← Blog · Strategy · 15 min read
Platform, custom model, or integration play? A structured framework for the decision that defines your AI trajectory.
Platform, custom model, or integration play? A structured framework for the decision that defines your AI trajectory.
Most Indonesian enterprise teams come to us asking "Should we build or buy this AI feature?" That's the wrong question. The right question is: "What is the half-life of the advantage this feature creates?"
A 30-minute scoping call. We listen, ask three questions, tell you if we can help.
Book a callIf the advantage lasts 3 months, buy. If it lasts 3 years, build. If you're not sure, run a time-boxed pilot and find out.
If the AI feature requires data that only you have — clinical notes, proprietary pricing signals, internal policy documents — the model or fine-tune needs to be yours. Off-the-shelf doesn't know your data. RAG helps, but fine-tuning compounds over time.
In fast-moving categories (copilots, chatbots), the frontier model providers close capability gaps within 12 months. Build only if you can execute a proprietary data flywheel faster than the frontier closes in.
A fine-tuned model requires: dataset curation, retraining runs, evaluation infrastructure, and someone who can debug a CUDA OOM error at 11pm. If that capacity doesn't exist, a well-integrated commercial API will outperform a poorly maintained custom model.
For Indonesian FSI clients, POJK 11/2022 requires explainability for model-driven credit decisions. That often rules out black-box commercial APIs and mandates an explainable in-house model. Compliance can force your hand.
Clinical AI, financial risk models, and public sector systems face high failure costs. For these, invest in the evaluation infrastructure to support a build — because off-the-shelf vendors won't give you the auditability you need.
Need a result in 6 weeks? Buy and integrate.
Building a 3-year platform? Build and own.
Building a 3-year platform but have 6 weeks? Build a buy-and-integrate bridge and migrate later.
Combining these six questions gives you a 2x2: short horizon / commodity task → buy and integrate; short horizon / proprietary data → RAG on commercial LLM; long horizon / commodity task → platform consolidation; long horizon / proprietary data → build and own.
"We've never regretted owning the evaluation layer. We've sometimes regretted owning the model." — Hai Patriana, Sainskerta
The talent supply for ML engineers in Indonesia is improving rapidly, but is still thinner than Singapore or India. Factor in a 20–30% talent premium for ML roles and a 6–9 month hiring lead time when budgeting a build decision.
