Fintech product · Explainable AI · Data controls · 2024 to 2026
BRINK AI Funding
Navigator
An award-winning fintech concept developed into a working, source-grounded product prototype for Indonesian MSMEs.
KUR Working Capital
91% alignment based on productive use, operating history, and repayment preference.
Independent portfolio prototype. Guidance is illustrative and does not predict approval or replace provider assessment.
From winning concept to working product
The strategy case did not end with the pitch deck.
BRINK began as a fintech proposition for the Tech in Asia x Bank Rakyat Indonesia FutureMakers challenge, placing second among more than 940 participants. The original work combined user research, product strategy, financial modelling, and go-to-market design.
I later translated the concept into an interactive application that makes financing pathways easier to understand without presenting a black-box recommendation. The product now demonstrates how I move from commercial framing to interface design, decision logic, data controls, and deployable software.
Product logic
Separate eligibility, suitability, evidence, and explanation.
Structured assessment
Captures operating stage, use of funds, available financial records, and preferred financing obligation through a four-step flow.
Deterministic boundary
Explicit requirements and control states remain rule-based. Generated language is never positioned as the authority for eligibility.
Source-linked pathways
Each financing route keeps its rationale, caveat, institution, source URL, and verification date visible in the interface.
Explainable guidance
The coach layer turns structured results into plain-language reasoning and next actions while preserving uncertainty.
Technical implementation
A real interface, built as a product system.
The deployed prototype is intentionally front-end only. It demonstrates product logic, evidence design, quality controls, and interaction patterns without collecting identity documents, financial credentials, or user data.
App Router project with typed content models, reusable UI states, optimized local assets, and Vercel-ready production configuration.
Client-side state coordinates multi-step answers, selected financing routes, coach responses, task completion, and view transitions.
Recommendation objects preserve traceability to primary sources and keep verification status beside the output rather than in hidden metadata.
A dedicated control surface models completeness, validity, consistency, freshness, exception severity, and human-review states.
Designed control flow
Recommendations should be publishable only when their evidence is.
The quality console turns a product-content problem into an auditable data workflow. It is represented in the prototype as a production design, not as a claim that a live ingestion backend is already running.
- 01Ingest
Official program, regulator, and market source records.
- 02Normalize
Standard fields for route, provider, requirements, limits, and dates.
- 03Validate
Completeness, validity, consistency, and freshness controls.
- 04Review
Exceptions remain visible until a human resolves or blocks them.
- 05Publish
Only reviewed evidence becomes recommendation content.
Responsible AI boundary
The current prototype is honest about what is and is not automated.
A production version would add retrieval over versioned source records, citation enforcement, evaluation sets, prompt and output logging, and escalation when evidence is missing. The core boundary remains the same: AI may explain a controlled result, but it cannot silently create a rule.
What the work demonstrates
Product judgment that moves across finance, data, and implementation.
BRINK is the clearest expression of how I work: frame the institutional and user problem, turn ambiguity into explicit decision logic, make the evidence legible, and build the interface people actually use.