23 September 2025
The technical journey behind Shindan: From single AI to agentic network
At Alpha CRC, our four decades of localization expertise informed every decision in developing Shindan, our proprietary language quality management platform. But the path to creating a production-ready AI system that could meet the exacting standards of professional localization wasn’t straightforward. The technical evolution of Shindan, from a promising but unreliable single-AI system to today’s sophisticated agentic network, illustrates why revolutionary tools require carefully considered approaches to AI architecture.
Shindan’s initial architecture seemed logical: a single AI handling all quality evaluations, supported by modules controlling data flow, filtering, and formatting. This approach aligned with traditional thinking about AI deployment: one powerful system handling multiple related tasks.
However, this architecture quickly revealed critical limitations that threatened Shindan’s viability as a professional localization tool. Hallucinations were commonplace, with the AI generating assessments that appeared convincing but often strayed from established quality frameworks like ISO 5060. While good results did occur, reproducibility was entirely chance-based: unacceptable for the consistent, reliable quality management that localization workflows demand.
The root cause became clear: asking a single AI to simultaneously evaluate terminology accuracy, style guide compliance, cultural appropriateness, and technical formatting while prioritizing all tasks equally created cognitive overload. The AI would hyperfocus on one evaluation criterion while neglecting others, or occasionally produce completely erratic assessments.
Our first solution attempt followed conventional AI optimization wisdom: enhanced prompt engineering. We developed detailed AI personas, rephrased evaluation tasks with greater specificity, provided extensive examples from our localization database, and fed the system comprehensive background information from Alpha CRC’s four decades of quality standards.
The theory was sound – guide the AI toward professional-grade assessments through comprehensive instruction. The reality was disappointing. Larger, more detailed prompts didn’t improve accuracy; they introduced noise and confusion that made the system even less reliable for production use.
This failure revealed a fundamental insight: the problem wasn’t insufficient information, but inadequate task division. Professional localization quality management requires focused expertise, not generalized knowledge.
This realization directly shaped Shindan’s current architecture philosophy: “Divide and conquer.” Just as Alpha CRC’s human teams include specialists for terminology, style, cultural adaptation, and technical accuracy, Shindan needed specialized AI agents, each focused on specific aspects of language quality evaluation.
The introduction of the agentic network marked Shindan’s transformation from experimental tool to production-ready platform. By restructuring quality evaluation into specialized, cooperative agents, each handling distinct elements like terminology validation, style guide compliance, or cultural appropriateness, we achieved the consistency and reliability that professional localization demands.
Results improved immediately and dramatically. Consistency levels rose sharply, and identical inputs began producing reproducible assessments: the foundation of any viable quality management system.
The agentic structure introduced new technical challenges. Initially, coordinating outputs from multiple specialized agents into cohesive quality assessments without duplication or contradiction proved complex. Different agents sometimes produced conflicting evaluations, creating confusion rather than clarity.
We solved this by implementing a shared memory layer, essentially giving agents awareness of each other’s assessments and reasoning. This innovation enables the formation of effective feedback loops, where agents can refine their evaluations based on insights from their specialized colleagues, much like human quality teams collaborate.
Shindan’s most significant ongoing challenge stems from its primary mission: reducing manual quality review by automatically approving content that meets defined standards. This allows expert linguists to focus exclusively on content requiring their specialized expertise, exactly what professional localization workflows need for efficiency and cost-effectiveness.
Shindan excels at this filtering function. Our testing shows over 90% accuracy in correctly identifying content requiring human review, with some language pairs achieving over 99% accuracy. The system very rarely misses genuinely problematic content.
Strictness can also be increased. While this ensures quality standards are never compromised, it can reduce the automation benefits we’re targeting. This represents the ongoing tension between automation and accuracy that defines professional AI deployment.
The journey from single AI to agentic network represents more than technical iteration, reflecting a fundamental shift in how we approach AI system design for professional applications. The key insight wasn’t about more powerful AI, but about better AI architecture that mirrors human expertise patterns.
Today, Shindan’s agentic network enables Alpha CRC to offer clients unprecedented quality management capabilities, supporting everything from traditional TEP workflows to cutting-edge machine-translation-first approaches. The technical foundation we’ve built ensures that as localization continues evolving, Shindan can adapt and scale with industry needs.
The evolution continues, but the architectural principles are proven: specialized agents, shared intelligence, and focused expertise deliver the reliability that professional localization demands.
