llms.txt
+118 −102
https://layerz.cc/llms.txt
− > Layerz is the structured financial modeling layer for AI agents and finance teams. It separates model **structure** (logic, the dependency graph of variables, timelines) from **data** (values, scenarios), so financial models can be versioned, audited, reused, and exported to clean Excel — without drift. Claude and other MCP clients drive Layerz from the outside, keeping full context end to end. Layerz is not an Excel generator; Excel is a compatibility output format.− Unlike spreadsheets, Layerz models never break when you change structure. Add a timeline, insert an item, modify a formula — the engine re-resolves all dependencies automatically. The structure is versioned and reusable; the data stays separate.− - Exposes 30 tools and 5 resources (FINANCE.md per model, custom instructions, modeling guide, formula and format specs): build, read, patch, compute, validate, diff, version, import, and export models programmatically.− - **SaaS Revenue Model** — MRR, churn rate, expansion revenue, unit economics, LTV/CAC− - **Startup Runway** — Burn rate, funding rounds, hiring plan, cash runway milestones− - **LBO Analysis** — Debt tranches, leverage ratios, interest schedules, equity returns, IRR− - **Manufacturing P&L** — Raw materials, labor costs, production capacity, gross margins, capex− - **Consulting Agency** — Utilization rates, day rates, headcount planning, project pipeline− - **Infrastructure Project** — Capex schedule, depreciation, operating cash flows, IRR/NPV− - **Fintech Lending** — Loan book growth, default rates, net interest margin, provisions− - **Real Estate Development** — Construction costs, rental income, vacancy rates, cap rates− - **E-commerce** — GMV, conversion rates, average order value, fulfillment costs, contribution margin− - **Deterministic & Auditable** — Every formula is transparent. Results are reproducible. No hidden cells.− - **Structured, Not Flat** — Typed items with explicit dependencies. No cell-reference guessing.− - **Persistent Context** — The model, its `FINANCE.md`, and its history persist across sessions. No re-explaining.− - **Compute on Demand** — One call evaluates the entire model in dependency order.− - **Open Excel Output** — Export audit-ready .xlsx with live formulas. No vendor lock-in. Export is never paywalled.− - **Version-Controlled Logic** — Model structure behaves like code. Version it, diff it, branch it.− - **Multi-Timeline** — Monthly, yearly, quarterly, constant periods with automatic cross-timeline projection.− - **MDK (Model Definition Kit)** — The pivot format: structured JSON describing a model's logic without data.− - **Model** — Named container with timelines and ordered items. Pre-loaded with default sections (P&L, Cash Flow, Balance Sheet).− - **Timelines** — Four grains: `constant` (single value), `monthly`, `yearly`, `quarterly`. Each has start/end dates.− - **Items** — Roles: `assumption` (input), `formula` (computed, or a no-expression aggregator that sums its children), `callup` (typed mirror of one source, e.g. a balance flow), `balance` (cumulative stock with opening balance + flows), `section` (structural grouping, nestable), `dashboard` (holds `chart` / `kpi` / `mini_table`). Note: the pre-MDK-0.2.0 `layer` role no longer exists.− - **Lists** — Parametric dimensions (e.g. departments, products, debt tranches) via `liste_ref`: one item expands to one instance per element, with cross-list bridging via `mapped_to`.− - **Compute** — Evaluates all items via topological sort; returns values indexed by UID.− - [AI Financial Model Risk and Governance: Statistics (2026)](https://layerz.cc/resources/ai-financial-model-risk-statistics) — Verified statistics on AI financial model risk and governance: accuracy benchmarks, shadow AI in finance, audit gaps, and EU AI Act rules.− - [AI in Finance: Adoption and Reality Statistics (2026)](https://layerz.cc/resources/ai-in-finance-statistics) — How many finance teams use AI, where agents are heading, and why most enterprise AI efforts still fall flat. The 2026 numbers, with primary sources.− - [E-Invoicing Mandate Statistics (2026)](https://layerz.cc/resources/e-invoicing-mandate-statistics) — Dated figures on Europe's e-invoicing mandates: France live 1 Sept 2026, platform registration counts, formats and lifecycle statuses. Sourced.− - [Financial Forecast Accuracy and Budget Cycle Statistics (2026)](https://layerz.cc/resources/forecast-accuracy-and-budget-cycle-statistics) — How accurate are corporate forecasts, and how long does the annual budget really take? The data, with primary sources, sample sizes, and years.− - [Fractional CFO Statistics: Demand, Pay, and the Engagement Model (2026)](https://layerz.cc/resources/fractional-cfo-statistics) — Fractional and part-time CFO statistics for 2026: demand, pay, clients per CFO, client size, hours, remote work and AI use, each figure with its primary source.− - [LLM Reliability: Hallucination, Consistency, and Reasoning Statistics (2026)](https://layerz.cc/resources/llm-reliability-statistics) — How often do LLMs hallucinate, how consistent are they run to run, and where does reasoning break down? Verified LLM reliability data, a source per figure.