AI Project Estimator
See how much faster you could ship with AI-native engineering. Answer four questions — get an instant comparison.
What are you building?
How to estimate an AI project
What actually drives the timeline of an AI-augmented build
Estimating software has always been hard. AI-augmented engineering changes the inputs, not the difficulty: code generation speed stops being the bottleneck, and three other factors become the real determinants of timeline. If you only remember one thing from this guide, it's this: in AI-augmented engineering, the slow parts are decisions and verification, not implementation.
The estimator above gives you a directional number based on four inputs. This guide explains what actually moves that number in practice, so you can sanity-check the output before you take it to a budget conversation.
1. Scope clarity matters more than scope size
Conventional estimating assumes effort is roughly proportional to feature count. AI-augmented estimating assumes effort is roughly proportional to decision count: how many distinct design choices the team has to make, validate, and align on.
A well-scoped 40-feature greenfield product with one stakeholder ships faster than a 6-feature integration with four stakeholders, three legacy systems, and an ambiguous data model.
Before estimating, write down the decisions you've already made. Stack, data model, auth, deployment target, who approves UI. Each unresolved decision is roughly half a sprint of real elapsed time, regardless of who's building.
2. Integration depth, not feature count
Pure greenfield work — new database, new UI, no external systems — is where AI-augmented engineering shows its biggest multipliers (commonly 5–10x faster than traditional). The team isn't fighting anyone else's code.
Integration work is harder. Every external system (CRM, ERP, payment processor, legacy database, custom auth provider) adds a discovery phase that AI can accelerate but not eliminate. We model integration-heavy work at a 2–4x multiplier, not 10x.
If the estimator above came back optimistic for your project, count integration points first. Three or more external systems and you should expect to land at the lower end of the AI-augmented range.
3. Verification quality is the new bottleneck
When the cost of writing code drops, the cost of verifyingit becomes the dominant constraint. Teams that pay this tax — automated tests, evaluation suites for AI components, real monitoring, manual smoke-testing of UI — keep their velocity high indefinitely. Teams that don't pay it spend the time saved on production incidents instead.
Our estimator assumes you're paying the verification tax. If you're not — if "done" means "the code compiles" — the numbers won't hold up past the first deployment.
4. Team shape: senior, small, AI-fluent
AI doesn't turn junior engineers into senior ones. It amplifies whoever's using it. Three senior engineers working AI-augmented will outship ten mid-level engineers using AI as a chatbot — not because of mystique, but because seniors know what to ask for, what good output looks like, and when to stop.
The estimator assumes the team using it understands the domain. If you're estimating a project where the team will be learning the domain at the same time as building, add roughly 30% elapsed time to the result.
5. What the estimator deliberately doesn't model
The estimator gives you a directional number, not a quote. It deliberately ignores cost, team composition, organisational friction (procurement, security review, internal approvals), and any regulatory or compliance overhead. Those factors are real and often dominate calendar time — especially in enterprise environments — but they don't belong in a tool that's trying to answer one question: "how much faster is this with AI?"
When you take the estimate to a budget conversation, expect the conversation to add 20–40% for the above factors in a typical mid-sized organisation, and 60–100% in heavily-regulated enterprise.
From estimate to engagement
If the estimate above is in the ballpark and you want to pressure-test it against your actual requirements, the fastest next step is our 1–2 week "show, don't tell" sprint: a fixed-price, tightly-scoped delivery of one high-value piece of work from your backlog. You get a shipped outcome at the end whether or not we continue together — and a much more reliable basis for sizing the full engagement.
FAQ
Common questions about the estimator
Is the 'AI-augmented' timeline realistic, or marketing?
It's our measured range, not best-case. We track shipped work in repository history — commits, PRs merged, features deployed — and the 3–10x range comes from comparing those numbers against our pre-AI baseline and the quotes clients show us from conventional firms. The variance is mostly project type: greenfield apps land closer to 10x, integrations into messy legacy codebases closer to 3x.
What's the catch — does quality suffer for the speed?
The opposite, in our experience. Because AI shortens the cost of writing tests, the test coverage on AI-augmented work tends to be higher, not lower. The thing that does suffer is engineers who refuse to verify model output — those teams ship faster bugs. Quality comes from process (verification, evals, code review) not from typing speed.
Why doesn't the estimator ask for a budget?
Because budget is a function of scope and timeline, not the other way around. Once you have a scope, an honest engineering team can price it. The estimator is meant to show what's possible — turning that into a fixed quote is a 30-minute conversation, not a form field.
Does this work for our specific stack?
Frontier AI is most effective on stacks with good documentation and large training-data footprint — TypeScript, Python, modern web frameworks, common databases. It's still useful on niche or proprietary stacks, just with smaller multipliers. If you're working with something obscure (FileMaker, legacy Cobol, embedded firmware), tell us and we'll give a grounded estimate rather than the default.