{"id":46875998,"title":"Show HN: Askfeather.ai – Professional Class AI Tax Assistant","points":7,"user":"sokratisv","time":1770146921,"time_ago":"7 hours ago","type":"link","content":"

Hi HN,

We’re the team at Feather Labs, and we built Feather (https://askfeather.ai), an AI tax assistant designed to assist how professionals handle modern Tax research.

General LLMs are a liability for tax work because they lack a hierarchical understanding of the law. They often conflate IRC Title 26 with non-authoritative blog posts or outdated Treasury Regulations. We built Feather to move past "plausible" prose toward audit-defensible reasoning.

The Technical Challenge: Standard RAG often chokes on the tax code for a few specific reasons:

1. Hierarchical Fragmentation: Simple character-count chunking breaks the logical nesting of tax law; we implemented a strategy to preserve the relationship between code sections, sub-clauses, and court cases.

2. Temporal Decay: A vector search might pull a 2021 Revenue Ruling that was superseded in 2024; our indexing prioritizes versioning and the latest IRS guidance.

3. Contextual Overlap: The tax code is highly repetitive. We use multi-jurisdictional analysis to distinguish between federal and state-level nuances that appear semantically similar but are legally distinct.

The Build:

1. Audit-Ready Citations: Every answer is grounded in primary sources—IRC Title 26, Treasury Regs, and IRS guidance, providing verified references you can actually defend.

2. Context-Aware Intelligence: Beyond answering queries, the system flags related risks, exceptions, and filing deadlines that practitioners might overlook.

3. Compliance: We are SOC 2 compliant with a strict zero-training policy on sensitive client data.

We’re curious to hear from anyone else building RAG for "dense" domains where "close enough" results are not enough. We'll be in the comments to talk about our indexing strategy and how we handle complex document extraction.

Sokratis","url":"https://askfeather.ai","domain":"askfeather.ai","comments":[],"comments_count":0}