− - [SaaS Spending Benchmarks: What Private SaaS Companies Spend, by Department and Stage (2026)](https://layerz.cc/resources/saas-spending-benchmarks) — What private SaaS companies spend on sales, marketing, R&D and G&A, plus growth, retention and CAC payback by stage. Each figure with its denominator.− - [Spreadsheet Errors and Risks: Statistics and Notable Failures (2026)](https://layerz.cc/resources/spreadsheet-errors-and-risks) — How often do spreadsheets contain errors, how much does finance still rely on them, and what have spreadsheet mistakes cost? The data, with primary sources.− - [The Finance Engineer Role: Job Posting Statistics (2026)](https://layerz.cc/resources/finance-engineer-statistics) — We scanned 421 employer job boards and 27,278 open postings for finance engineering roles. 37 verified requisitions: titles, departments, tools, pay.− - [What a Finance Function Costs: Benchmark Statistics (2026)](https://layerz.cc/resources/finance-function-cost-statistics) — Finance cost as a share of revenue, finance FTEs per $1bn, and where finance time goes. Benchmark data with primary sources and sample sizes.− - [Where FP&A Time Actually Goes: Productivity Statistics (2026)](https://layerz.cc/resources/fpa-productivity-statistics) — How much of FP&A time reaches analysis, and what stops the rest getting there. Verified time-allocation data for financial planning, a source per figure.− - [Sanity-Check a SaaS Financial Model Against Benchmarks: Five Ratios and How to Read Them](https://layerz.cc/blog/sanity-check-saas-model-against-benchmarks) — Five ratios to compute from your own SaaS model, the benchmark ranges to hold them against, and the four mistakes that make the comparison meaningless.− - [Does the AI Do the Math? 10 Questions to Ask Before You Buy an AI Finance Tool](https://layerz.cc/blog/questions-to-ask-ai-finance-tool) — Every finance tool now has AI in it. Ten questions that separate the ones you can defend in front of an auditor, and what a good answer sounds like.− - [Semantic Layer vs Model Layer: What a Number Means vs What It Will Become](https://layerz.cc/blog/semantic-layer-vs-model-layer) — Finance AI has converged on the semantic layer: metric definitions, lineage, permissions. It answers what a number means, not what it will become.− - [Why Not Just Build It Yourself With AI?](https://layerz.cc/blog/build-it-yourself-with-ai-vs-model-layer) — Claude Code can build the finance tool you were about to buy. The honest version of when DIY wins, and the three questions that actually decide it.+ > Layerz is the structured financial modeling layer for AI agents and finance teams. It separates model **structure** (logic, the dependency graph of variables, timelines) from **data** (values, scenarios), so financial models can be versioned, audited, reused, and exported to clean Excel without drift. Claude and other MCP clients drive Layerz from the outside, keeping full context end to end. Layerz is not an Excel generator; Excel is a compatibility output format.+ Unlike spreadsheets, Layerz models never break when you change structure. Add a timeline, insert an item, modify a formula, and the engine re-resolves all dependencies automatically. The structure is versioned and reusable; the data stays separate.+ - Exposes 29 tools and 5 resources (FINANCE.md per model, custom instructions, modeling guide, formula and format specs): build, read, patch, compute, validate, diff, version, import, and export models programmatically.+ - **SaaS Revenue Model**: MRR, churn rate, expansion revenue, unit economics, LTV/CAC+ - **Startup Runway**: Burn rate, funding rounds, hiring plan, cash runway milestones+ - **LBO Analysis**: Debt tranches, leverage ratios, interest schedules, equity returns, IRR+ - **Manufacturing P&L**: Raw materials, labor costs, production capacity, gross margins, capex+ - **Consulting Agency**: Utilization rates, day rates, headcount planning, project pipeline+ - **Infrastructure Project**: Capex schedule, depreciation, operating cash flows, IRR/NPV+ - **Fintech Lending**: Loan book growth, default rates, net interest margin, provisions+ - **Real Estate Development**: Construction costs, rental income, vacancy rates, cap rates+ - **E-commerce**: GMV, conversion rates, average order value, fulfillment costs, contribution margin+ - **Deterministic & Auditable**: Every formula is transparent. Results are reproducible. No hidden cells.+ - **Structured, Not Flat**: Typed items with explicit dependencies. No cell-reference guessing.+ - **Persistent Context**: The model, its `FINANCE.md`, and its history persist across sessions. No re-explaining.+ - **Compute on Demand**: One call evaluates the entire model in dependency order.+ - **Open Excel Output**: Export audit-ready .xlsx with live formulas. No vendor lock-in. Export is never paywalled.+ - **Version-Controlled Logic**: Model structure behaves like code. Version it, diff it, branch it.+ - **Multi-Timeline**: Monthly, yearly, quarterly, constant periods with automatic cross-timeline projection.+ - **MDK (Model Definition Kit)**: The pivot format: structured JSON describing a model's logic without data.