Startups are increasingly using AI to handle legal and accounting tasks themselves, avoiding high professional fees. This signals a significant market need for tools that formalize and support this DIY approach, especially as startups scale and require more robust solutions for investors.

By implementing an AI-powered auditor for customs entries, logistics company Flexport reduced its internal error rate from 1.8% to 0.2%—a tenfold improvement. This served as a major wake-up call, demonstrating AI's potential to dramatically enhance accuracy and efficiency in core operational tasks that involve unstructured data and reasoning.

Journalist Casey Newton uses AI tools not to write his columns, but to fact-check them after they're written. He finds that feeding his completed text into an LLM is a surprisingly effective way to catch factual errors, a significant improvement in model capability over the past year.

Instead of rigid if-then rules, companies can use natural language for expense policies (e.g., "business class for flights over 5 hours"). AI agents interpret and apply these nuanced rules to over 100,000 daily expenses with 99% accuracy, freeing up managers' time.

To solve for AI hallucinations in high-stakes decisions, advanced platforms use the LLM as an interpreter that writes code to query raw data. If data is unavailable, it returns an error instead of fabricating an answer, making every analysis fully auditable and grounded in verifiable data.

After an initial analysis, use a "stress-testing" prompt that forces the LLM to verify its own findings, check for contradictions, and correct its mistakes. This verification step is crucial for building confidence in the AI's output and creating bulletproof insights.

Building reliable AI agents for finance, where accuracy is critical, requires moving beyond pure LLMs. Xero uses a hybrid system combining LLM-driven workflows with programmatic code and deep domain knowledge to ensure control and reliability that LLMs inherently lack.

Before planning the future, analyze the past. A Profit & Loss (P&L) statement reveals what truly drove revenue and where money was spent. For a deeper, non-obvious analysis, input your P&L into ChatGPT and ask it to act as a financial analyst, identifying trends, overspending, and hidden opportunities.

Before engaging expensive experts like lawyers or accountants, use AI to do preliminary work. You can draft initial documents, analyze data, or formulate questions. This prepares you for a more productive conversation, saving time and money while ensuring you still rely on the human expert for final verification and strategy.