AI / Capabilities
RAG & Knowledge Systems.
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.
The difference between "plausible" and "correct" is citations
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.
How we build RAG you can defend in a boardroom and an audit
A four-phase path that treats citations, access controls, and refusal behavior as design inputs, not hopes.
Discover
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.
Pilot
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.
Validate
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.
Scale
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.
What we build
Four disciplines that together turn "a corpus full of documents" into AI you can trust in front of a customer or a regulator.
Retrieval Pipelines
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.
Hybrid Search
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."
Access Controls & Permission Mapping
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.
Source Citations & Refusal Design
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.
Retrieval-augmented AI in action
The market shape for Indonesian enterprises building knowledge systems on their own documents.
RAG is the default architecture for enterprise AI
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?"
Grounded AI is now a legal requirement, not a preference
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.
36,000+ facilities creating retrieval-scale healthcare knowledge
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.
What's trending in retrieval-augmented AI
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Which part of your business needs its own grounded AI?
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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