+ - **Model**: Named container with timelines and ordered items. Pre-loaded with default sections (P&L, Cash Flow, Balance Sheet).+ - **Timelines**: Four grains: `constant` (single value), `monthly`, `yearly`, `quarterly`. Each has start/end dates.+ - **Items**: Roles: `assumption` (input), `formula` (computed, or a no-expression aggregator that sums its children), `callup` (typed mirror of one source, e.g. a balance flow), `balance` (cumulative stock with opening balance + flows), `section` (structural grouping, nestable), `dashboard` (holds `chart` / `kpi` / `mini_table`). Note: the pre-MDK-0.2.0 `layer` role no longer exists.+ - **Lists**: Parametric dimensions (e.g. departments, products, debt tranches) via `liste_ref`: one item expands to one instance per element, with cross-list bridging via `mapped_to`.+ - **Compute**: Evaluates all items via topological sort; returns values indexed by UID.+ - [SaaS Spending Benchmarks: What Private SaaS Companies Spend, by Department and Stage (2026)](https://layerz.cc/resources/saas-spending-benchmarks): What private SaaS companies spend on sales, marketing, R&D and G&A, plus growth, retention and CAC payback by stage. Each figure with its denominator.+ - [AI Financial Model Risk and Governance: Statistics (2026)](https://layerz.cc/resources/ai-financial-model-risk-statistics): Verified statistics on AI financial model risk and governance: accuracy benchmarks, shadow AI in finance, audit gaps, and EU AI Act rules.+ - [AI in Finance: Adoption and Reality Statistics (2026)](https://layerz.cc/resources/ai-in-finance-statistics): How many finance teams use AI, where agents are heading, and why most enterprise AI efforts still fall flat. The 2026 numbers, with primary sources.+ - [E-Invoicing Mandate Statistics (2026)](https://layerz.cc/resources/e-invoicing-mandate-statistics): Dated figures on Europe's e-invoicing mandates: France live 1 Sept 2026, platform registration counts, formats and lifecycle statuses. Sourced.+ - [Financial Forecast Accuracy and Budget Cycle Statistics (2026)](https://layerz.cc/resources/forecast-accuracy-and-budget-cycle-statistics): How accurate are corporate forecasts, and how long does the annual budget really take? The data, with primary sources, sample sizes, and years.+ - [Fractional CFO Statistics: Demand, Pay, and the Engagement Model (2026)](https://layerz.cc/resources/fractional-cfo-statistics): Fractional and part-time CFO statistics for 2026: demand, pay, clients per CFO, client size, hours, remote work and AI use, each figure with its primary source.+ - [LLM Reliability: Hallucination, Consistency, and Reasoning Statistics (2026)](https://layerz.cc/resources/llm-reliability-statistics): How often do LLMs hallucinate, how consistent are they run to run, and where does reasoning break down? Verified LLM reliability data, a source per figure.+ - [Spreadsheet Errors and Risks: Statistics and Notable Failures (2026)](https://layerz.cc/resources/spreadsheet-errors-and-risks): How often do spreadsheets contain errors, how much does finance still rely on them, and what have spreadsheet mistakes cost? The data, with primary sources.+ - [The Finance Engineer Role: Job Posting Statistics (2026)](https://layerz.cc/resources/finance-engineer-statistics): We scanned 421 employer job boards and 27,278 open postings for finance engineering roles. 37 verified requisitions: titles, departments, tools, pay.+ - [What a Finance Function Costs: Benchmark Statistics (2026)](https://layerz.cc/resources/finance-function-cost-statistics): Finance cost as a share of revenue, finance FTEs per $1bn, and where finance time goes. Benchmark data with primary sources and sample sizes.+ - [Where FP&A Time Actually Goes: Productivity Statistics (2026)](https://layerz.cc/resources/fpa-productivity-statistics): How much of FP&A time reaches analysis, and what stops the rest getting there. Verified time-allocation data for financial planning, a source per figure.+ - [The CFO Harness: What Sits Between an AI Model and a Number You Sign](https://layerz.cc/blog/cfo-harness): A CFO harness is the layer around an AI model that makes its finance work signable: structured model, conventions, permissions, history and review.+ - [Sanity-Check a SaaS Financial Model Against Benchmarks: Five Ratios and How to Read Them](https://layerz.cc/blog/sanity-check-saas-model-against-benchmarks): Five ratios to compute from your own SaaS model, the benchmark ranges to hold them against, and the four mistakes that make the comparison meaningless.+ - [Does the AI Do the Math? 10 Questions to Ask Before You Buy an AI Finance Tool](https://layerz.cc/blog/questions-to-ask-ai-finance-tool): Every finance tool now has AI in it. Ten questions that separate the ones you can defend in front of an auditor, and what a good answer sounds like.+ - [Semantic Layer vs Model Layer: What a Number Means vs What It Will Become](https://layerz.cc/blog/semantic-layer-vs-model-layer): Finance AI has converged on the semantic layer: metric definitions, lineage, permissions. It answers what a number means, not what it will become.
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