
AI-powered Intercompany Elimination
Close faster. Sign off smarter.
What it does
Financial close teams spend 3–5 days on intercompany matching, eliminations, and approvals. Close Command changes that. AI computes matching and elimination entries across 28 rule types — then stops. Every journal entry requires a named human reviewer before posting. Timestamped. Audit-ready. No exceptions. 60–70% less manual reconciliation. Up to 2 days recovered per close cycle. → 28 elimination rule types → Human-in-the-loop hard gate → Audit trail + Excel export → Multi-currency support
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- IBI built AI that turns 4 hours of financial analysis into 30 seconds2025 · duebase.com · ▲15
I built Duebase AI to solve a problem I kept running into in fintech - analyzing UK company financial health takes forever. The process usually goes: download PDFs from Companies House → manually extract data to spreadsheets → calculate ratios → interpret trends. Takes 3-4 hours per company and requires serious financial expertise. The technical challenge: Companies House filings are messy. Inconsistent formats, complex accounting structures, missing data, and you need to understand UK accounting standards to make sense of it all. My approach: Parse 15M+ UK company records from Companies…




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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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