Generative AI Development Services

A fixed-price generative AI pilot costs $12K–$20K and takes about 6 weeks. One production capability, such as a copilot, a document generator or a RAG assistant, typically costs $20K–$60K over 8–12 weeks. A full system with data pipelines, evaluation, monitoring and several integrations runs $60K–$150K+. Our minimum engagement is $10K. Beyond the build, budget for per-use model costs and about 15–25% of the build cost per year for hosting, monitoring and updates. The AI development cost guide explains each driver.

A pilot takes about 6 weeks and a focused production app 8–12 weeks. The cost guide puts generative AI in-product features at roughly 6–14 weeks, depending on evaluation work, UX iterations and integrations. The first two weeks agree the use case, the examples that define good output and the success metric. Apps that must integrate with several systems, meet strict compliance requirements or run on self-hosted models take longer. You see a working prototype within the first weeks, so quality and cost are measured before the full build.

Generative AI development focuses on the application: what users do with generated output, how it fits their workflow and how quality is checked. LLM development focuses on the model layer: choosing, evaluating, fine-tuning, hosting and serving the large language model itself. Most generative AI apps use a hosted model through an API and never need model-level work. When a project needs a fine-tuned or self-hosted model, our LLM development services team handles that layer and the generative AI team builds the product on top.

Start with RAG in most cases. Retrieval-augmented generation answers from your current documents with citations, is quick to update and keeps proprietary data out of model training. Fine-tuning changes the model itself and suits fixed output formats, house style or specialist terminology, or cutting cost per call at high volume, but it does not add fresh facts. We fine-tune only when an evaluation shows a gap that prompting and retrieval cannot close. See RAG development services and LLM fine-tuning services.

Yes. Many of our generative AI projects add features to an existing web app, SaaS product, CRM or internal tool rather than building a new one. We connect to models through secure APIs, add retrieval over your data where needed, and surface the feature where users already work, such as a panel in your app, a CRM sidebar or a Teams bot. Usage, cost and quality are logged from day one. Our AI integration services page covers integration patterns for common platforms.

We mask PII and sensitive fields before data reaches a model API, use private cloud or self-hosted open-weight models when data cannot leave your environment, encrypt data in transit and at rest, and log prompts and outputs with user identity. Client data is never used to train models for other clients, and you own all code, prompts and outputs. VOCSO's delivery process is certified to ISO/IEC 27001. For regulated sectors we map GDPR, HIPAA or SOC 2 obligations to concrete controls during discovery.

We are model-agnostic. For text we typically benchmark OpenAI GPT models, Anthropic Claude and Google Gemini, plus open-weight models such as Llama and Mistral for self-hosted deployments. For images we pick a generation API per use case based on realism, style control and licensing. We compare candidates on your real examples for quality, latency and cost per request, and keep the model swappable so you can change provider later without rebuilding. Orchestration usually uses LangChain, LlamaIndex or LangGraph.