Taxbit is a digital-asset tax and accounting compliance platform. Founded in 2018, Taxbit helps enterprises such as exchanges, payment services, and brokers calculate gains and losses, collect tax documentation, and file information returns at scale. Now, a cited, permission-aware AI assistant helps compliance teams find answers right inside the product dashboard.

But the knowledge needed to use that dashboard well was scattered across the public website, API documentation, and help-center articles, and a meaningful share was never written down at all. When a customer asked “what does this form status mean?”, they either went digging or filed a support ticket. Today more than 55 percent of users who ask Assist a question return to ask another within 24 hours, according to Taxbit’s internal production analytics.

In this post, we share how we built Taxbit Assist, the AI assistant now embedded in the Taxbit dashboard, on Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale, with any framework or model. We cover the architecture, the retrieval work that made answers trustworthy enough to ship to regulated customers, how we handle safety and data residency, and how we continuously measure answer quality on live production traffic.

A generic chatbot was not an option in this domain. Four constraints shaped what we built:

We are also a small team. Rather than build and operate the agent loop, conversation-state store, and session isolation ourselves, we chose Amazon Bedrock AgentCore for three reasons: its managed agent runtime removed infrastructure operations, built-in AgentCore memory, a capability of Amazon Bedrock AgentCore, eliminated a separate conversation-state store, and per-session isolation kept each user’s context contained.

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