Articles on AI & ML.

Forecasting models, computer vision, LLM agents — integrated directly into business workflows, not shelved POCs.

Z.ai GLM-5.2: China's coding model matches GPT-5.5 at roughly a sixth of the cost

Z.ai's GLM-5.2 scores near Claude Opus 4.8 and ahead of GPT-5.5 on coding benchmarks, while operating at roughly a sixth of the cost. KonexForge is adding it to the list of supported models to give clients another cost-optimization option — not a replacement, but an addition to the existing model-routing layer.

01/07/20267 min read

AI & MLDevelopmentAI OrchestratorLLM

KonexForge AI Core: when the AI Orchestrator becomes the central brain of your enterprise ecosystem

Not a chatbot, not an LLM wrapper — AI Core is a unified orchestration layer that connects every AI specialist, enterprise tool, and internal data source into a single automated pipeline. An 8-component architecture, local/cloud routing, and a Critic Engine are the real differentiators.

27/06/20268 min read

AI & MLDevelopmentSEOGEO

Legacy websites are falling behind: when SEO isn't enough and GEO doesn't exist

Users are asking ChatGPT, Perplexity, and Google AI Overviews instead of clicking links. If your website isn't cited by AI, you're invisible to a growing share of new searchers — even if you rank #3 on Google.

26/06/20267 min read

AI & MLVision-LanguageData DigitizationOCR

AI Entry: digitizing image data with Vision-Language technology — replacing 50 data-entry workers in a single deployment

Most businesses still digitize invoices, forms, IDs, and handwritten ledgers with dozens of manual data-entry workers. AI Entry uses Vision-Language (VL) models to read images and emit structured data directly — why VL differs from OCR, the confidence + human-in-the-loop pipeline architecture, and what 'replacing 50 workers' actually means.

24/06/20268 min read

AI & MLRAGLLMVector SearchMLOps

RAG pipelines in production: chunking strategy, vector search, and retrieval quality evaluation

RAG (Retrieval-Augmented Generation) is the most common architecture for grounding LLM answers in internal data — but most first implementations only work well in demos, not in production. Chunking strategy affects recall; embedding model affects precision; without a retrieval evaluation pipeline, there is no way to know where the system is failing. The three most important technical decisions and how to measure quality before deployment.

21/06/20269 min read

AI & MLGPUVRAMHBM

How important are VRAM and HBM in AI infrastructure: from inference to fine-tuning

When choosing AI infrastructure, FLOPS usually gets mentioned first — but in practice, VRAM capacity and HBM bandwidth are often what determine which models can run, at what batch size and latency. How to size VRAM for production, why HBM differs from GDDR, and when you need multiple GPUs.

15/06/20267 min read

AI & MLQwen3-VLComputer VisionOCR

Deploying Qwen3-VL for computer vision in production: from OCR to visual inspection

Qwen's latest Vision-Language Model unlocks a class of computer vision problems that traditional OCR and CV pipelines struggle with — but production deployment requires a different mindset than calling a demo API. Use-case selection criteria, model sizing, and how we integrate it into Pilot Builds at KonexForge.

10/06/20267 min read

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