Two buyer groups. One result: a working system.
For public programs, we turn policy goals into services people can use. For enterprises, we reduce manual handling and shorten the path to a reviewable decision.
Turn national strategy into services people can use
- Mongolian-language AI services: citizen-facing assistants, document processing, evaluation harnesses
- Rapid government AI pilots: tightly scoped builds with measurable KPIs, handed over without lock-in
- Digital service build-out: offline-tolerant, low-bandwidth, integrated with national platforms
- Data platforms: ingestion, governance, dashboards, open-data publishing
- Applied AI literacy training for civil servants, in Mongolian, tied to real workflows
Less manual work. Faster, reviewable decisions.
- Workflow agents: compliance screening, document handling, customer operations
- Internal assistants that speak Mongolian and connect to your systems of record
- Web and mobile platforms: from design through deployment and operation
- Measurable scope, short cycles, working software, and clean handover
Start with one important workflow and record the baseline: time, errors, and manual handling. Put a working version in users' hands, measure the change, and expand only when the result holds.
One workflow. Measured result. Clean handover.
Scope
A tight, testable definition with measurable outcomes. We write down what will exist at the end — and commit only to what we can build.
Build & verify
Working software every week, with test suites and evaluation harnesses running alongside — including publication-grade Mongolian Cyrillic checks as a house standard.
Hand over
Documentation, training, and source in your hands. Built to be operated by you, not to create vendor lock-in.
Results embodied in working systems
These systems improve alert review, Mongolian-language publishing, and back-office completion. Each case is labeled by its current status.
Scale Guard
An AML screening co-pilot for banks: agentic review of alerts with human sign-off. In-house prototype.
Case study → Mongolian-language AImn-write
A Mongolian Cyrillic orthography engine: automated checks that hold AI-generated text to publication standard.
Case study → Back-office automationTamga
An agent system for LLC filing and back-office paperwork — documents drafted, tracked, and verified.
Case study →Talk to us. Leave the first call with a plan.
Tell us the problem; we will tell you what can be built, how long it takes, and what it costs.
Email mike@scale.mn↗ Scale.mn LLC · Ulaanbaatar, Mongolia · mike@scale.mn