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K
Kelvin

Kelvin

Agency
Indonesia flagIndonesia

Key Skills

Software

Other
Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

TextText
AudioAudio
DocumentDocument

Top Task Types

Data CollectionData Collection
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)
RLHFRLHF
Evaluation/RatingEvaluation/Rating
Entity (NER) ClassificationEntity (NER) Classification

Company Overview

Loker Dollar is an Indonesia-based data annotation and LLM-data provider. We build and manage a vetted annotation workforce drawn from our active Indonesian jobseeker community (lokerdollar.com), giving us fast, scalable access to motivated, work-ready contributors. Services: text/NLP annotation, RLHF and model-response evaluation, transcription, document data capture and validation, and image labeling. Differentiator — language. We deliver native Bahasa Indonesia at scale, plus low-resource regional languages — Javanese (68M speakers), Sundanese, and other Indonesian languages — in high demand for Southeast Asian LLM training and hard to source reliably elsewhere. Quality: rubric-driven workflows with gold-standard tasks, consensus labeling, and reviewer spot-checks; we track accuracy and inter-annotator agreement on every project. Tooling: Label Studio, CVAT, and client-provided platforms. Security & compliance: NDA-ready, documented infosec controls, ISO 27001 roadmap in progress, PII handling compliant with Indonesia's UU PDP (Law 27/2022). Workforce & coverage: managed crowd operating in UTC+7 (strong Asia + partial Europe overlap); fair-wage policy above regional minimum. We welcome paid pilots; Net-30 terms.

Security

Security Overview

Loker Dollar operates a remote-first annotation model. We are an early-stage Indonesian provider building a managed workforce from our vetted jobseeker community. We hold no formal security certifications yet but operate documented controls and are pursuing ISO 27001 (roadmap in progress). Access & data handling: project data is shared only under a signed NDA. We apply least-privilege access — annotators see only the data needed for their task, via client-provided platforms or our self-hosted tools (Label Studio, CVAT, Doccano, Argilla) on access-controlled accounts. We support data minimization and, where required, snippeting/redaction of PII before it reaches annotators. We do not retain client data beyond project needs and delete on request. Workforce: contributors are individually onboarded, identity-verified, NDA-bound, and trained per project, with a fair-wage policy above regional minimum. Remote work runs over secured accounts with unique logins; we can enforce view-only / no-download workflows and restrict access by account or IP wherever the client's tooling allows. Compliance: PII is handled in line with Indonesia's Personal Data Protection Law (UU 27/2022, in force Oct 2024). Cross-border data is processed per client instructions and applicable transfer requirements. Maturity & roadmap: as a new provider we are transparent that enterprise certifications (ISO 27001 / SOC 2) are not yet in place. We will complete client security questionnaires, sign DPAs, run pilots inside the client's own secure environment, and prioritize whatever controls the client requires. ISO 27001 targeted within 12 months.

Security Credentials

ISO 27001

Labeling Experience

Indonesian Job-Listings NLP Labeling & Enrichment (internal platform project)

Internal/Proprietary ToolingTextTextClassificationClassificationEntity (NER) ClassificationEntity (NER) Classification

Built and maintained labeled Indonesian-language datasets powering lokerdollar.com's job-matching platform. Work included: classifying and normalizing job postings (role, seniority, remote/on-site, salary band); named-entity extraction of skills, companies, and locations from Bahasa Indonesia text; and curating prompt/response data for a Bahasa job-search chatbot. Applied rubric-driven labeling with gold-standard checks and review passes to keep label quality consistent across a large, continuously ingested listing feed. Demonstrates native Bahasa text classification, NER, and conversational-data curation at scale on our own production system.

2026 - Present