AI / Solutions
The bar for customer service AI in Indonesia is already high. Kata.ai is resolving 50 million conversations a month at 81% first-contact. Telkomsel has AI at 800+ service points. If your contact center is still routing every message to a human, you are both over-staffed and under-serving your customers. We build customer service AI that resolves, on WhatsApp, in Bahasa and regional languages, wired to your policy, payment, and account systems.
Most Indonesian contact centers are still running the motion they built for phone calls, with WhatsApp and webchat bolted on top. The result: every customer interaction becomes a human interaction, even the ones that didn't need to. We rebuild customer service as a resolution engine, AI on the first touch for intent detection, context lookup, and action; humans on the interactions that actually need judgment. We measure on resolution rate and customer sentiment, not containment.
Four phases from your worst support queue to a resolution system your CSAT graph respects.
We map the conversations. Ticket logs, call recordings, WhatsApp threads. We cluster by intent, effort-to-resolve, language, and outcome. We identify which intents AI can close, which need humans in the loop, and which you'd lose customers over if you automated. For financial services, OJK April 2025 guidance applies.
A six-week pilot on a bounded intent space, one product line, one support category, or your top three contact drivers. Bahasa-native, tool-wired, with fallback to your human team. Pass/fail on resolution rate, sentiment, and handoff quality.
Evaluation harness: resolution accuracy, tone calibration, bias checks across languages, refusal quality, escalation handoff metrics. Red-teaming in Bahasa and regional languages. Documentation for OJK or UU PDP where applicable. Sentiment sampling on production traffic.
Handover to your ops and IT. Your team gets the runbook, the monitoring dashboards, the intent-expansion process, and the handoff-quality metrics. New intents and new languages added as modules.
Four disciplines that together convert a contact center into a resolution engine.
Multi-label intent classification tuned to Bahasa and regional-language input. Customer context pulled via RAG from policies, tickets, and account data, permission-scoped per your org's access rules.
The difference between a chatbot and a resolution system. Live integration with your policy engine, payment system, ticketing, account system, appointment booking, order tracking, whatever the resolution actually requires.
When AI hands off, it hands off prepared. Agent context panel pre-filled: customer profile, conversation summary, AI's proposed resolution, sentiment cues, suggested next action. Your human agents arrive briefed.
We measure what customers experience, first-contact resolution, sentiment, handoff quality, re-contact rate, not containment. Every conversation logged for UU PDP data-subject requests; for financial services, logged to OJK audit standards.
The bar is set, the scale is proven, the regulator has drawn the line.
Kata.ai's platform handles more than 50 million Indonesian conversations monthly, resolving 81% at first contact across major brand deployments. The benchmark is set, the question isn't whether Bahasa-native customer service AI can resolve at scale; it's whether yours does.
Telkomsel deployed AI Digital Smart Care across more than 800 GraPARI service points, winning IDC's 2024 Best Future Customer Experience award. When a national telco puts AI at the physical frontline at this scale, the question for competitors shifts from whether to deploy, to how well.
OJK's April 2025 AI Governance Guidance covers AI systems in financial-services customer interactions. Bias testing, audit trails, and human oversight on high-risk decisions now apply. For banks and insurers, customer service AI is governed, not optional.
The KPI swap that changes how chatbots get built. Why containment rate protects the wrong thing and what the resolution-based measurement stack looks like in production.
What the April 2025 guidance means for financial-services chatbots, bias testing in Bahasa, audit logs, and the human-oversight handoffs you need to document before launch.
A mid-market playbook for SME CEOs running customer service on WhatsApp without a formal contact center. How to pilot AI without over-engineering, and what "good enough" actually looks like.
Tell us the top three contact drivers, the repeated question, the status check, the booking change, the claim query, the complaint intake. We'll scope a six-week pilot on real conversations with real metrics: resolution rate, sentiment, handoff quality.
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