10x Efficiency Doesn't Come From Improvement – It Comes From Cutting

01 /The Concept

AI coding platforms are everywhere now. What makes Lancey different is that its AI doesn't just write code – it decides what code to write.

The underlying idea: connect user feedback signals directly to the product's codebase, removing the human bottleneck in the middle.

Setup involves integrating Lancey with two things: the channels where user feedback arrives (Slack, Intercom, Zendesk, and similar), and the code repository (GitHub or equivalent).

Once connected, the AI engine scans user messages for bug reports, feature requests, and general feedback – then draws conclusions about what in the codebase needs to be fixed or extended.

Each insight becomes a discrete task, visible to the whole team on a shared board. One click on any task instructs the AI to write and test the corresponding code, which is then ready to merge into the repository.

The AI tests its own code in isolated sandboxes on a test server, so nothing it does touches the production build.

For teams that want to go further, there's autopilot mode: the AI continuously pulls tasks from the queue and works through them in the background. The developer's job then narrows to reviewing which completed code changes to merge.

The net result: request intake and prioritization speeds up by 40%, and the volume of fixes and additions the same engineering team can ship increases by an order of magnitude.

Lancey went through Y Combinator in 2022, then raised CA$2M (~$1.5M USD). The product has gone through several pivots since. A [previous review](/review/na-kazhdom-zarabotat) covered an earlier version – an AI-powered micro-experimentation platform that segmented users to identify what to test. The current version's launch was posted to the YC website yesterday.

02 /Why It Matters

The Lancey founders say they built the platform because they believe code should respond directly to user feedback – without any human bottleneck in the loop.

Rocketable ([related review](/review/v-obshhem-sluchae-jeto-poka-fantastika-a-v-chastnom-vozmozhnost-na-milliard)) took the same core conviction and turned it into something entirely different. The startup graduated from Y Combinator earlier this year and raised $6.5M – not to build a developer tool, but to create a software holding company.

In Rocketable's model, an AI engine handles user feedback and product updates autonomously – fully on autopilot, with no engineers in the loop. But the software products themselves are chosen by humans: Rocketable acquires existing software businesses with at least two years of operating history, $500K–$2M in annual revenue, stable or growing top-line numbers, and profitability or near-profitability.

Lancey and Rocketable start from the same technical concept and end up in completely different places. Lancey built an efficiency tool for engineering teams. Rocketable is building a new kind of company. Same underlying idea; different ambitions, different models.

This pattern repeats across the startup ecosystem: most technologies can either become a tool or become a business model. The choice is entirely a function of how the founder thinks and how large their ambitions run.

Crosby ([related review](/review/bolee-prostaja-model-dlja-sozdanija-perspektivnogo-ii-produkta)) illustrates the same fork. Its AI engine verifies legal documents. The founders could have built a platform and sold it to corporate legal teams as a productivity tool. Instead they built an "AI law firm" – clients send documents, the AI analyzes them, and Crosby's human lawyers do a final verification pass before delivery. Same technology; a different business model entirely.

Crosby was founded this year and has already raised two rounds totaling $25.8M. Impressive – though no guarantee of what comes next.

It's also impossible to know whether Crosby would have raised more or less if it had gone the platform route. Harvey, which built an AI platform for corporate lawyers, raised $150M in October at an $8B valuation. But then – who knows what valuation Harvey could have reached if it had built an AI law firm using its advanced technology.

"Tool or business model" is always an interesting question – one that almost any founder can find themselves facing, if they recognize that their technology can be applied in more than one way.

03 /Opportunities

The first takeaway from this review: if you're building a startup, think carefully about whether the technology you're developing would be more powerful as a tool sold to others, or as the backbone of a new business model you operate yourself.

The second insight lives in Lancey's core idea: connecting feedback directly to response, cutting out the human bottleneck. In Lancey's case, that means pushing feedback all the way to code changes. But the same logic can apply in other domains. Where else could you close the loop between a signal and a reaction without requiring a human in the middle?