Experiences
Practical lessons from planning and running AI, GeoAI and immersive 3D projects: what to ask, what to avoid and what to do next, shared so your own project goes smoother.
All experiences
Showing 1–6 of 6 experiences
Planning · Project intake
Seven questions we ask before we say yes to an AI or GeoAI project
Most failed AI projects were doomed in the first meeting. These are the questions that separate a project worth doing from one that will quietly stall.
· 7 min readRead →
Planning · Scoping and pricing
How to estimate an AI project honestly: ranges, phases and the costs people forget
Why a single fixed number for an AI project is usually wrong, how to estimate in phases with ranges, and the hidden work that sinks budgets.
· 6 min readRead →
Planning · Data readiness
When the data is missing, late or wrong: what to do before and during a project
Real data is never as clean as the sample suggested. A practical approach to auditing, repairing and living with imperfect data.
· 6 min readRead →
Operationalizing · Adoption and training
Getting people to use what we build: training, trust and the first thirty days
A technically excellent system that nobody uses has failed. How to plan training, earn trust and spot the early signs of an unused tool.
· 5 min readRead →
Operationalizing · Monitoring and maintenance
Keeping an AI system honest after launch: monitoring, feedback and drift
AI systems do not stay accurate by themselves. A practical routine for watching quality, collecting feedback and deciding when to retrain.
· 6 min readRead →
Operationalizing · Remote delivery
Running a project across time zones: how a remote team keeps clients informed and in control
Communication rhythms, decision logs and handovers that keep international AI and 3D projects moving without constant meetings.
· 6 min readRead →
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