Explore Problems

An organisation evaluated every AI feature its support platform offers and found each fell short of what the vendor documentation led them to expect. The gap is not a single broken feature but a consistent shortfall between marketed and delivered capability. Buyers absorb the evaluation cost repeatedly as vendors attach AI to incumbent suites.

Site owners adding popular block-AI-crawlers robots.txt snippets often unknowingly disable a rule they already had, due to how robots.txt directive precedence works. The mistake is invisible until crawl behavior or indexing changes are noticed.

Users of Salesforce describe the platform as clunky and disorganized, noting that its high degree of customizability comes at the cost of a clear, solid interface. This reflects a broader usability tension in general-purpose CRMs that try to serve every use case.

As AI has collapsed the cost of building software, the cost of running and maintaining what's built has stayed flat or grown, so capable builders increasingly stall out not from lack of skill or demand but from the accumulating operational weight of running multiple products. This shift moves the constraint on solo/indie output from creation to ongoing operations, a gap current tooling hasn't caught up to.

Legal and procurement teams manually read through contracts to extract key clauses, assess risk, and answer routine questions, which is slow and repetitive. This drains time from higher-value negotiation work, and the problem compounds as contract volume grows across disconnected storage systems like Drive, OneDrive, and Dropbox.

After returning or exchanging a financed vehicle within a return window, consumers report online car retailers holding onto loan payoff checks for months, leaving them with an active loan for a car they no longer own. Conflicting information from support agents compounds the delay.

When an AI agent fails through no fault of the prompt, users must rerun it and spend more tokens. Usage-based billing makes users absorb the cost of model unreliability, which matters as teams adopt agents for routine work.

Car owners facing a repair bill often lack an objective way to decide whether to fix the vehicle or walk away and replace it, risking either overpaying for a doomed car or discarding a still-viable one. This decision-support gap affects anyone facing an unexpected repair estimate.

A customer kept being charged for a roadside-assistance add-on months after cancelling the underlying auto policy because the add-on runs on a separate, undisclosed billing agreement discovered only by chance on a credit card statement. Resolving it required five transfer calls and a hostile support interaction before the add-on could be cancelled.

A Carvana buyer says the vehicle condition report listed a resolved shake and adequate brake pad thickness, but an independent shop found brake pads worn to unsafe levels and lug nuts over-tightened to the point of breaking one. Carvana would only cover the resulting failure under a paid warranty deductible.

A business-services firm hires an ongoing, detailed B2B researcher because its 59 target-account lists require nuanced verification, source-reading, and rejection rules that go well beyond simple firmographic filtering, indicating a gap in existing automated list-building tools for complex criteria.

Market researchers who pay for third-party survey panel responses find a significant share of the data unusable due to fraud, bots, or low-effort answers, forcing them to discard responses after paying for them. This wastes research budget and undermines confidence in survey-based insights.

Farms and growers depend on expensive agronomy consultants or trained staff to diagnose plant diseases during routine crop scouting. Smaller operations lack an affordable way to let general staff identify and respond to plant health issues independently.

Educators running self-hosted or open-source learning platforms need a way to protect paid course video content from piracy, but proper DRM like Widevine is costly and complex, while common alternatives like encrypted HLS only obscure the key rather than truly protecting it. Creators are left choosing between expensive enterprise DRM and weaker protections like per-user watermarking that only deters rather than prevents redistribution.

A microwave purchased from Home Depot caught fire 60 days after purchase and was confirmed unrepairable by a technician, but Home Depot refused to replace it, directing the customer to the manufacturer instead. The customer is still making monthly credit card payments on the now-unusable, fire-damaged appliance.

Makers who launch products on discovery platforms like Product Hunt get a single day of traffic before their listing disappears into an unbrowsed archive. Weeks of launch preparation yield only short-lived visibility, leaving no lasting channel for continued discovery or backlink value.

Developers building on LLMs typically judge prompt quality by manual spot-checking rather than running it against a structured test dataset, leaving them without a pass rate, per-case failure reasoning, or a way to catch regressions when a prompt is edited. This makes prompt iteration largely guesswork instead of a measurable, repeatable process.

Personal assistants, coordinators, and admin staff juggling multiple stakeholders accumulate hours of unscheduled tasks and interruptions that never appear on any calendar. Without automatic tracking, they have no evidence to show when competing priorities collide and they need to justify how their time was actually spent.

A resident covered under a building-wide fire and water insurance agreement experienced repeated radiator leaks that damaged their unit, but the insurer has been resistant to paying out despite the coverage. This points to friction in claims handling for group/building-level policies where individual residents struggle to get insurers to honor coverage terms.

A cardholder reports recurring unauthorized monthly charges on a card they never used, and finds that disputes filed with all three credit bureaus fail to get the charges removed or corrected. This highlights a breakdown in the fraud dispute and credit reporting correction pipeline for affected consumers.