Generative AI Development Services

Frequently Asked Questions

• Q:

  What generative AI solutions does Azumo build?

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  Azumo builds custom generative AI applications that create text, images, code, audio, and video from your proprietary data. Projects include automated content generation systems, AI-powered code assistants, document drafting platforms, conversational AI interfaces, and synthetic data pipelines. We built a generative AI voice assistant for a gaming company, deployed real-time generative AI on Omnicom's 24/7 content platform, and created an AI-powered supplier search tool for Meta that uses NLP to parse unstructured vendor data. Our stack spans OpenAI GPT-4o, Anthropic Claude, LLaMA, Mistral, Qwen, DeepSeek, and Stable Diffusion. We deploy on AWS Bedrock, Azure OpenAI, and Google Vertex AI with fine-tuning and RAG capabilities to ground outputs in your data. SOC 2 certified. Nearshore engineering teams across Latin America working in US time zones.

• Q:

  Why should a company invest in custom generative AI development instead of using off-the-shelf tools?

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  Off-the-shelf generative AI tools produce generic outputs trained on public data. Custom development lets you train on your proprietary documents, enforce your brand voice, and embed compliance guardrails specific to your industry. A custom solution also gives you control over model selection, cost per inference, and data privacy: your proprietary data never leaves your infrastructure. Azumo clients invest in custom generative AI when they need domain-specific accuracy that ChatGPT or Gemini cannot match, when sensitive data cannot reach third-party APIs, or when they want AI capabilities embedded directly in their product. Common triggers include content teams needing 10x output at consistent quality, legal teams automating contract review, and engineering teams building AI-assisted development tools. Our nearshore model delivers enterprise-quality generative AI at 30-50% lower cost than equivalent US-based teams.

• Q:

  What is the difference between generative AI and traditional AI?

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  Traditional AI classifies, predicts, and optimizes based on existing data patterns. Generative AI creates new content: text, images, code, audio, and video. Traditional AI answers 'what category does this belong to?' or 'what will happen next?' Generative AI answers 'create something new that meets these criteria.' In technical terms, traditional AI uses supervised learning for classification and regression. Generative AI uses transformer architectures, diffusion models, and GANs to produce novel outputs. In practice, most production systems combine both. Azumo builds systems where a generative model drafts content while traditional ML models score quality, check compliance, or route outputs. Example: an LLM generates a contract clause, then a classification model checks it against regulatory requirements and flags exceptions for human review.

• Q:

  What generative AI models and frameworks does Azumo work with?

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  Azumo works with OpenAI GPT-4o and o1, Anthropic Claude 3.5 and Claude 4, LLaMA 3, Mistral, Qwen, DeepSeek, and Stable Diffusion for image generation. We build with LangChain, LangGraph, LlamaIndex, Hugging Face Transformers, and CrewAI. Cloud deployment spans AWS Bedrock, Azure OpenAI, and Google Vertex AI. For retrieval-augmented generation, we integrate Pinecone, Weaviate, Chroma, and Qdrant. Valkyrie, our internal AI infrastructure platform, provides a single REST API to any LLM, image model, or fine-tuned model running across AWS, RunPod, and Hetzner. This lets your team switch providers without rewriting application code. We are technology-agnostic: model selection depends on your accuracy requirements, latency targets, cost per token, and data residency constraints.

• Q:

  How long does it take to build a custom generative AI application?

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  A proof-of-concept generative AI application can be delivered in 1-2 weeks. Production-ready applications with enterprise integrations typically take 4-9 months depending on data preparation, number of system integrations, fine-tuning scope, and compliance requirements. Azumo accelerates delivery using pre-built components: Valkyrie for model routing and infrastructure, established RAG pipelines for knowledge grounding, and evaluation frameworks for quality assurance. For clients with clean data and well-defined requirements, we have shipped production generative AI systems in as few as 6 weeks. Our dedicated nearshore teams work in US time zones with daily standups and sprint-based delivery. We start every engagement with a Discovery phase that aligns business objectives, technical feasibility, and data readiness before committing to a full build.

• Q:

  How does Azumo prevent hallucinations in generative AI systems?

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  Azumo reduces hallucination through four techniques: retrieval-augmented generation (RAG) that grounds outputs in your actual documents and databases, fine-tuning on verified domain data to teach the model your domain's facts and terminology, structured output validation that checks generated content against source material, and confidence scoring that flags low-certainty outputs. We also implement prompt engineering with system-level instructions that constrain the model to cited sources. For high-stakes applications in healthcare, legal, and financial services, we add human-in-the-loop review and build escalation paths when confidence falls below defined thresholds. Our evaluation frameworks measure hallucination rates on your specific content types before deployment and monitor drift continuously in production using tools like LangSmith.

• Q:

  What industries use generative AI most effectively?

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  Generative AI delivers measurable ROI in financial services, healthcare, legal, marketing, media, and software development. Financial firms use it for research summarization, earnings report generation, and compliance document drafting. Healthcare organizations automate clinical note generation, patient communication, and medical literature review while maintaining HIPAA compliance. Legal teams accelerate contract analysis, brief drafting, and regulatory research. Marketing agencies scale personalized content across channels: Azumo built scaled AI-content generation capabilities for a large marketing agency and deployed real-time generative AI on Omnicom's 24/7 platform. Software teams use AI-assisted code generation, documentation, and test generation. Azumo also built an AI-powered search tool for Meta that processes unstructured supplier data using NLP.

• Q:

  What security measures does Azumo implement for generative AI projects?

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  Azumo is SOC 2 certified and implements end-to-end encryption, role-based access controls, prompt injection detection, output filtering, and comprehensive audit logging. We prevent sensitive data from reaching third-party model APIs through PII detection and removal, data masking, and on-premises deployment options. For regulated industries, we implement HIPAA, GDPR, and PCI-DSS compliance controls. Generative AI introduces unique security risks: prompt injection attacks, data leakage through model outputs, and adversarial manipulation. We address these with input validation, output scanning for PII, monitoring for adversarial usage patterns, and guardrails that constrain model behavior to approved use cases. We can deploy generative AI entirely within your private cloud or on-premises infrastructure when data sovereignty requires it.