STATUS: PRE-CONSTRUCTION · SITE A UNDER EXCLUSIVITYNODE: VASILIKOS-01 — 34.7246°N, 33.2247°ECAMPUS: RISC-V PHASE 1 · MULTI-SILICON EVAL · PLANNEDPOWER: 42MW ON-SITE GENERATION · DESIGN TARGETSTATUS: PRE-CONSTRUCTION · SITE A UNDER EXCLUSIVITYNODE: VASILIKOS-01 — 34.7246°N, 33.2247°ECAMPUS: RISC-V PHASE 1 · MULTI-SILICON EVAL · PLANNEDPOWER: 42MW ON-SITE GENERATION · DESIGN TARGET

§ 01 AI Factory · Overview

AI Factory
Token Production at Scale

The AGICY AI Factory is a complete, end-to-end platform for producing AI tokens at industrial scale. From data ingestion and model training to real-time serving and continuous monitoring — targeting over 10 billion tokens per day on sovereign RISC-V infrastructure.

Tokens/Day

10B+

End-to-End

E2E

Scaling

Auto

Monitoring

24/7

§ 02 Architecture · Factory stack

Factory Architecture

The AI Factory is organized into three core stages — ingestion, training, and serving — each independently scalable and continuously monitored.

Data Ingestion

The ingestion stage is the factory's input layer, responsible for acquiring, cleaning, transforming, and validating all data that flows into the training and serving pipelines. The AGICY ingestion engine supports structured data (databases, APIs, CSV/JSON), unstructured data (documents, PDFs, web pages), and multimodal inputs (images, audio, video). All data undergoes automated quality checks including deduplication, PII detection, language identification, and toxicity filtering before entering the pipeline. The ingestion system processes data in real-time streaming mode for continuous updates or batch mode for bulk imports. All data remains within EU-sovereign infrastructure throughout the entire ingestion process — no data ever leaves the AGICY facility for processing by third-party services.

Real-Time Streaming

Model Training

The training stage handles fine-tuning, continued pre-training, and RLHF alignment of AI models on your proprietary data. The AGICY training orchestrator automatically provisions Tenstorrent Galaxy servers, distributes training across multiple nodes using data parallelism and tensor parallelism, and manages checkpointing for fault tolerance. Training jobs support LoRA, QLoRA, full fine-tuning, and DPO alignment methods. The orchestrator optimizes hyperparameters automatically using Bayesian search, monitors training loss in real-time, and detects convergence issues before they waste compute. Completed models are automatically validated against held-out test sets, evaluated for safety and alignment, and registered in the model registry for deployment to the serving stage.

Auto-Orchestrated

Production Serving

The serving stage deploys trained models to production inference endpoints with zero-downtime blue-green deployments. The AGICY serving layer automatically handles model quantization, batching optimization, KV-cache management, and speculative decoding to maximize throughput on Tenstorrent Galaxy hardware. Auto-scaling provisions additional Galaxy servers within minutes based on real-time demand signals, scaling down gracefully during low-traffic periods to minimize cost. The serving layer exposes an OpenAI-compatible API, making it instantly compatible with any application built for OpenAI, Azure, or Anthropic APIs. A/B testing of model versions, canary deployments, and automatic rollback on quality degradation are built into the serving pipeline.

Zero-Downtime Deploy

§ 03 Pipeline · Production

Production Pipeline

Every stage of the AI Factory pipeline is instrumented, monitored, and optimized for maximum throughput and quality.

Pipeline Stage	Capability	Throughput	SLA
Data Ingestion	Structured, unstructured, multimodal	100TB/day	99.9%
Data Processing	Dedup, PII, quality filtering	50B tokens/day	99.9%
Fine-Tuning	LoRA, QLoRA, full, DPO	Multi-node distributed	99.5%
Model Validation	Safety, accuracy, alignment	Automated per checkpoint	100%
Serving (Inference)	Streaming, batch, function calling	612K tok/s per server	99.9%
Monitoring	Latency, throughput, quality, drift	Real-time dashboards	24/7
Auto-Scaling	Demand-based capacity management	Minutes to provision	Automatic

§ 04 QA · Quality

Quality Assurance

Production AI demands production-grade quality controls. The AI Factory embeds quality assurance at every stage of the token production pipeline.

