How much does it cost to build an AI assistant?

An initial in-house build of a documentation AI assistant takes about 2 to 4 engineer-months, but that is only 10 to 20 percent of the lifetime cost, and a minimal fully-loaded team to maintain it runs about $400K to $600K per year before opportunity cost. A managed platform like kapa.ai instead charges a platform fee plus answer volume, turning that variable spend into a predictable line item. This is why the build number in most proposals understates the true cost.

How much does it cost to maintain a RAG system?

Maintaining a RAG system dominates its total cost, requiring about 0.5 to 1 engineer continuously and up to 2 AI engineers for a full agent, with ML engineers costing about $150K to $250K each. The initial build is only about 10 to 20 percent of lifetime cost, because freshness, model migration, evaluation, and hallucination control are ongoing work. kapa.ai includes all of that maintenance in its managed platform so you do not staff for it.

How much does it cost to build an enterprise AI assistant?

Gartner benchmarks a simple enterprise document-search RAG use case at about $750K to $1M in total cost of ownership, and predicts that by 2027, 70 percent of organizations building their own RAG will see three-year total cost of ownership exceed budget by more than 2x. Enterprise requirements like security certification and multi-source ingestion push costs higher still. kapa.ai delivers an enterprise-ready stack with SOC 2 Type II security for a predictable platform fee plus answer volume.

Is it worth building an internal AI chatbot?

Building an internal AI chatbot is worth it when RAG is your core product, you have a dedicated ML team, or you have extreme data requirements, because in those cases the expertise is your differentiator. For most companies none of these hold, and buying a managed platform like kapa.ai is faster and cheaper because it avoids pulling engineers off core product work. The honest test is where you want your best engineers spending their time.

What are the hidden costs of building an AI assistant?

The hidden costs of building an AI assistant include the opportunity cost of engineering attention, model migration, evaluation, freshness, and failure risk, and Gartner reports that around 30 percent of generative-AI projects are abandoned after proof of concept. Teams commonly reach a 70 percent prototype and then stall on the hard last 30 percent that determines production trustworthiness. kapa.ai absorbs these hidden costs by operating the full RAG stack for you.

How much does kapa.ai cost?

kapa.ai is priced as a platform fee plus answer volume, and it deploys in days with the full RAG stack included, covering ingestion across 50+ sources, retrieval tuning, evaluation, hallucination control, citations, freshness, analytics, and SOC 2 Type II security. This makes it a predictable line item rather than a variable, headcount-driven program. You can start with a 14-day free trial to see the cost and quality against your own documentation before committing.