AI tools are fundamentally changing how software gets built — but adopting them without a clear-eyed strategy leads to inconsistent productivity gains, security gaps, hallucination-driven bugs, and a team that doesn't trust its own tooling. ZannoTech helps engineering teams cut through the noise: evaluate what's actually worth adopting, implement it safely, and build the habits required to use it well. On the developer tooling side, this means evaluating and deploying AI coding assistants (GitHub Copilot, Cursor, and others), establishing prompt-engineering best practices for software development contexts, and setting up code-review processes that account for AI-generated code quality and licensing risk. On the product side, LLM integration guidance covers architectural patterns for incorporating generative AI features — retrieval-augmented generation (RAG), semantic search, structured output parsing, function calling, and agent workflows — into production applications built on your existing stack. Guidance spans model selection, API integration, cost management, observability for AI-generated outputs, and responsible AI guardrails that keep your product compliant and trustworthy. Every AI tooling engagement is grounded in the same principle that applies to every other technology decision: adopt what genuinely improves outcomes, measure whether it's working, and don't let hype drive architecture.

What’s included

• Current tool stack audit and AI readiness assessment
• AI coding assistant evaluation, deployment, and configuration
• GitHub Copilot enterprise setup and policy configuration
• Prompt engineering workshops tailored to your stack and team
• LLM API integration: OpenAI, Azure OpenAI, Anthropic, or open-source models
• RAG pipeline design: embedding, vector store selection, retrieval tuning
• Structured output, function calling, and agent workflow design
• Observability for AI features: logging, evaluation, and cost monitoring
• Responsible AI guardrails: content filtering, PII handling, output validation

Getting started

Typical kickoff includes a short discovery, agreement on success metrics, and a roadmap to first value. For proposals or timelines, contact us.

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