LLM-powered applications built and deployed for real production use across customer-facing and internal workflows.
Retrieval-augmented systems that ground generative AI in your own data, documents, and internal knowledge bases.
Vision and OCR systems engineered to extract structure and meaning from images, documents, video, and physical-world data.
Multi-agent workflows that plan, decide, and act across tools and systems on behalf of the business.
Domain-specific models trained, fine-tuned, and benchmarked for the use cases that off-the-shelf models can't reach.
The infrastructure, monitoring, and operational practices that keep AI systems running reliably in production.
End-to-end data collection, annotation, and dataset development for AI projects that need high-quality training data at scale.
AI moves leadership from gut calls to grounded calls. Operational decisions get faster, sharper, and more defensible.
AI turns institutional knowledge into a working asset. Documents, conversations, and decisions become searchable and usable across teams.
AI shifts the economics of service, support, and sales. Conversations run faster, scale further, and resolve more on the first interaction.
AI takes routine, repetitive work off the team's plate. Manual processes move from human time to background operation.
AI surfaces what teams would otherwise miss. Anomalies, risks, demand shifts, and opportunities all become visible earlier.
AI lets products, services, and communications respond to individuals at the scale of the customer base. Personal becomes operational.
AI compounds the output of every analyst, designer, engineer, and operator. The team's work gets faster, sharper, and broader in reach.
Selected engagements across applied AI, cloud, integration, and technical talent deployment.
We work with you to define the problem, validate the use case, and map a delivery path against measurable business outcomes.
We assess the data you have, identify what's missing, and build the collection, annotation, and pipelines needed to support production-grade AI.
We select, build, train, or fine-tune the models the use case requires — from generative AI and RAG systems to custom vision, OCR, and agentic architectures.
We benchmark performance, evaluate for accuracy and bias, and validate the system against real-world scenarios before any release.
We ship the system into your environment — integrated with the workflows, applications, and infrastructure where it needs to operate.
We run the ongoing monitoring, model lifecycle management, and operational practices that keep AI systems performing reliably over time.
The technologies Cyberstack delivers on for applied AI engagements.
Microsoft Azure · GCP · AWS · STC Cloud
Hugging Face · Databricks · OpenAI · Anthropic · NVIDIA
PyTorch · TensorFlow · scikit-learn · Keras
LangChain · LlamaIndex · Pinecone · Weaviate
MLflow · Kubeflow · Weights & Biases
Apache Spark · Apache Airflow · Snowflake
Docker · Kubernetes · GitHub · GitLab
Python · SQL
