Explore Problems

Intercom's AI support agent generates incorrect information and sometimes sides with customers even when those customers are factually wrong. Support teams using AI deflection cannot trust the bot to represent company policy accurately, creating customer confusion and potential liability when the AI confirms false premises.

Identity theft victims find fraudulent accounts opened in their name across banking institutions, telecom providers, and reporting agencies like ChexSystems simultaneously, with no coordinated process to dispute them all. Each institution requires separate dispute processes, leaving victims to fight the same identity theft on multiple fronts independently. The absence of a unified identity recovery workflow causes extended exposure and ongoing damage across every financial and telecom relationship.

Security professionals and developers lack accessible training environments to practice attacking and defending AI systems against prompt injection, jailbreaks, and agent exploitation. As AI deployments proliferate in enterprise settings, this skills gap represents a growing security risk. There is a clear market need for purpose-built AI red-teaming and defense training platforms.

Developers building AI agents face slow, ad-hoc validation workflows with no standardized way to run evals against agent behavior at speed. The gap between building and reliably testing agents creates compounding quality risk as agentic systems grow more complex.

Companies in regulated sectors (finance, healthcare, legal) cannot adopt AI customer support agents like Intercom Fin because the vendor cannot clearly articulate what customer data is accessed, how it is processed, and what security controls apply. Without audit-grade data governance documentation, compliance teams block AI support adoption regardless of the productivity value. This is a structural gap between AI platform commercial ambitions and the contractual due diligence requirements of enterprise regulated buyers.

Tribal and rent-a-bank lenders charge effective triple-digit APRs, allowing them to extract multiples of the original principal while maintaining an active balance. ACH authorization traps borrowers in indefinite payment cycles with no payoff visibility.

AI agent workflows that call tools, move data, and spend money lack a practical pre-execution decision boundary. Post-event scanners and monitors cannot prevent irreversible actions, and existing policy engines break down for autonomous AI-driven execution.

As AI coding tools dramatically increase engineering velocity, the product specification process has become the new bottleneck. PMs are forced to choose between rushing specs and incurring rework or becoming a drag on delivery. The structural mismatch between human spec-writing speed and AI code generation speed is a growing organizational pain with no clear tooling solution.

Third-party MCP tools that edit files must return plain text content with no way to signal diff rendering, resulting in walls of escaped text instead of colored diffs. The native edit tool gets rich visual rendering that external tools cannot access, creating a first-class vs. second-class experience gap. This is the most frequently cited user complaint for MCP-based developer tools.

Every new AI agent session starts with zero architectural knowledge — developers must re-explain system topology, module relationships, and prior decisions each time. This session amnesia multiplies the overhead of AI-assisted development and compounds as codebases grow. Early adoption signals (190 GitHub stars in two weeks, multi-IDE integrations) confirm this is a widely felt and actively unsolved problem.

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.

Developers building multi-step LLM agents report that models drift from their original task framing over extended sessions, abandoning planned workflows or producing outputs that deviate from agreed specifications. The problem is particularly acute with architect-style sub-agents expected to maintain consistent behavior across many turns. No reliable mechanism exists to detect or correct drift without full session restarts.

Small businesses and startups face sudden Stripe account closures with funds held, disrupting operations without warning or adequate recourse. The dependency on a single payment processor amplifies the impact. This is a structural risk for any business using Stripe as their primary payment infrastructure.

Teams deploying AI agents face a false choice between blocking all shell and command execution or granting full execution rights. There is no middle layer that allows verified, audited command macros to run while blocking novel or dangerous commands. This gap forces either security compromises or significant developer friction.