Our methodology

The LOKAL framework for AI

A structured method that starts with your business problem, not the latest tool — so every project is practical, measurable and sustainable.

Book a discovery call See the stages

Hand pointing at a process map on a wall

Why a method

Business outcome before tools

Our first filter is simple: does an option solve a specific business problem that we can measure with clear KPIs?

Profit, growth or risk — name the lever
Check data readiness before any pilot
Test fit inside real workflows
Agree metrics leadership can verify

The LOKAL framework

From discovery to long-term success

Each letter stands for a project stage, named in Bahasa Indonesia.

01

Latar Belakang · Background

Stakeholder interviews, workflow mapping and a review of your current tools and pain points.
02

Objektif · Objectives

Concrete, measurable KPIs instead of vague goals like “improve efficiency”.
03

Kelayakan · Feasibility

Compare tools on cost, integration, scalability and data security, then design the solution.
04

Adopsi · Adoption

Training, bilingual documentation and a feedback loop so your team actually uses it.
05

Lanjutan · Longevity

Maintenance, monitoring against the original KPIs and room to scale as you grow.

Scoring criteria

How we compare tools

When choosing between n8n, Make or Zapier, or ChatGPT and Claude, we score every option on the same criteria.

01 / 05

Cost-effectiveness

Total cost of ownership and how pricing scales for a business paying in Rupiah.
02 / 05

Integration

How well it connects to your existing systems, including local Indonesian software.
03 / 05

Scalability

Whether it grows with your business and handles heavier workloads reliably.
04 / 05

Security & privacy

Data protection practices in line with Indonesia’s personal data protection law.
05 / 05

Ease of use

How easily non-technical staff can use it, and the support available.

FAQ

Methodology questions

How we decide what to recommend.

Which AI solution is right for us?

The one that fits your current readiness. We check business need, data quality, workflow fit, governance and cost in sequence before recommending anything.

Should we start with automation or a chatbot?

It depends where the friction sits. Repetitive, structured work suits automation; work that depends on language or knowledge retrieval suits a conversational layer.

How long does evaluation take?

A focused discovery phase usually takes 1–2 weeks: enough to review data, interview stakeholders, test workflow fit and agree success metrics.

How do you measure success?

We agree pilot metrics against a baseline before building — such as time saved per task, response times or error rates — so you can expand, revise or stop.