Generative AI applications
Build assistants, knowledge search, summarization, workflow copilots, and domain-specific LLM applications using secure enterprise data patterns.
Applied AI
Applied and generative AI that moves from prototype to production.
Cybzilla designs and builds AI systems that improve operations, decisions, customer experience, and knowledge work, with governance built into the delivery model.
Plan an AI use caseApplied AI that earns its place in production
Most AI initiatives stall between a promising demo and a dependable product. The model works on a curated sample, then meets real data, real users, and real security reviews. Cybzilla builds AI systems for that second stage: grounded in your data, measured against agreed success criteria, and designed with the governance enterprises need before they can scale.
We build generative AI applications, retrieval augmented generation (RAG), and classic machine learning, and choose between them based on the problem rather than the hype. Our work is model-agnostic, using commercial models such as GPT, Claude, and Gemini, or open-weight models deployed in your own cloud when data residency or cost requires it.
Challenges we solve
- Pilots that never shipProofs of concept impress in a demo but lack evaluation, security review, and integration, so they never reach real users.
- Answers you cannot trustGeneric chatbots hallucinate, cite nothing, and ignore your policies, which rules them out for customer-facing or regulated work.
- Data that is not readyKnowledge is spread across documents, tickets, and systems with inconsistent access rules, so the AI sees either too little or too much.
- No way to measure valueWithout baselines and success metrics, no one can tell whether the AI is saving time, improving decisions, or just adding cost.
What we deliver
Machine learning systems
Develop predictive models, recommendation systems, NLP, computer vision, and analytics pipelines that integrate into real business processes.
MLOps and governance
Operationalize models with deployment pipelines, monitoring, evaluation, human review, data controls, and responsible AI practices.
How we work
Discover the use case
Rank candidate use cases by business value, data availability, and risk, and pick one with a clear owner and a measurable outcome.
Prepare the data
Connect sources, clean and structure content, carry access controls through, and build the retrieval or feature pipelines the model relies on.
Build and evaluate
Develop the application with evaluation sets, guardrails, and human review built in, and test accuracy, safety, latency, and cost before launch.
Deploy and integrate
Ship into the tools people already use, such as your intranet, CRM, ticketing system, or workflow platform, with authentication and audit logging.
Monitor and improve
Track quality, drift, usage, and spend in production, and keep refining prompts, retrieval, and models as data and needs change.
Technologies and platforms
- Foundation models
- OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Mistral, and other open-weight models
- AI platforms
- Azure OpenAI Service, Amazon Bedrock, Google Vertex AI, Hugging Face
- RAG and orchestration
- LangChain, LlamaIndex, and vector stores such as pgvector, Pinecone, Weaviate, and OpenSearch
- Machine learning and MLOps
- Python, PyTorch, scikit-learn, MLflow, Databricks, Amazon SageMaker, Azure Machine Learning
Not sure where to start?
Most engagements begin with a short, scoped assessment that gives you a clear picture of where you stand, a prioritized plan, and an estimate for the next step.
Applied AI questions
Does Cybzilla build generative AI applications?
Yes. Cybzilla builds LLM applications, RAG systems, AI assistants, workflow copilots, and enterprise knowledge search experiences.
How does Cybzilla reduce AI project risk?
AI work starts with use-case discovery, data readiness, security review, measurable success criteria, and a production deployment plan.
What is retrieval augmented generation (RAG)?
RAG connects a language model to your own documents and data. When someone asks a question, the system retrieves the most relevant content and the model answers from it with citations, which makes responses more accurate and easier to verify.
Is our data used to train public AI models?
No. We build on enterprise model services and private deployments configured so your prompts and data are not used to train public models, and access rules from your source systems carry through to the AI application.
How do you measure whether an AI project worked?
Success metrics are agreed before build starts, such as time saved per task, answer accuracy on an evaluation set, ticket deflection, or forecast error. They are measured against a baseline and tracked after launch.
Can you work with open-source models?
Yes. When data residency, cost, or customization requires it, we deploy and fine-tune open-weight models such as Llama or Mistral in your own cloud environment.