Tajikistan Accelerates Industrial License Processing From Days to Seconds With NVIDIA AI

Government agencies in Tajikistan responsible for business permits and licenses were operating almost entirely on paper and scanned PDFs. Staff extracted data field by field from every incoming document, manually cross-checking records across multiple disconnected systems. A single application could take between 14 and 30 days to clear, and each case consumed up to 60 minutes of skilled staff time. During peak periods, backlogs mounted and businesses waited. 

The problem ran deeper than volume. Documents arrived in Tajik, Russian, and English, requiring multilingual processing that legacy systems simply could not support. Existing infrastructure also lacked the modern application programming interfaces (APIs) needed to integrate newer tools. And unlike many organizations, Tajikistan’s government agencies couldn’t route any AI workload to public cloud providers. Data-residency mandates required every citizen record to remain within national borders.

Building the language capability from scratch added another dimension. Tajik accounts for less than 0.01% of Common Crawl—effectively absent from the training data of today’s leading AI models. Any platform capable of genuinely serving Tajikistan’s agencies would need to understand Tajik natively, which meant developing a national language model purpose-built for the task. What the government needed was an AI ecosystem—built for Tajik, hosted in-country, and powerful enough to process complex government documents at production scale.

Tajikistan’s answer was to bring together three specialist partners, each owning a distinct layer of a shared NVIDIA-powered stack.

Darya.ai operates the compute foundation: From its sovereign data center in Darvoz, building on earlier development phases, the platform has transitioned to a fully in-country setup. Current training, inference, and related compute workloads now run on a Supermicro server equipped with NVIDIA H200 GPUs, using the NVIDIA CUDA stack. This setup supports national data-sovereignty requirements.

Zehnlab.ai built the language intelligence layer. Starting from open-weight checkpoints, zehnlab.ai performed Tajik-only continual pretraining on a curated corpus and instruction tuning on tens of thousands of Tajik-language examples. The result is Soro 27B (SoroLLM), Tajikistan’s first national large language model, unveiled in June 2025 with the participation of President of the Republic of Tajikistan, H.E. Emomali Rahmon. Soro 27B outperforms every openly available model of comparable size on a six-benchmark Tajik evaluation suite. Building on this foundation, the team is evaluating NVIDIA NeMo for post-training and distributed training across multi-GPU H200 nodes in future model cycles.

Simultaneously, zehnlab.ai developed Soro Guard 1B—the first content-safety model for the Tajik language. Rather than adapting an off-the-shelf guard model (none supported Tajik), zehnlab.ai used NVIDIA’s Nemotron Content Safety dataset and taxonomy as the foundation, extended it to Tajik and Russian, and modeled the architecture on NemoGuard—NVIDIA’s reference design for prompt and response safety classification. Soro Guard 1B achieves unsafe-F1 scores above 89% across Tajik, Russian, and English.

Epsilon3.ai built the Ijozat AI application layer: a set of containerized microservices orchestrated via Kubernetes and Docker that route each licensing application through a pipeline of specialized AI agents. An OCR agent extracts structured data from scanned documents in Tajik, Russian, or English. A classification agent identifies document type and determines routing. Validation agents cross-check identity and legal fields against government records. A risk-detection agent flags anomalies for human review.

SoroLLM sits at the center of the pipeline—interpreting regulatory requirements, answering applicant questions, and generating the structured decision summaries that permit officers review in place of raw document stacks. GPU-accelerated inference is hosted at darya.ai in Tajikistan on NVIDIA infrastructure using the CUDA stack. To support future growth, the team is evaluating NVIDIA TensorRT-based serving for Soro 27B, creating a clear path to higher throughput as demand scales.

The results represent a fundamental shift in how Tajikistan serves its business community. A process that once consumed 14 to 30 days now completes in approximately 90 seconds—end to end—with every decision captured in a full audit trail. More than 100 licenses have already been issued through the platform, and the system supports workflows across three languages and more than 150 document types. 

For permit officers, the transformation is equally significant. Rather than spending shifts on manual data entry and cross-system lookups, staff now receive AI-generated structured summaries and focus on exceptions—a shift that has reduced repetitive workload by an estimated 50%–60%. As one permit officer noted: “I now receive structured summaries instead of stacks of unprocessed documents—Ijozat AI saves me time on every case and lets me focus on decisions.”

Equally important for Tajikistan’s long-term AI strategy: The same infrastructure that runs the licensing platform also supports SoroLLM serving and Soro Guard safety moderation. As AI models advance, the government can adopt more capable versions without expanding its hardware footprint. Throughput scales with infrastructure, not headcount—and peak-period backlogs are now a problem of the past. The impact on staff is just as significant as results made the case for something broader.