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Loreon Technologies Limited  ·  RC 7803910  ·  Abuja, NG

Systems that turn data into decisions.

We are a Nigerian technology team building software, deploying AI and machine-learning systems, and turning data into decisions — then training the people who keep all of it running.

Capability index

  • 01 Technology Solutions
  • 02 Artificial Intelligence
  • 03 Machine Learning & Data Science
  • 04 Data & Analytics
  • 05 Mentorship & Training

What we've built

Shipped,
not proposed.

Three kinds of system make up most of our delivered work. Each one went into daily use by people who did not have a choice about whether it worked.

01

AI applications

Production systems with models inside them — assistants grounded in a client's own documents, automated document handling, prediction and recommendation embedded directly into the tools people already use.

LLM applications Document AI Predictive
02

Operations management systems

End-to-end platforms to run a business day to day: records, workflow and approvals, billing, staff, scheduling and the reporting that sits on top — one system instead of six spreadsheets.

Records & workflow Billing Scheduling Reporting
03

Analytics dashboards

Operational and executive reporting built on a real pipeline underneath — one agreed definition per metric, refreshed automatically, designed for a decision rather than decoration.

Power BI Data pipelines Automated reporting
2024 Incorporated under CAMA 2020
RC 7803910 Corporate Affairs Commission
Abuja Federal Capital Territory, Nigeria
05 Service lines under one team

Why Loreon

A team, a system,
and a track record.

Most organisations don't need another slide deck about digital transformation. They need a team that will sit with the data, find where the process actually breaks, and ship the thing that fixes it.

Loreon brings together engineers, data scientists, analysts and designers under one delivery model. Every engagement runs on the same documented process — discovery, architecture review, iteration, code review, testing, deployment, handover — so the outcome depends on our system rather than on who happens to be free that week.

That system is what lets us take on work across all five service lines at once, and what lets a client hand a platform to their own people at the end without anything breaking.

How the team is built
01 — Multidisciplinary

Every discipline in one team

Engineering, machine learning, data, analytics and design work from a shared backlog rather than passing documents between silos. Discovery through to production is one accountable team.

02 — Repeatable

Process, not improvisation

Version control, peer review, automated testing, staged environments and monitoring are standard on every project — not extras that get cut when a deadline moves.

03 — Accountable

Measured against a baseline

Each engagement opens with a baseline and closes against it. If a model or dashboard can't be tied to a decision someone actually makes, we say so before you pay for it.

04 — Transferable

Capability left behind

Documentation, runbooks and hands-on sessions ship with the build. Our training practice exists so your team owns the system, not just the invoice.

Services

Five service
lines.

Engagements run from a two-week diagnostic to a multi-quarter build, staffed from the same team. Most clients start with one service line and grow into two or three.

01

Technology
Solutions

Custom software across web, mobile, desktop and cloud — plus the integration, infrastructure and consulting work that makes it dependable. Including enterprise platforms and modernisation of systems that have outgrown their original design.

Web & mobile Enterprise platforms APIs & integration Cloud & DevOps Consulting
02

Artificial
Intelligence

Applied AI with a job to do: workflow and document automation, assistants grounded in your own knowledge base, language and vision models, recommendation engines — and the evaluation harnesses that keep all of it honest in production.

LLM applications NLP & chatbots Computer vision Document AI Recommenders
03

Machine Learning
& Data Science

Forecasting, scoring, segmentation, anomaly and fraud detection — modelled on your data, validated properly, then deployed with monitoring so drift is caught before it costs anything.

Predictive models Forecasting Risk & fraud MLOps
04

Data &
Analytics

The unglamorous foundation: pipelines, warehouses, definitions people agree on. Then the dashboards and reporting that turn all of it into something a leadership team can act on in a Monday meeting.

Data engineering Warehousing BI & dashboards Governance
05

Mentorship
& Training

Loreon Academy: structured tracks in data science, analytics and software engineering, one-to-one mentorship for people switching into tech, and private upskilling for teams already in motion.

Cohort tracks 1:1 mentorship Corporate training Capstones

Method

How an
engagement runs.

Four phases, run in this order on every engagement. We don't build until we can describe the decision the build is meant to improve.

Phase 01

Diagnose

Workshops with the people doing the work. We map the process, audit what data exists, and write down the decision we're trying to move.

Phase 02

Design

Architecture, data model and interface direction — costed, sequenced, and scoped so the first useful thing ships early.

Phase 03

Build

Short iterations against a working environment. You see the system every fortnight, not at the end. Tests and documentation as we go.

Phase 04

Operate & hand over

Monitoring, runbooks, and training sessions with your team. Support stays available, but dependence on us is designed out.

Technology only earns its cost when someone, somewhere, makes a better decision because of it. Everything we ship is aimed at that moment.

Loreon Technologies — operating principle

Loreon Academy

The talent gap
is a build problem.

Nigeria is not short of ability. It is short of structured, senior mentorship. Loreon Academy exists because we kept meeting brilliant people with no one to review their work.

  • T1 Data Science & Machine Learning
  • T2 Data Analytics & Business Intelligence
  • T3 Software Engineering
  • M1 One-to-one Mentorship
  • C1 Corporate & Team Training

Every track is taught by engineers who ship for clients, and closes with a capstone reviewed the way we review production work.

Next step

Tell us what's
not working yet.

Send a short note about the problem. We'll reply with an honest read on whether we're the right team for it — and what the first two weeks would look like if we are.