AI & Cloud Engineering

Intelligent Generative AIBuilt for Real-World Business

We design and engineer practical Generative AI products that turn language, knowledge and automation into secure experiences your teams and customers can actually use.

Build an AI ProductExplore Capabilities
GENERATIVE
AICYANOUS AI ENGINEERING
LLMsReasoning & generation
CopilotsHuman + AI workflows
AI SearchKnowledge discovery
ContentScale creation
AutomationAI-powered operations
GuardrailsSafe enterprise AI
What we build

Generative AI engineered around your workflow.

From the first prompt to a production-grade AI system, we connect models, business data, applications and governance into one dependable experience.

01

AI Copilots

Context-aware assistants for employees, operators, analysts, developers and customer-facing teams.

02

Enterprise Chat

Secure conversational interfaces connected to approved knowledge, tools, workflows and business rules.

03

Content Generation

Generate, transform, summarize and personalize text and structured content at scale.

04

Document Intelligence

Extract information from documents and convert unstructured material into useful business outputs.

05

AI Search

Natural-language discovery experiences that help users find answers across large information sets.

06

AI Automation

Combine models with APIs and business systems to automate repetitive knowledge-intensive processes.

07

Multimodal AI

Work with text, images, documents and other inputs where richer context improves the experience.

08

Model Integration

Design application layers that can work with the appropriate hosted or private model architecture.

09

AI Evaluation

Measure response quality, groundedness, latency, safety and operational performance before release.

Business solutions

Move from AI experiments to useful products.

Generative AI creates value when it is connected to the right data, permissions, interfaces and processes. We focus on that complete product layer—not just the model.

Customer support copilotsSummarize conversations, retrieve approved answers and assist support teams.
Internal knowledge assistantsGive teams a natural interface for policies, documents, procedures and knowledge bases.
Sales and marketing AIAccelerate research, content preparation, personalization and proposal workflows.
Developer productivitySupport code discovery, documentation, testing and engineering workflows.
Operations automationConnect AI reasoning with business APIs and human approval steps.
AI architecture

A production architecture, not a prompt box.

We separate the experience, orchestration, knowledge, model and governance layers so the system can evolve as your requirements change.

Experience LayerWeb apps • Mobile apps • Portals • Chat • Embedded copilots
↓
AI OrchestrationPrompts • Tool calling • Routing • Memory • Workflow control
↓
Knowledge & ContextBusiness data • Documents • Search • Retrieval • Permissions
↓
Model LayerHosted LLMs • Private models • Embeddings • Multimodal models
↓
Governance & ObservabilitySecurity • Evaluation • Logging • Cost • Quality • Human oversight
Technology stack

Flexible foundations for modern AI applications.

PythonTypeScriptOpenAI APIsAzure AIAWSGoogle CloudLangChainLlamaIndexVector DatabasesPostgreSQLRedisFastAPINode.jsDockerKubernetesCI/CD
Our process

From use case to measurable AI product.

Discovery

Define the business problem, users, data, constraints and measurable outcome.

AI Design

Select the model approach, interaction pattern, context strategy and guardrails.

Prototype

Validate the experience quickly with representative prompts, data and workflows.

Engineering

Build the application, integrations, retrieval, tools, permissions and observability.

Evaluation

Test representative cases for quality, safety, consistency, latency and cost.

Security

Apply access controls, data boundaries, privacy measures and operational safeguards.

Deployment

Release through controlled environments with monitoring and production support.

Optimization

Improve prompts, retrieval, models and workflows using real usage signals.

Design principles

AI that people can trust and control.

Grounded answers

Use approved context and retrieval strategies where factual business knowledge matters.

Human in the loop

Keep review and approval points where decisions require human ownership.

Least privilege

Connect AI to only the data and tools that the user and workflow are authorized to access.

Observable systems

Track quality, errors, latency, usage and cost so teams can operate the product confidently.

Model flexibility

Keep application architecture adaptable rather than coupling every feature to one model.

Continuous evaluation

Use representative test sets and production feedback to catch regressions as systems evolve.

Industries

Generative AI across business domains.

Financial Services

Knowledge assistants, document workflows, research support and controlled automation.

Healthcare

Information experiences, administrative workflows and document-focused applications with appropriate controls.

Retail & Commerce

Product discovery, customer support, content operations and personalization.

Manufacturing

Operational knowledge, technical documentation and intelligent workflow assistance.

Technology

Developer copilots, support automation, documentation and internal knowledge systems.

Education

Learning assistants, content transformation and personalized information experiences.

Logistics

Operations support, exception handling, document processing and communication workflows.

Professional Services

Research, drafting, knowledge management and workflow acceleration.

Security & quality

Enterprise controls around the intelligence layer.

AI applications need the same engineering discipline as any critical software system. We design access, data handling, testing and observability into the product from the beginning.

Data protectionDesign clear data boundaries and minimize unnecessary exposure of sensitive business information.
Access controlRespect application roles and permissions when retrieving knowledge or invoking tools.
Prompt & output safetyApply validation, filtering and policy controls appropriate to the application.
Evaluation suitesTest representative business scenarios before and after important changes.
Production monitoringObserve quality, errors, latency, token usage and operational health.
Why Cyanous

AI engineering connected to real product delivery.

Business-first discovery

We start with the workflow and measurable outcome rather than forcing AI into a problem that does not need it.

Full-stack capability

UI, APIs, cloud infrastructure, data and AI orchestration can be engineered as one product.

Production mindset

Security, testing, monitoring, cost and maintainability are considered before launch.

FAQ

Generative AI development questions.

Yes. Cyanous can build custom conversational applications connected to your approved knowledge, business APIs, authentication and workflow rules.
Yes. The application can be designed around controlled retrieval and access policies so users receive context they are authorized to use.
Not necessarily. Many business applications can use capable existing models with good prompting, retrieval, tools and evaluation. A custom model strategy can be considered when requirements justify it.
Yes. AI capabilities can be added through APIs and orchestration layers to existing web, mobile, portal, CRM, support or internal applications.
We define representative evaluation cases and measure factors such as relevance, groundedness, consistency, safety, latency and cost according to the use case.
Yes. A production architecture can separate model, orchestration, retrieval and application layers so capacity and components can evolve independently.

Turn your AI idea into a working product.

Tell us about the workflow, users and business outcome. We can shape the right Generative AI architecture and delivery plan around it.

Let’s Talk