Output Quality Monitoring

Every token produced by the AI Factory passes through a multi-layer quality monitoring system. Real-time evaluators score model outputs for relevance, coherence, factual accuracy, and safety compliance. Statistical process control charts detect quality drift before it impacts end users, triggering automatic alerts when output quality deviates beyond acceptable thresholds. The monitoring system maintains rolling benchmarks against reference evaluation datasets, providing continuous visibility into model performance over time. Detailed quality reports are generated daily with per-model, per-endpoint, and per-customer breakdowns that enable data-driven decisions about model updates and pipeline optimization.

Real-Time Scoring

🔄

Continuous Evaluation

The AI Factory runs continuous evaluation loops that compare model outputs against curated benchmark datasets, human preference data, and domain-specific evaluation criteria. Automated regression testing catches performance degradation after model updates or infrastructure changes. The evaluation framework supports custom metrics defined by customers for domain-specific quality requirements — whether that's medical accuracy for healthcare applications, legal precision for compliance use cases, or creative quality for content generation. All evaluation data is stored in a sovereign evaluation ledger that provides complete auditability of model quality over the entire operational history of your AI Factory instance.

Automated Testing

🛡️

Safety & Compliance

The AI Factory integrates safety guardrails at both the input and output stages of the serving pipeline. Input filters detect and block prompt injection attacks, jailbreak attempts, and requests for harmful content. Output filters verify that generated content meets safety policies before delivery to end users. The compliance engine enforces EU AI Act requirements including risk classification, transparency logging, and human oversight triggers for high-risk applications. All safety evaluations, policy decisions, and override events are recorded in tamper-proof audit logs that can be provided to regulators on demand. AGICY's safety framework is continuously updated to address emerging threat vectors and evolving regulatory requirements.

EU AI Act Ready

§ 05 Questions · FAQ

Frequently Asked Questions

Common questions about the AGICY AI Factory and token production at scale.

What is the maximum capacity of the AI Factory?

The AGICY AI Factory is designed for horizontal scalability with no hard capacity ceiling. The baseline configuration produces over 10 billion tokens per day using a cluster of Tenstorrent Galaxy servers. Capacity can be expanded by adding additional Galaxy servers to the fleet — each server adds 612,000 tokens per second of inference throughput. For training workloads, the factory supports distributed training across hundreds of Galaxy nodes. The auto-scaling system dynamically provisions and deprovisions servers based on real-time demand, ensuring you only pay for the capacity you actually use. Enterprise customers can reserve dedicated capacity with guaranteed minimums for mission-critical applications that require predictable throughput.

Can I customize the AI Factory pipeline?

Yes. The AI Factory is fully configurable at every stage. You can define custom data ingestion connectors for your specific data sources, configure processing pipelines with custom quality filters and transformation steps, choose training methods and hyperparameter ranges, and set custom evaluation metrics for your domain. The pipeline configuration is managed through a declarative, version-controlled interface that enables reproducible pipeline definitions. Enterprise customers can also deploy custom preprocessing and postprocessing functions that run within the sovereign infrastructure, ensuring that proprietary business logic never leaves your controlled environment.

How does monitoring and alerting work?

The AI Factory provides comprehensive observability through three layers of monitoring. Infrastructure monitoring tracks GPU utilization, memory, network, and storage metrics across all Galaxy servers. Application monitoring tracks inference latency, throughput, error rates, and queue depths at each stage of the pipeline. Quality monitoring tracks model output scores, safety filter activations, and evaluation benchmark results over time. Alerts can be configured for any metric with customizable thresholds and notification channels (email, Slack, PagerDuty, webhooks). A real-time dashboard provides at-a-glance visibility into the health and performance of your entire AI Factory, with drill-down capabilities for root cause analysis.

How does auto-scaling work in the AI Factory?

The AI Factory's auto-scaling system uses predictive and reactive scaling algorithms to manage capacity. Reactive scaling monitors real-time metrics like request queue depth, latency percentiles, and GPU utilization to trigger immediate scale-up when demand spikes. Predictive scaling uses historical usage patterns to pre-provision capacity before anticipated demand increases, such as business-hour ramps or seasonal peaks. The scaling system respects configured minimum and maximum boundaries, ensuring cost control while maintaining performance guarantees. Scale-up operations complete within minutes as pre-warmed Galaxy servers are brought online with model weights already loaded. Scale-down is graceful — servers are drained of active requests before being returned to the pool, ensuring no in-flight requests are dropped.

§ 06 Get Started · Contact

Build Your AI Factory

Start producing AI tokens at industrial scale on sovereign infrastructure. End-to-end pipeline, auto-scaling, 24/7 monitoring.

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