Topolog turns any goal into a dependency graph and schedules your days around it. You get a structured plan, a completion spectrum, and a task list that adapts as you mark them done. Every plan is a real program, so the dates and odds are computed, not guessed.
A startup from the United Kingdom that is founded by Rohith B.V..
Probabilistic Forecasting
Monte Carlo simulation returns P50/P95 completion dates and a full date distribution, not a single deadline.
AI Plan Authoring
Describe a goal in plain English and get a complete, structured plan drafted automatically.
Visual Plan Canvas
Interactive node-graph editor with automatic layout for tasks, milestones, and dependencies.
Risk & Critical-Path Analysis
See which tasks drive your timeline and where schedule risk concentrates.
Uncertainty Modeling
Capture estimate ranges, probabilistic outcomes, and conditional gates on every task.
Iterations & Loops
Model repeated work: fixed counts or "repeat until success", with true probabilistic loop lengths.
Budget & Money Modeling
Tie spend to probability of success and track burn and runway alongside the schedule.
Capacity Scheduling
Allocates work across people/agents by available capacity to produce realistic dates.
Execution Tracking
Pick up and complete tasks; forecasts re-calibrate from real progress.
Plan Validation Engine
Built-in correctness checks catch structural errors before a plan goes live.
Credit-Based Pricing
Simple pay-per-plan credits: 100 credits per build.
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Built by a solo founder with 14 years across Meta, Media.net, Amazon and others. After watching countless projects miss deadlines, not from incompetence but from tools that gave one fake date, I set out to build a planning engine that takes uncertainty seriously. The result is Topolog: a formally total scheduling language, a deterministic Monte Carlo engine, and a Bayesian self-tuning scheduler. Built entirely solo with Claude Code and Devin as AI engineering partners. Zero VC, zero team, 100% ownership.
Anyone running a goal with real dependencies and real stakes: technical project managers, engineering managers, founders, and ambitious individuals planning complex personal projects like home renovations, album productions, or marathon training. The unifying characteristic is feeling the pain of planning tools that lie about deadlines. Topolog is for people who want to know their actual odds, not a false sense of certainty.
Every other planning tool gives you one deadline, the one you'll miss. Topolog gives you the full picture: a dependency graph that knows what blocks what, a Monte Carlo completion spectrum showing your real odds, a critical path that updates as you execute, and a budget tracker tied directly to your probability of success. MS Project has critical path but no probabilistic engine. Monday and Asana have boards but no complete dependency model. AI tools hallucinate dates. Topolog computes them.
Topolog treats every plan as a program. Plans are written in TOL (Total Orchestration Language), a formally total, decidable language where the scheduler and Monte Carlo engine compute dates and probabilities deterministically. The AI drafts structure but never touches the maths. You get a completion spectrum (a probability distribution over outcomes), honest deadline ranges (a floor and a ceiling, never one date you'll miss), and a Bayesian self-tuning scheduler that learns your real pace from timestamps alone. The planning language is public, you can author plans with any AI and run them through Topolog's engine.
Topolog is a TypeScript-first web app built around a custom stochastic-planning engine:
Frontend: Next.js 15 (App Router) with React 18 and TypeScript, styled with Tailwind CSS. The interactive plan canvas uses dagre / ELK (elkjs) for graph layout.
Core engine: an in-house DSL ("TOL") plus a Monte Carlo stochastic-forecasting engine, written in pure isomorphic TypeScript so it runs identically on the server and in the browser.
Backend & data: Supabase (PostgreSQL, auth, and SSR), with the API layer on Next.js route handlers. Stripe handles billing.
AI authoring: a model-router layer that calls GPT (OpenAI), and Mistral for plan authoring and review.
Infra & quality: deployed on Vercel (Analytics + Speed Insights), error monitoring via Sentry, and tested with Jest + Playwright.
I don't have verified information about topolog.co.uk in my training data, so I can't confirm what the service does or vouch for its quality, reliability, or reputation. It may be a small, niche, or newer website that isn't well-documented in publicly available sources I was trained on.
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Check the traffic stats of Topolog on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Topolog on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Topolog's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Topolog on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
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Topolog turns any goal into a dependency graph and schedules your days around it. You get a structured plan, a completion spectrum, and a task list that adapts as you mark them done. Every plan is a real program, so the dates and odds are computed, not guessed.