectivity, and operating context, then agree the deliverables and ownership.
02 / Service
Data engineering
Your team should not spend every morning copying data.
Which systems need to connect, and how often must data refresh?
Typical output: Ingestion and transformation flows, quality checks, monitoring, and runbook.
Explore the service & demo03 / Service
Analytics engineering
Finance and sales should not report different versions of revenue.
Which business numbers need one trusted definition?
Typical output: Documented data models, metric definitions, tests, and reporting layer.
Explore the service & demo04 / Service
AI engineering
Routine searches and repeated tasks are taking time from your team.
Which workflow can AI improve safely and measurably?
Typical output: Scoped integration, evaluation cases, review points, and operating instructions.
Explore the service & demoDecision Lab pilot
Bring one decision your team struggles to make.
Which stock should we reorder? Which payment exceptions need attention? Tell us what is difficult today. We will review whether a focused pilot can help, then agree the brief, scope, price, and success measure.
Start with a plain-language description. We agree data access and permissions before preparing a learner brief.
What your pilot delivers
A tested workflow
A working approach checked against agreed cases, with findings and limits documented.
A named owner
An agreed person in your team, with handover notes and a clear review process.
A measured result
A baseline and a pilot result against the agreed measure, such as time to decide or exceptions resolved.
- 01
Map
Name the decision, its owner, and what a better result looks like.
- 02
Create safe brief
Turn the problem into an approved brief with safe or synthetic data.
- 03
Build
Selected learners build with mentor guidance and review.
- 04
Test
Check the workflow against the agreed decision and success measure.
- 05
Deploy
With organization approval, hand over to a named owner and measure the result.
Learner work uses approved or synthetic data and receives mentor review. Production deployment happens only after testing and your approval. See the mentorship project track.
Work and collaborations
Context matters. So does the work already done.
These relationships span engineering, business intelligence, and training. Each should be understood in its own context.
Irri-hub
Data engineering & analytics
Built a pipeline from Irri-hub’s Zoho ERP APIs, staged the data in PostgreSQL, and developed Power BI analytics and visuals for senior leaders to review financial and customer performance over time.
Phoenix KE Analytics
Agentic AI and BI training
Helped train 100+ students in Agentic AI and 150+ students in Excel and Power BI.
Crafted
Study abroad eligibility AI
Students submit their grades, budget, and interests. An n8n workflow uses data stored in Pinecone to recommend suitable partner universities.
Men's Focus
Mtumba retail, Tanzania
Helping the team use Excel with AI and digital marketing with AI to improve stock, sales, and customer growth.
Supporting paths
Start where the pressure is already visible.
Engineering can be supported by advisory and training. Begin from the need your team can already name.
Executives and founders
Decide where AI fits, what to avoid, and what to fund first.
Start with AI Readiness Audit, then build a Responsible AI Roadmap.
Operations and finance teams
Reduce manual reporting, repetitive follow-ups, and decision delays.
Start with Reporting Automation, then train the team on Data & AI Fluency.
Customer-facing teams
Improve response speed, intake quality, and follow-up without losing trust.
Start with Customer and WhatsApp Agents, with human review and escalation rules.
People and learning teams
Build AI capability across departments without turning everyone into engineers.
Start with team training through Academy, project-based mentorship, and adoption clinics.
What stays connected
Engineering, advisory, and training reinforce each other.
Advisory identifies the right workflows and risks.
Engineering services build the data, automation, and AI systems.
Solution patterns show how those services combine around a business problem.
Academy trains the team to use, review, and improve them.
Handover and measured outcomes show whether the system is useful.
Start a conversation
Tell us about the system you need.
Share the problem, current systems, desired result, and who will use it. We can then agree the right discovery step.
You can also email Ubuntu Analytiq.