AI / Capabilities
A general-purpose AI doesn't know your business. Your documents do. Your policies, your tickets, your precedents, your playbooks, your product catalogue, your audit archives. We build retrieval-augmented AI that grounds every answer in your knowledge, with citations you can verify, access controls mapped to your org, and refusal behavior that fails safe when the answer isn't in the source.
RAG, retrieval-augmented generation, is the architecture that takes an AI from impressive-sounding to defensibly correct. Every answer cites the document it came from. Every source gets access-controlled so the AI can't leak what a user isn't supposed to see. And when the answer isn't in the source, the AI refuses, not guesses. For regulated industries, RAG isn't a feature. It's the minimum bar between AI that can be deployed and AI that will create a compliance finding the moment it's audited.
A four-phase path that treats citations, access controls, and refusal behavior as design inputs, not hopes.
We inventory the knowledge. Which documents, policies, tickets, precedents actually matter? Which are canonical vs. deprecated? Which contain PII or regulator-sensitive content that needs permission mapping? We also map who in your org is supposed to see what.
A six-week pilot on a bounded knowledge corpus and a specific query pattern, typically internal knowledge search, policy Q&A, or precedent retrieval. Pass/fail criteria: citation accuracy, refusal rate on out-of-corpus queries, latency budget.
We engineer the evaluation harness: citation faithfulness, retrieval recall/precision, refusal quality, bias checks on source selection, and access-control tests across roles. Documentation for OJK, UU PDP, BPJPH, or sector-specific regulators, including proof that the system refuses to answer when it should.
Handover. Your team gets the corpus-update workflow, the reranker retraining cadence, the access-control maintenance playbook, and the dashboards that catch retrieval drift before it becomes a hallucination.
Four disciplines that together turn "a corpus full of documents" into AI you can trust in front of a customer or a regulator.
The foundation: how we break your documents into retrievable units, embed them, and rerank to surface the right context for the right question. Sloppy chunking is why most RAG systems hallucinate on the answer your customer actually needed.
Pure semantic search misses exact matches. Pure keyword search misses paraphrases. We build hybrid retrieval that combines both, with rank fusion tuned to your corpus, the difference between "nearly right" and "right."
Retrieval without permission mapping is a data incident. We wire RAG to your existing access controls, SSO, RBAC, document-level ACLs, so every retrieval call is scoped to what the user can legally see. Audit-logged for UU PDP compliance.
Every answer cites. Every citation points to a verifiable location in the source. And when the answer isn't in the corpus, the AI refuses, it doesn't guess. Refusal behavior is not a bug; it's the difference between a trustworthy system and a liability.
The market shape for Indonesian enterprises building knowledge systems on their own documents.
Around 70% of enterprise AI deployments globally are now built on a RAG pattern, grounded on internal corpora rather than pure generative models. The question in 2026 isn't "should we use RAG?" but "is our RAG architecture defensible, access-controlled, and compliant with our regulators?"
UU PDP (effective Oct 2024) requires demonstrable access controls on personal data, including data AI retrieves. OJK's April 2025 guidance adds source traceability and audit trails for AI decisioning. Together, they make citation-grounded, permission-scoped retrieval the only legally defensible pattern.
SATUSEHAT integration is generating the kind of large, heterogeneous, access-sensitive document corpus that requires sophisticated retrieval architecture. Indonesian hospitals and clinics building toward the 87% EHR target will rely on RAG-pattern AI.
Semantic embeddings have a hard time on Bahasa morphology and Indonesian product-catalogue names. A walk through why hybrid search is the safer default in local-language corpora.
The practical architecture for mapping role-based access, document-level ACLs, and PII scoping onto a RAG pipeline. How we engineer the audit trail before the first query runs.
Why well-designed RAG systems say "I don't know" more often than broken ones. How to calibrate refusal thresholds, and why the refusal rate is one of your most important production metrics.
Tell us the corpus that would create leverage if your team could query it in natural language, internal knowledge base, policy library, ticket history, legal precedents, product documentation, regulatory filings. We'll scope a six-week RAG pilot with a real query set and an evaluation harness that measures citation faithfulness and refusal quality.
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