Practical notes from the engineers who build agents, copilots and AI products.
A super app bundles many services behind one login and wallet; build one only if you have several services and one customer base. The test is whether the same customer already uses two or more of your services and would use them more if they shared identity, payment and history. If not, build one excellent app instead.
RAG builds typically run $14,000–50,000 depending on document types, permissions and refresh needs, plus modest running cost. The price is driven by how messy the sources are, whether access control must be enforced per user, and how often the index must refresh, far more than by the model or the vector database.
Maintenance runs 15–25% of build cost per year or a monthly tier; AI systems need an add-on for evals, prompts and cost tuning. A good plan names a response time, an hour allowance and what it covers: patches, updates, monitoring, and for AI, the eval re-runs that catch a model change first.
Ask which decision changes, what the data readiness is, what it costs monthly, who owns it and how success will be measured. Five questions, answered in writing before approval, separate an AI investment from an AI experiment, and a good vendor will already have asked the team proposing the spend the same questions.
TCO includes inference, retrieval infrastructure, monitoring, evaluation upkeep, model changes and the people who own the system. The build price is the smallest line over three years; the recurring lines are what decide whether a system pays for itself, and most budgets leave half of them out.
Custom CRM typically starts around $28,000 and ERP higher, phased over months; the comparison is licence plus customisation of a generic product. Count the five-year total on both sides, including integrations, data migration, training and the cost of bending your process to fit a package you did not design.
The quoted fee is rarely the largest number. The hidden costs of an AI discovery sprint are internal time, data extraction, evaluation set construction, benchmarking API spend, and the cost of acting on the answer. Together they usually exceed the fee, and none of them is a surprise if you budget for them.
A ten-day sprint benchmarks models on your data, sketches the architecture, estimates cost and ends with a go or no-go and a fixed price. It replaces months of vendor conversations with evidence: which model clears your accuracy bar, what the monthly bill looks like, and whether the build is worth funding at all.
Support agents answer admissions, fees and timetable questions in local languages on web and WhatsApp, escalating with context. A student support chatbot pays for itself in the admissions and fee-deadline peaks if it is grounded in the institution's own rules, connected to the student record and gated on every action.