Interactive unit economics calculators, verified marketing benchmarks, and deep search models designed for growth teams, founders, and performance marketers.
Client-side instant calculations backed by transparent mathematical formulas. No email required.
Interactive Diagnostic Engine: scores 107 tactical growth tasks and builds a personalized 30-day P0/P1/P2 Roadmap with 1-click AI prompts and strategic frameworks.
Priority Score = (Effectiveness × 3) / (Complexity + Cost + Time) × Multipliers
Open, preview, and edit Markdown (.md) files with real-time GFM preview, KaTeX math formulas, Mermaid diagrams, and instant export to Word DOCX, HTML, or PDF.
100% Client-Side GFM + CommonMark Parser
Convert Markdown (.md) files into publication-ready PDF documents with 2x Retina resolution, LaTeX math, code highlighting, and custom page breaks.
100% Client-Side 2x Retina In-RAM Engine
Convert PDF documents to clean Markdown (.md) online in your browser. Extracts text, tables, headings, and lists with 100% client-side privacy.
100% Client-Side PDF.js In-RAM Parser
Convert Markdown (.md) to Microsoft Word (.docx) documents online with native styling, One Dark code blocks, LaTeX math formulas, tables, and zero server uploads.
100% Client-Side docx.js In-RAM Engine
Convert Microsoft Word (.docx) documents to clean Markdown (.md) online in your browser. Extracts headings, tables, lists, and code with 100% client-side privacy.
100% Client-Side Mammoth + Turndown Engine
Convert Markdown (.md) to clean, styled HTML5 code and fragments online. Features real-time HTML source inspection, KaTeX math formulas, One Dark syntax highlighting, and 100% client-side in-RAM privacy.
100% Client-Side Marked + DOMPurify In-RAM Engine
Convert HTML code, web pages, and CMS rich text into clean GitHub Flavored Markdown (.md) online. Features live Markdown source and visual preview, GFM tables, KaTeX equations, and 100% client-side privacy.
100% Client-Side Turndown + GFM In-RAM Engine
Create, edit, and format GitHub Flavored Markdown (GFM) tables online with an interactive spreadsheet grid, smart paste from Excel/Sheets, KaTeX math support, and AI token minifier.
100% Client-Side In-RAM Spreadsheet Grid Engine
Universal LLM token counter and text-to-tokens estimator. Calculate BPE tokens, word density, and separate input/output inference costs across models with 100% in-browser privacy.
Tokens ≈ Chars / 3.95 (General) | Cost = (Tokens / 1M) × Rate
Calculate exact Target ROAS, break-even thresholds, and required profit margins across Google & Meta campaigns.
ROAS = Gross Revenue / Ad Spend
Convert seamlessly between gross profit margin percentage and markup on cost. Avoid dangerous pricing errors.
Margin % = (Price - Cost) / Price
Itemize cost of goods, warehouse rent, software tools, taxes, and ad spend to model net operating profit (EBITDA).
Operating Profit = (Rev - COGS) - Fixed OPEX
Model customer acquisition backward from LTV to calculate maximum allowable CAC, CPL, and max PPC click bids.
Max CPC = (Net LTV / 3) × (CR₁ × CR₂)
Evaluate margin-adjusted Customer Lifetime Value, benchmark your LTV:CAC ratio health, and track CAC recovery speed.
Payback Mo = CAC / (ARPU × Gross Margin %)
Model token consumption COGS, Inference Efficiency Ratio (IER), true gross margins, Net LTV:CAC, and safe hybrid token quotas.
IER = Revenue / Direct Inference COGS (GPUs + Token APIs)
Model customer acquisition cohorts from inbound traffic down to net contribution profit using ARPU, ARPPU, and ROMI.
Profit = UA × (ARPU - CPA_visitor)
Cross-calculate acquisition costs, expected CTR, and conversion rates to stress-test your paid media budget.
CPA = CPC / Conversion Rate
Estimate the number of referring domains required to rank in the Top 10 based on keyword difficulty and target DR.
Link Gap = Competitor Med. Ref. Domains - Current
Forecast monthly Google Search ad spend from keyword demand and compute margin-based break-even CPC bids.
Max CPC = (AOV × Margin %) × CR %
Simulate 25 real-time scenarios analyzing Net Profit and ROAS across fluctuating CPC bids and conversion rates.
Net Margin = (Clicks × CR × AOV × Margin %) - Budget
Calculate statistical significance, two-tailed Z-scores, p-values, confidence intervals, and required sample size per variant.
Z = (p_B - p_A) / SE_pool
Forecast broadcast campaign revenue, benchmark Revenue Per Subscriber (RPS), calculate ESP ROI, and model list decay.
ROI = ((Email Revenue - ESP Cost) / ESP Cost) × 100
Model compounding competitor link growth, portfolio decay (link rot), required monthly velocity, and capital budgets to rank in Top 10.
V_mo = [RD_target(1+g)^T - RD_curr(1-δ)^T] · δ / [1 - (1-δ)^T]
Simulate passage extraction probability, exponential positional decay (t½ ≈ 350 tokens), and multi-engine viability across Google AI Overviews, Perplexity, and ChatGPT Search.
S_cit = 0.15·S_len + 0.25·S_pos + 0.20·S_fd + 0.15·S_edr + 0.10·S_dr + 0.15·S_fmt
Model total annual software expenditure across Ahrefs, SE Ranking, and Semrush. Quantify seat fees, credit overages, net migration savings, and reallocated link budgets.
ΔTCO = [S₁ + U₁·C_seat + O_credits] - [S₂ + U₂·C_seat + A_pack]
Deep studies synthesized from 50–130+ primary sources via Google Gemini Deep Research.
Head-to-head empirical benchmark across 54 primary sources and live stress-tests. Quantifies Ahrefs' 12.9x link graph advantage (35T vs 2.7T) against SE Ranking's 82% core feature parity at 41% total cost of ownership, daily rank tracking, and workspace credit mechanics.
Cross-engine meta-analysis synthesized from 72 primary sources, Google patents, and Princeton GEO benchmarks. Deconstructs the 5-stage RAG retrieval pipeline, exponential positional decay (t½ ≈ 350 tokens), Information Gain entropy, and empirical passage-level extraction multipliers.
Audit analysis of 138 SEC Form 10-K filings, NRF benchmarks, and GAAP datasets proving why naive break-even ROAS (1/Margin) leads to insolvency. Quantifies return friction, 3PL logistics, gateway drag, CM1/CM2/CM3 waterfalls, and the 135-day Cash Conversion Cycle.
Meta-analysis of 50,000 competitive SERPs proving why unique referring domains remain the primary search gatekeeper. Quantifies empirical domain deficits, annual link rot (t½ ≈ 3.47 yrs), and algorithmic velocity safety corridors across industries.
Continuous AI inference compute and token APIs compress software gross margins down to 50%–60%. Under these compressed margins, a nominal 3.0x Gross LTV delivers an effective Net LTV:CAC of only 1.65x—driving startups into structural cash flow insolvency.
