At ZYNO Tech, we guide enterprises to adopt Generative AI (GAI) not as a novelty but as a measurable business capability. From governance and pilot validation to large-scale copilots and multimodal systems, our approach converts experimentation into repeatable ROI.
Our Generative AI offering is end-to-end — combining strategy, engineering, and enterprise-grade controls.
Readiness assessment, opportunity prioritization, governance, and ROI mapping.
Custom LLM fine-tuning, multimodal model development (text, image, audio, video), and prompt engineering.
Employee and customer copilots embedded into CRMs, ERPs, and knowledge bases.
Scalable content generation for marketing, product descriptions, personalized communications, and code generation.
Hosted, secure model endpoints and APIs for rapid integration and consumption.
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A structured approach guiding every AI initiative from strategy and data to model deployment, integration, and continuous optimization, interlinked with specialized ZYNO Tech services for deeper exploration.
Readiness & Discovery
Strategy & Roadmap
Model & Data Setup
Prototype & Validation
Integration & Deployment
Governance & Continuous Optimization
Domain-Specific Enhancements
We start with workshops to understand business goals, workflows, and data maturity. This step uncovers the highest-impact AI opportunities with measurable business value.
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We design with enterprise security, scale, and flexibility in mind. Typical stack elements we deploy.
We move beyond generic experiments to deliver consistent, measurable outcomes for enterprises.
Every model is linked to KPIs to ensure measurable value, not stalled proofs-of-concept.
From strategy and data to models, integration, governance, and continuous operations, all managed by a single accountable team.
Bias mitigation, human-in-the-loop controls, and enterprise-grade security compliance.
Proven GAI deployments across retail, finance, healthcare, logistics, and manufacturing.
Start with a narrow, high-frequency use case (e.g., customer reply drafting, product description generation, or internal knowledge retrieval). Build an MVP that measures concrete KPIs (time saved, accuracy, CSAT delta) and iterate.
Both. We can leverage third-party APIs for speed (OpenAI, Anthropic, Gemini) or host fine-tuned/private models for data-sensitive workloads or cost optimization. We advise a hybrid approach when sensible.
We combine retrieval-augmented generation (RAG), strict prompt engineering, provenance tagging, answer verification steps, and post-generation fact-checking pipelines to reduce hallucinations materially.
Data encryption in transit and at rest, role-based access, audit logging, SOC/GDPR/HIPAA guardrails where required, and private-hosted inference for sensitive data.
We optimize via hybrid inference (mix of smaller task-specific models + LLM for complex prompts), token-level optimizations, caching, and batching; and implement cost monitoring with budget alerts and model selection guidelines.