We build the AI systems your business runs on.

We engineer production AI across three pillars: LLMs and agentic NLP — LangChain-powered RAG pipelines, LangGraph-orchestrated multi-agent workflows, and fine-tuned models (LoRA / QLoRA) with LangSmith observability; computer vision — Vision Transformers, object detection, medical imaging, and edge inference; and AI software automation — combining LLMs and CV into unified platforms that automate complex, judgment-heavy business operations. Every system is designed for production — not just proof-of-concept.

Yes. Our embedded engineering team specializes in production-ready software close to the metal. This includes Classical and Adaptive AUTOSAR stacks, ECU software with full communication (CAN, LIN, Automotive Ethernet) and diagnostic (UDS, XCP) support, Embedded Linux with custom Yocto builds, and RTOS-based firmware. We also work on OTA update frameworks and cryptographic modules for connected vehicle platforms.

Absolutely. A core part of our offering is bridging the gap between cloud-trained models and edge hardware. We optimize inference pipelines using TensorRT, ONNX Runtime, and custom C++ backends, and we build the surrounding Embedded Linux or AOSP system that hosts them. Our models run efficiently on Jetson devices, ARM SoCs, and industrial embedded hardware — without cloud dependency.

We work across automotive and mobility, healthcare and medical imaging, industrial automation, and enterprise software. Common threads: environments where reliability matters, data is sensitive or complex, and off-the-shelf AI tools don't cut it. If your challenge involves visual data, ECU or embedded hardware, or automating complex operations with LLMs and CV, we're a strong fit.

Both. We've worked with early-stage startups that needed a senior technical team to build their core AI product, and with established enterprises running AUTOSAR-based vehicle programs or large-scale clinical imaging platforms. We scale our engagement model to match: from focused sprint-based projects to longer-term embedded partnerships.

Our engineering and leadership teams are distributed between Canada and Egypt, with additional team members working remotely across EMEA and North America. This lets us serve clients across both North American and European time zones with a senior, responsive team. We work asynchronously by default and schedule live sessions around your timezone.

The best starting point is a technical consultation — a focused 45-minute session where we review your architecture, data, and goals, then come back with a clear assessment and proposed path forward. No pitch decks, just engineering conversation. You can request one through the form on this page or reach us directly at hr@wyvtech.com .

Our RAG architecture starts with document ingestion and chunking strategies tuned to your content type, then builds a hybrid retrieval layer combining dense vector search (Pinecone, pgvector, Weaviate, or FAISS) with sparse BM25 retrieval. We add a cross-encoder reranking step and query decomposition for complex multi-hop questions before the LLM generates a grounded, cited response.

For agentic workflows, we use LangGraph to build stateful, graph-structured agent pipelines that support cyclical reasoning, human-in-the-loop checkpoints, and persistent memory across sessions. Agents are equipped with tool libraries — web search, code execution, database queries, API calls — and we instrument everything with LangSmith for full tracing, evaluation, and production monitoring. We work with OpenAI, Anthropic Claude, Mistral, and self-hosted Llama and Qwen models based on your data residency and cost requirements.