AI Development Services

AI development services cover building custom and generative AI for your business—including RAG chatbots and assistants, content generation tools, document and workflow automation, AI integration into existing software, and strategy or consulting on where AI returns real value.

RAG lets an AI answer using your specific documents and data by retrieving relevant content and feeding it to the model at query time, instead of relying only on the model's training. It's the most common, cost-effective way to build accurate, domain-specific assistants.

A focused AI feature or RAG assistant runs $15,000–$40,000, a custom AI application $25,000–$80,000, a multi-feature platform $80,000–$250,000, and an enterprise program $200,000–$400,000+. Most of the cost is data, integration, and change management rather than the model.

By the 10-20-70 rule, roughly 10% of effort is model work, 20% is infrastructure, and 70% is data pipelines, integrations, testing, and change management. The model is largely a commodity; the value and cost are in connecting it reliably to your data and systems.

A focused AI feature or RAG assistant ships in 2–6 weeks, a custom AI application in 4–10 weeks, a multi-feature platform in 10–20 weeks, and an enterprise program in 4–12 months. Integration complexity and approval cycles drive most of the timeline, not model work.

Expect LLM API fees of about $300–$5,000 per month per roughly 10,000 queries, hosting of $50–$500, vector storage of $0–$1,000, and observability tooling. Operating costs can grow significantly with usage, so model routing, caching, and token budgets matter from the start.

We ground the assistant on your real data with RAG, constrain it to retrieved content, add guardrails, and evaluate output quality on real inputs before launch. Grounding plus evaluation is what turns a generic chatbot into a reliable, domain-specific assistant.

Yes. We embed AI into your products, CRMs, internal tools, and workflows through clean APIs so it works inside the systems your team already uses. The integration and data-readiness work is usually the bulk of the project.

Generative AI creates new content—text, summaries, drafts, code—using large language models, while AI automation applies models to streamline repetitive tasks like classification, routing, and document processing. Many projects combine both, generating content and automating the workflow around it.

We match model hosting, data retention, and access controls to your sensitivity and compliance needs, and can use private or open-weight models when sending data to a public API isn't appropriate. Privacy and compliance posture are decided early, before any data flows.

We run an opportunity and data-readiness assessment, identify the few use cases with clear ROI, and define success metrics before building. If a simpler tool solves the problem, we'll say so—AI should be chosen because it returns value, not because it's trendy.

Yes. When you need software that reasons and acts across tools—processing tasks and updating systems rather than only answering questions—that's AI agent development, a focused discipline we cover on its own with evaluation harnesses and action guardrails.