Christian Anejo.
I build systems that carry data all the way to the field. The pipelines, workflows, and collection tools that turn messy operational data into decisions people can actually act on. Most of what I build is running quietly in production right now, for AI agencies, content teams, and the smallholder-farmer programmes that feed communities.
currently building
AI automation for agency clients
Leading a small remote team building automation for international clients: ticket triage, content pipelines, and data tooling. I set the technical direction, review the work, and keep the systems stable as traffic scales.
Operations data & stakeholder alerts
Turning field data into the reports, dashboards, and automated alerts that guide decisions across a large agricultural programme, and automating the flows that keep them running.
selected work
labour-cost research engine
A multi-workflow engine that researches and estimates labour cost by company and role, built as a bulk orchestrator/worker system with atomic row-claiming so hundreds of lookups run in parallel without collisions.
planning & scheduling automation
Automation supporting the planning and scheduling workflow for a field-service platform, taking repetitive manual steps out of the loop so the schedule keeps itself moving.
bilingual blog engine (en / nl)
Generates English and Dutch articles across a six-node AI pipeline, with translation quality checks, heading and link cleanup, and sitemap-based slug resolution so every post lands publish-ready.
multi-client publishing pipeline
Drafts and publishes Dutch articles to a CMS with sourced images, using structured-output parsing to keep the results clean and predictable across many clients at once.
support-ticket automation
Workflows that triage, route, and draft responses for support tickets, cutting manual handling so client teams spend their time only on the tickets that actually need a human.
stakeholder alerts & reporting
Automated alert and reporting emails that keep stakeholders current on operations without anyone pulling numbers by hand.
agricultural field-data system
Cascading district → pod → site data collection across 9 districts, 46 pods, and 275 sites, feeding clean, structured records straight back into operations instead of leaving them stranded on paper.
rural retail data & sales analytics
Pipelines and dashboards supporting 30+ field officers and 11,000+ farmer prospects, plus district- and shop-level sales analysis and early churn signals for lapsing dealers over a SQL view.
the stack
ai & automation
- n8n
- Python
- Claude / LLM APIs
- Structured-output prompting
- Ticket automation
data & storage
- SQL / PostgreSQL
- Supabase
- Excel (advanced)
- Dataiku
- Dashboards & reporting
field & collection
- ODK
- KoboToolbox
- XLSForms
- Cascading geo-hierarchies
- Machine learning
the background
I'm a data and automation engineer based in Nigeria. I build the unglamorous machinery that lets an organisation act on its own data: the pipelines, the workflows, the dashboards, the collection tools that quietly hold everything together.
Most of my work sits where software meets the real world. I've built automation for AI agencies and content teams, and I've spent long days at fertilizer distribution sites making sure the records that leave a laptop actually reach the farmer standing at the front of the queue. The same instinct runs through both: build the system so it holds when a person is depending on it.
Along the way I've founded a literacy programme, led student projects, and trained interns. I like building things that outlast the person who built them.