Industry Intelligence Platform

For localization, language-technology and product professionals

For professionals who want to understand how a useful intelligence workflow is designed, tested and kept verifiable.

The product need and learning goals

The platform answers a practical need: follow the localization industry and emerging technology without manually reading dozens of sources. It provides a first screening gate, while the original articles remain the reference for human reading and judgement.
It is also a functional-design and product-building demonstrator: the development approach is part of the value proposition, making the end-to-end process visible alongside the resulting functionality.

Making intelligence useful without losing the evidence

Industry signals exist, but they are dispersed, vary in importance and easily lose their provenance. The platform organises collection, qualification and delivery so readers can move faster without losing context.

Five ways to build an intelligence workflow

From signal to report

Human governance is the guiding principle throughout: people define the perimeter, validate content before and after AI processing, and remain accountable for publication decisions.

Each completed execution leaves publishable outputs and an internal traceability record. The Pre-AI editorial validation may be further automated in a next version, after the current observation period.

A curated perimeter, not a random feed

The source set covers specialist analysts and press, associations and think tanks, major LSPs, TMS and localization platforms, machine translation and language-AI providers, and emerging multilingual orchestration players.

Local where it matters, assisted where it helps

For report processing, no content processed by the local AI runtime is transmitted to third-party AI services or used for third-party model training. Processing remains local and confidential. Iterative qualification of the local LLM and runtime also clarified prompt-engineering and token-economy trade-offs, helping balance sobriety, reliability and output quality.

Grow the perimeter without rewriting the core

The platform is designed to evolve primarily through configuration. Sources can be added or removed through AI-assisted curation, monitored topics can be changed, the same workflow can be adapted to another industry, and report-generation frequency can be adjusted without redesigning the functional architecture.

Specification first, increments under test

AI-assisted or not, the project follows the standard Git and GitHub workflow for versioning, review and release traceability. Functional, architecture, data-model and version specifications guide staged development. Automated tests and regression checks precede human inspection and formal acceptance. Nearly 300 automated regression tests support the current process, including module checks, global functional tests and human-in-the-loop arbitration.

A product that is verifiable by design

The platform is designed to remain clear to operate, inspect and extend as its scope evolves.

The preparation is part of the product

The most time-consuming work was not typing code. It was preparing accurate requirements, guardrails, architecture and data contracts, then iterating on source qualification, prompts, testing and quality control.
AI is a powerful assistant for research, coding, testing and documentation, but it is not the author. It can fail subtly, so human verification of sources, numbers, outputs and assumptions remains non-negotiable.
The quality of the result is governed primarily by the quality of the product definition, not by the speed of the coding loop.
Documentation managed in Obsidian and written in Markdown provides detailed traceability, while recording AI-assisted steps makes decisions easier to revisit. As the product grows, professional work-management tools such as Jira remain the preferred standard for epics, user stories and version follow-up, while Obsidian remains the durable record of decisions and specifications.

Build, observe, refine

Embeddings could later support semantic deduplication, historical comparison, thematic clustering and trend detection. They are not yet part of the current implementation, but represent a natural path toward richer, more context-aware reporting.