FinSight.
An AI system that turns a raw P&L into executive-ready financial analysis in 90 seconds. Deterministic cleaning and KPI math; LLM-driven narrative and Q&A. Architecture designed end-to-end, demo built on real sample data.
The problem.
The first three hours of every FP&A cycle are the same: pull the export, fix the column headers, normalize the date columns, recompute the variance columns the export software didn't get right, build the chart deck, write the executive narrative. None of this is the analyst's actual job. The actual job is the interpretation that comes after.
FinSight is an attempt to compress the first three hours into ninety seconds, freeing the analyst to spend their time on the part that requires real judgment. Cleaning and KPI computation are coded, deterministic, and provably correct. The LLM only does what it is good at: writing the narrative, hypothesizing variance drivers, and answering ad-hoc questions about the data.
The architecture is fully designed. The demo below is interactive and built on real sample data so it can be evaluated end-to-end without burning live API budget. Live deployment with rate limiting and per-user cost controls is the next phase.
The architecture.
Six stages, with a hard line between deterministic work (cleaning, parsing, KPI math) and AI work (narrative, hypotheses, Q&A). The split matters: a finance demo where the LLM hallucinates a number is dead on arrival.
The demo.
Two real sample P&Ls, the full pipeline running in your browser. Pick a company, click run, and the system processes the upload, computes KPIs, and renders the analyst report.
What's hard.
The deterministic / AI line
The single most important architectural decision is what the LLM does and what it does not. If the LLM computes the gross margin and gets it wrong by 0.3 percentage points, the demo is over and the trust is gone. KPIs and variance math are coded; the LLM only narrates and hypothesizes. This split is enforced by the API contract between layers, not a guideline.
Variance hypothesis honesty
An LLM will happily invent a confident reason for any variance you put in front of it. The fix is a system prompt that constrains hypotheses to data-supported claims and explicitly frames them as suggestions to verify. The variance section is also visually marked as "AI HYPOTHESES" in the report, distinct from the deterministic sections.
Schema variance across exports
NetSuite, QuickBooks, Sage, and Excel-built P&Ls all use different account hierarchies and column conventions. The parser cannot LLM its way out of this, it has to be a real classifier, with a fallback to ask the user when it cannot map a column with high confidence. For the demo, parsing is shortcut to two known schemas.
Cost control if deployed live
An open Q&A endpoint with no rate limiting is a credit-card fire. The deployed version needs per-IP rate limits, per-question token caps, and an API budget alert. For the demo, hardcoded answers serve the same recruiter-facing purpose at zero ongoing cost.
The stack.
What's next.
The architecture is fully designed. The interactive demo proves the UX end-to-end on real sample data. The piece that has not been built is the live deployment, connecting the Q&A endpoint to a real Claude API key, adding rate limiting and per-user cost controls, and handling actual user uploads.
That phase is mostly engineering work, not architectural work. It is also a non-trivial commitment in API spend at scale, which is why the architecture-first version exists. If FinSight ever becomes a paid product targeting mid-market FP&A teams, the live deployment is the natural first step.