Models that answer from your own data.
AI Engineering
We build AI systems that answer from your own data, not from a general model's guesswork. Every system ships with a readable accuracy number, not a demo that happened to work.
When this service makes sense
If one of these sounds familiar.
A chatbot is already deployed, but its answers are often wrong and there's no way to measure it.
Internal documents pile up and keyword search turns up nothing.
The team has experimented with prompts for months without ever reaching production.
How we work
Four stages, no surprises along the way.
Audit Data
We map document sources, quality, access rights, and format before touching a model.
Design Retrieval
Document chunking, metadata, embedding, and re-ranking are tuned to your case.
Build Evaluation
A labeled test question set becomes the brake: every change is measured, not felt.
Release & Monitor
Query logs, token cost, and questionable answers are monitored after going live.
What you get
Not slides, but something you can run.
- Retrieval service + documented API
- Evaluation test set with scores
- Cost and quality monitoring dashboard
- Code handover and operational runbook
Tools
Chosen for stability, not for hype.
FAQ
The questions we get most often.
The Data Can't Leave The Office. Is That Possible?
Yes. We run open models on your own infrastructure, with cost and quality tradeoffs explained upfront.
How Long Until It's Usable?
A testable prototype usually takes 3 to 4 weeks. A production version with evaluation takes 8 to 12 weeks.
Punya masalah yang layak dibedah?
Ceritakan situasinya. Kalau bukan kami yang paling tepat, kami bilang dan tunjukkan arah lain.