Generative AI Development Services

Generative AI development services cover the design, development, and deployment of systems that generate text, images, code, audio, or video using foundation and large language models. The work spans use case discovery, data preparation, model selection, RAG, fine-tuning, guardrails, integration, and post-launch monitoring.

The goal is a production system grounded in your data, not a demo built on a public model.

AI is the broad field of systems that perform tasks requiring human judgment. Machine learning is the subset where systems learn patterns from data to predict or classify. Generative AI is the subset that creates new content, such as text, images, or code, using foundation models trained on large datasets.

Most production systems combine them: generative AI for output, machine learning for prediction, and traditional engineering for everything around both.

Generative AI produces content in response to a prompt: it answers, writes, or creates. Agentic AI plans and acts, breaking a goal into steps, calling tools, and completing a task across your systems without step-by-step instruction.

Agentic systems are usually built on generative models, so agentic AI is generative AI with planning, access to tools, and autonomy layered on top.

RAG, or retrieval-augmented generation, retrieves relevant content from your knowledge base and gives it to the model as context before it answers. This grounds the output in your verified data rather than the model's training data.

Enterprises need it because a foundation model does not know their policies, products, or documents; without retrieval, it will guess. RAG reduces hallucinations and makes answers traceable to their sources.

We ground the system in your content via retrieval, so answers come from verified sources rather than from open-ended generation. We constrain output where accuracy is critical, add fallback and escalation paths for low-confidence answers, and evaluate against benchmarks before launch.

After deployment, we monitor output for drift because accuracy is maintained, not achieved once.

Use human-in-the-loop when a wrong answer carries real cost: regulated advice, clinical or financial content, customer-facing claims, or anything that triggers an action. A reviewer approves output before it lands.

We design the review step into the workflow rather than adding it after an incident, and we narrow it over time as evaluation data shows where the system is reliable.

Generative AI development cost depends on model strategy, data readiness, accuracy requirements, integration scope, and compliance. Prompting a foundation model costs the least, RAG sits in the middle, and fine-tuning or domain-specific model development costs the most.

Inference cost also continues after launch, driven by model choice and traffic. We give a detailed estimate within five business days of a scoping call.

Timelines depend on complexity and data readiness. A focused feature on a foundation model moves fastest; a RAG system takes longer because content must be prepared and evaluated, and fine-tuned or domain-specific models take the longest.

The most common cause of delay is data that is not ready, which is why we start with a readiness assessment.

Yes, and we check integration compatibility before any design decision. We connect generative AI to your platforms, CRMs, ERPs, and data systems through clean API design and pipelines, so it works inside your current stack rather than forcing a rebuild.

We review your architecture, data, and authentication in discovery so integration issues surface before production.

Yes. Where data cannot leave your environment, we deploy generative AI on-premises or in your private cloud, using self-hosted models and designing retrieval and storage to remain within your boundary.

This is common in healthcare, financial services, and government work, and we scope hosting during discovery because it shapes model choice.

We define governance at the architecture stage: access controls, audit logs, data handling, output moderation, and human review where risk warrants it. For regulated work, we align with GDPR, HIPAA, SOC 2, and ISO 27001 requirements from the first technical decision.

AI-specific regulation continues to develop, including in the EU, so we design for traceability and documentation rather than assuming today's rules are final.

Yes. For regulated or specialized fields, we build domain-specific models tuned to your language, data, and rules, either by fine-tuning a foundation model or by combining retrieval with tightly scoped evaluation.

We decide between the two based on your accuracy bar, data volume, and privacy constraints, since fine-tuning is not always the right answer.

You do. Everything we build belongs to you, including fine-tuned models, prompts, pipelines, source code, and data. There is no lock-in and no ongoing dependency on Cypherox.

Ownership is set out in the contract from the start, and we sign NDAs before any data is shared.

Yes. We monitor output quality, cost, and latency after launch, watching for drift as your data and the underlying models change, and tune on a regular cycle to hold performance.

Support terms are set per engagement, from a defined maintenance window to ongoing model governance for teams who do not want to manage it internally.