"They Don't Know What They Want" Is Costing You Real Money

01 /The Concept

The biggest problem with B2B products isn't selling them – it's implementing them. When implementation happens before the sale, as in a pilot, the deal's outcome depends entirely on that implementation going well. When it comes after the sale, the results determine whether the client renews. Either way, implementation is the hinge everything else turns on.

Despite that criticality, 70% of implementations run late, blow their budgets, or fail outright. The root cause is almost always the same: the initial requirements are vague, incomplete, contradictory, and scattered across a dozen different documents. Wrangling them into something actionable requires enormous manual effort – gathering materials, chasing clarifications, backtracking, reworking.

Auctor set out to fix the requirements problem. Instead of implementations taking months, it wants them to take weeks – or even hours.

To get there, Auctor's AI sits inside every stage of client communication: online meetings, email threads, messaging apps, and other channels. It doesn't just take notes – it analyzes what's been said, surfaces gaps, and proactively suggests questions to ask so that the requirements picture becomes as complete and detailed as possible.

To stay in the loop across tools, Auctor ships ready-made integrations with the most popular enterprise systems used for communication and project management.

From all that gathered intelligence, the AI generates project documentation in whatever form the implementation team needs: formalized business or technical requirement lists, user stories, Agile epics assembled from those stories, Gantt charts showing task dependencies – whatever format the team works in.

Once the project is underway, Auctor pulls completed-task data from project management systems and cross-references it against the requirements it captured. If a delivered task is incomplete or contradicts a requirement, the AI flags it immediately – alerting the specific implementer, their manager, and the project owner – so they can either update the requirements with the client or fix the implementation before the gap compounds.

Auctor is currently in the spring Y Combinator cohort, from which it received its first $500K in funding. The startup published details of its platform on the YC website just days ago.

02 /Why It Matters

The founder of Optimizely – now a Y Combinator partner – commented on Auctor's launch on Twitter: "Auctor has found a solution to a massive market that most people overlook at first glance. At Optimizely we ran into this problem ourselves, repeatedly, and it required months of coordination across multiple client teams. I genuinely believe in this company's success."

AI that can write code is already accelerating the implementation of technology solutions inside businesses. But Auctor's argument is that coding shouldn't be the first place you reach for AI in an implementation.

Because you can program anything – if only you know what to program. Not knowing what's actually needed is the source of every other problem that emerges downstream.

Auctor believes AI can solve this in three distinct ways:

- First, as a **knowledge integrator** – pulling requirements from multiple sources, including direct client conversations, and surfacing both explicit and implied constraints.

- Second, as a **smart translator** – converting requirements expressed in the client's language into something the implementation team can act on, and flagging anything too ambiguous to translate accurately so the client can sharpen their intent.

- Third, as an **early-warning system** – catching gaps and contradictions in real time, before they cascade into a snowball that's nearly impossible to unwind later.

Auctor frames this as a new methodology for running projects – one that sits outside both Agile and Waterfall.

The core win is compressing delays at every stage, from initial requirements gathering through each implementation milestone. Real-time feedback from the AI is simply faster than the traditional cycle of noticing a problem, escalating to the client, waiting for a response, and backtracking. And the AI can catch drift even when the implementer hasn't noticed anything is wrong.

At a higher level, what Auctor is building is a digital twin of the client. Implementers can query that twin directly to get instant answers. But the twin also acts as a silent presence inside the team – monitoring whether what's being built actually matches what was asked for, without anyone needing to ask it a question.

What's clever is that this concept was hiding in plain sight. Startups had already begun building digital twins of product *users* – so developers could pressure-test roadmap decisions against simulated audience reactions. Velozity ([related review](/review/mgnovenno-vmesto-polugoda)) and Lakmoos are examples of that. The leap from "simulated user" to "simulated client" is a short one.

There's a nuance worth noting: real users are notoriously poor at articulating what they actually want – they either don't know, or they tell you what they wish were true. That's part of why simulating them can surface more reliable signals than surveying them. Enterprise clients, by contrast, generally do know what they want and tend to be more straightforward. So the same digital-twin technology applied here should produce even sharper results – while also smoothing over the inconsistencies and contradictions that always creep into real client communications.

03 /Opportunities

The client digital twin is a genuinely exciting concept. One that could eliminate a huge volume of delays, overruns, and failures across virtually any kind of project delivery – not just B2B software implementations, which is where Auctor is starting.

The broader opportunity is building platforms that create these digital twins for clients across different domains: any context where someone is giving direction to someone else executing work, and where misaligned understanding is the single biggest risk to success.

The most commercially promising adjacent domains are those where deliverables are complex, stakeholders are numerous, and the cost of misalignment is high: construction and infrastructure projects, healthcare IT implementations, and enterprise software migrations. These are markets where implementations already run 12–18 months, budgets routinely blow out, and clients are primed to pay for anything that reduces that pain. The client digital twin, applied there, isn't a productivity tool – it's a business survival tool.