Cruzata

From impressive demo to dependable product

Large language models can write, summarise, translate, classify and reason. Putting them into production safely is the hard part: grounding answers in trusted data, controlling cost and latency, evaluating quality and defending against misuse.

Cruzata designs generative AI systems with these concerns first, using retrieval-augmented generation, prompt engineering, evaluation harnesses and guardrails.

  • Retrieval-augmented generation (RAG)
  • Prompt, tool and workflow design
  • Evaluation and red-teaming
  • Cost and latency optimisation

What we build

Assistants
Chat over your data
Copilots
In-app helpers
Content
Drafts at scale
Extraction
Documents to data

Generative AI use cases

Knowledge assistants

Employees and customers get cited answers from your documents.

Content generation

Product descriptions, summaries and drafts in your brand voice.

Document intelligence

Extract fields from invoices, contracts and forms.

Developer copilots

Code search, review help and documentation generation.

Translation & localisation

Multilingual content with consistent terminology.

Support automation

Draft replies and deflect repetitive tickets.

RAG versus fine-tuning

Aspect RAGFine-tuning
Best for Up-to-date, company-specific factsStyle, format and specialised behaviour
Data changes Update the index instantlyRequires retraining
Citations Can cite source documentsNot by default
Cost to start LowerHigher

Generative AI delivery

01

Scope & data

Choose the use case and gather trusted sources.

02

Prototype

Build retrieval, prompts and a test UI.

03

Evaluate

Measure accuracy, safety and cost on real queries.

04

Harden

Guardrails, permissions, logging and caching.

05

Launch

Integrate, monitor and improve.

Generative AI technology

Foundation models

  • OpenAI GPT
  • Anthropic Claude
  • Google Gemini
  • Llama
  • Mistral
  • Stable Diffusion

Orchestration

  • LangChain
  • LlamaIndex
  • Semantic Kernel
  • OpenAI Assistants
  • MCP
  • Function calling

Retrieval & ops

  • pgvector
  • Pinecone
  • Weaviate
  • Elasticsearch
  • Langfuse
  • Redis cache
FAQ

Generative AI: frequently asked questions

Generative AI refers to models that create new content such as text, images, code or audio from prompts. In business, large language models are used for assistants, summarisation, extraction and content workflows.

We ground answers in your approved documents with retrieval, require citations, constrain the model with instructions and tools, and measure accuracy with evaluation sets. Where stakes are high we add human review.

Yes. We can use enterprise API terms with no training on your data, private cloud deployments or self-hosted open models, depending on your requirements.

We select by task, quality, cost, latency and data policy, and keep the architecture model-agnostic so you can switch as the field evolves.

Running cost depends on usage and model choice. We design with caching, smaller models where possible and usage monitoring to keep it predictable.

Start your generative ai project

Tell us what you are planning. We will reply within one working day with questions or a clear next step.