Generative AI Development Services

Generative AI development services encompass the design, building, and deployment of AI systems that generate new content — text, code, images, documents, or data — using large language models and related technologies. This includes custom model fine-tuning, RAG system development, guardrail implementation, and integration with enterprise systems. The goal is production-ready AI that delivers consistent, accurate outputs within your existing workflows.

A proof of concept typically runs $5,000–$15,000 and takes 1–3 weeks. A single-use-case production system costs $15,000–$50,000 over 4–8 weeks. Enterprise platforms with multiple use cases, integrations, and compliance requirements range from $50,000–$250,000+. The biggest cost variables are data preparation complexity, number of system integrations, and regulatory requirements.

A working POC takes 1–3 weeks. A production MVP for a single use case takes 4–8 weeks. Full enterprise deployments with multiple use cases, integrations, and compliance typically take 3–8 months. We run two-week sprints with working demos at every checkpoint, so you see progress continuously rather than waiting months for a big reveal.

Yes — this is the core of what we build. Our RAG architectures connect to your databases, document stores, CRMs, ERPs, and internal knowledge bases. We support both cloud-based and on-premise data sources, handle structured and unstructured data, and build integration layers that keep your data in your infrastructure when required. Data preparation and integration typically account for 40–50% of project effort.

We use a layered approach: RAG architectures that ground outputs in retrieved source material, citation verification that cross-checks generated claims, confidence scoring that flags uncertain outputs, and automated evaluation pipelines that test for factual accuracy across thousands of edge cases. For high-stakes use cases, we implement mandatory human review for outputs below confidence thresholds. No system eliminates hallucination completely, but ours reduce it to rates that are operationally acceptable.

It can be — with the right architecture. We build systems with data residency controls, PII handling, consent management, and right-to-deletion support for GDPR. For the EU AI Act, we classify system risk levels, implement required transparency measures, maintain technical documentation, and build human oversight mechanisms. We also support on-premise deployment for organizations that cannot send data to third-party APIs. Compliance is an architecture decision, not a feature you add later.

Fine-tuning modifies the model itself by training it on your domain-specific data — the model learns your terminology, patterns, and domain logic permanently. RAG keeps the base model unchanged and instead retrieves relevant information from your data sources at query time, injecting it as context. Fine-tuning is better for teaching the model how to reason about your domain. RAG is better for grounding answers in current, specific data. Most production systems use both: a fine-tuned model for domain understanding, with RAG for access to current information.

Yes. All custom code, fine-tuned model weights, RAG pipelines, and system architecture we build are yours. You own the IP fully. For foundation models (GPT, Claude, Llama), licensing follows the original provider's terms — but any customization, fine-tuning, and system integration built on top is your intellectual property. We provide full source code, documentation, and knowledge transfer at project completion.