AI / Capabilities
Indonesian enterprises still run on documents, claims forms, invoices, medical records, audit workpapers, KTP scans, NPWP filings, flight maintenance logs. Most of it is Bahasa-layout, mixed-format, and trapped in PDFs or mobile photos. We turn that chaos into structured, queryable, audit-grade data, built for Indonesian documents first. Live in weeks, not quarters.
Nine out of ten Indonesian enterprises we meet have the same bottleneck, a pile of documents a human has to read before anything happens. Claims. Purchase orders. Patient records. KYC files. Audit evidence. Each one holds the queue for hours, sometimes days. We replace that read with a model your team can trust: trained on your layouts, validated against your rules, logged for OJK, BI, UU PDP, or BPJPH, whichever regulator is watching. The work still gets checked. It just stops being the bottleneck.
A four-phase path that treats compliance as a design input, not a post-deployment scramble.
We start with your worst pile. Which documents queue the most hours, cost the most in error rework, and face the most regulator attention? We map the document type, the extraction fields that matter, the downstream systems, and the audit trail expectations from OJK, BI, UU PDP, BPJPH, BPOM, or SATUSEHAT.
A six-week pilot on your data. We build extraction, classification, and validation pipelines for one document type end to end, typically a claim form, a tax invoice, a patient record, or an audit workpaper. Target: 90%+ field-level accuracy with clean fallback to human review.
Where most document AI fails. We engineer the evaluation harness, bias testing, drift monitoring, and human-in-the-loop review queues. Every extraction gets a confidence score. Every override gets logged. The output is documentation your audit team, your regulator, and your board can all read.
Production handover. Your team gets the model runbook, the retraining cadence, the dashboards, and access to ours until the system is fully yours. New document types added as modules, not rebuilds. You own the output, the logs, and the IP.
Four document-intelligence disciplines that together replace the "someone has to read this first" bottleneck.
Receipts on mobile photos, KTP scans at 45 degrees, handwritten margins on claims forms, faxed medical records. We tune vision models to the formats Indonesian enterprises actually receive, not the clean lab-grade PDFs in English-language vendor demos.
Indonesian names, place names, product names, currency formats, date patterns, and regulator-specific codes (NPWP, NIK, BPJS IDs, HSN). We extract with models tuned for Bahasa morphology and code-switching, not ported-over English NER that guesses on every third field.
Documents don't all mean the same thing. An invoice from a tier-1 supplier routes differently than a disputed claim from an end customer. We build classifiers that understand your taxonomy, the one your team already uses, and wire the routing to your workflow tools.
Every extracted field gets a confidence score, a source-region citation, and a traceable decision path. Every override gets a logged reason. Every revalidation gets a diff. This is the difference between a demo and a system you can defend when the regulator asks how a decision was made.
One deployment we've shipped, and the market reality shaping every conversation we walk into.
We built the vision and extraction models behind a smallholder agronomy system, turning farmer-submitted photos of crops, pests, and soil conditions into structured inputs for field-specific advice. Delivered over WhatsApp, in Bahasa and regional languages. Silver award at the 2025 Salesforce Tech4Good Awards.
Global B2B invoice automation is crossing the 50% threshold this year, with Southeast Asia lagging, SE Asia AP teams still spend 40% more time on multi-entity processes than global peers, and regional e-invoicing mandates are closing the window for voluntary modernization.
Indonesia's hospital EHR baseline sits at 16% today against a Ministry target of 87% by end-2026. SATUSEHAT is already integrating 36,000+ facilities. The documents haven't digitized themselves, they have to be extracted, structured, and made FHIR-ready.
A tour through the specific failure modes we see, name morphology, currency formats, regulator codes, handwriting on pre-printed forms, and the architecture choices that fix them.
Field confidence, source citations, override logs, revalidation diffs. What OJK, BI, and UU PDP actually expect, and how to build it in.
From handwritten discharge summaries to FHIR-aligned records, a practical architecture for hospitals moving from the 16% EHR benchmark toward the 87% target.
Tell us the document type that backs up the queue most, claims forms, supplier invoices, patient records, audit evidence, tax filings, mobile-photo receipts. We'll scope a six-week pilot with honest exit criteria, benchmarked against a real sample from your data.
Start a project