Explore Problems

AT&T customers who trade in phones report that promised bill credits are never applied, requiring repeated calls that go unresolved as agents escalate without action. Long-term customers experience this across multiple upgrade cycles. The failure appears systemic — trade-in credit fulfillment is tracked separately from the promise made at sale, with no automated reconciliation.

Consumers whose personal information was stolen find fraudulent accounts appearing on their credit reports that they have no way to quickly remove. The dispute process is slow, burdensome, and often ineffective at actually removing confirmed fraud. Credit bureaus continue reporting the accounts while investigations drag on, damaging credit scores.

First-time founders accepted to competitive programs like YC experience acute delivery anxiety when presenting under real partner scrutiny. Existing coaching is generic; there is a gap for realistic high-pressure practice environments that simulate the specific dynamics of investor partner calls and interviews.

Businesses and individuals needing to collect signatures on documents face high recurring subscription costs from incumbents for what is fundamentally a simple workflow. This pricing model is especially painful for low-volume senders who only occasionally need to get PDFs signed. The gap has been validated by builders shipping lightweight alternatives with no-account signer flows and audit trails.

Job seekers manually copy job descriptions into resume tools with no in-browser solution that shows match scores and suggests CV improvements at the listing.

Insurance companies route claims through multiple disconnected third-party vendors whose staff lack training on each other's systems, creating multi-day delays for simple claims. Policyholders are forced to personally track and push the process forward across departments. This coordination failure is structural across large insurers and represents a gap in claims management software.

Progressive assigned three adjusters to a single not-at-fault claim; the final adjuster determined liability against the customer without reviewing any evidence, was dismissive, and left no appeal path. Physical damage evidence clearly contradicted the ruling.

Allstate customers report systematic claim denials for auto and home insurance events they paid to be covered against. The company collects premiums but routinely refuses to pay out, contradicting the purpose of insurance coverage.

U-Haul cancelled a confirmed truck reservation less than 24 hours before pickup, provided an alternative 36 miles away, and offered no compensation for the disruption or out-of-pocket costs incurred. The pattern appears connected to the platform prioritizing higher-value contract moves.

Development teams juggle .env files, share credentials via Slack, and lack a standard approach to secrets management. With 29 million secrets leaked on GitHub in 2025, the problem remains widespread despite existing tools like Vault and Doppler.

Monday.com automation and integration features require technical expertise beyond what most users have, leaving valuable features unused.

Founders at sub-$200k ARR after 3 years face an emotional and analytical go/no-go decision with no clear benchmarks or decision frameworks to guide them

Founders repeatedly discover that their best customers are in unexpected segments that pay more and churn less, but this insight comes too late and by accident rather than through systematic customer segmentation analysis

Developers using AI coding agents like Cursor or Claude Code to build UIs find that generated components ignore existing design systems, mix inline styles, and produce hallucinated code that becomes inconsistent and production-unready after a few iterations. This structural limitation of context-unaware AI code generation is a major pain point as AI coding adoption accelerates.

As organizations deploy multiple self-improving AI agents across tools, memory systems, and workflows, managing them as a coordinated fleet lacks dedicated tooling. Existing solutions handle individual agent observability but not fleet-level governance, policy enforcement, and cross-agent coordination. The gap widens as agent adoption accelerates.

Indie developers releasing apps in multiple languages must manually create and update screenshot sets for each locale on every release, a process that doesn't scale. There is no official tooling to automate localized screenshot generation from a single source. The pain is confirmed by developers building their own automation tools to solve it.

Developers building with multiple AI models lack a single workspace to orchestrate parallel agents, browser, and IDE simultaneously, forcing constant context switching. Multi-agent coordination tooling represents an emerging infrastructure gap as agentic AI workflows become standard practice.

Engineering hiring teams are struggling to design assessments that meaningfully evaluate candidates now that AI tools are a normal part of how engineers work. Banning AI makes assessments feel artificial while allowing it without redesigning the evaluation produces noisy signals that conflate prompt skill with engineering ability. There is a clear and growing market need for AI-native technical assessment frameworks and tooling.

AI agents relying on web search face latency and dependency issues with incumbent providers not designed for programmatic agent use. The need for a custom-built search API with own crawler and retrieval models indicates a clear market gap as agent workloads scale.

Teams building AI agents find that standard benchmarks are poor predictors of real-world performance, making it difficult to evaluate and compare agents reliably. This creates a gap in the evaluation tooling ecosystem as multi-agent architectures become more common.