Signal Through the Noise: An AI Product Builder’s Guide
As AI capabilities rapidly advance, the challenge for product teams has shifted from “what can we build?” to “what should we build?” The following insights, drawn from recent presentations and conversations with AI founders, successful product launches, and emerging security research, offer practical guidance for teams designing AI applications that users will actually adopt and trust.
1. Master Vertical Domains to Build Defensive Moats
While horizontal AI platforms offer broad capabilities, breakout enterprise successes consistently emerge from deep vertical specialization. Generic models struggle with industry-specific terminology, nuanced workflows, and domain-particular success metrics. Companies that achieve mastery within specific sectors can command premium pricing while building defensible positions that larger, generalized competitors find difficult to penetrate.
Shortcut’s exclusive focus on spreadsheet-based financial modeling allows it to outperform general-purpose AI on domain-specific tasks. This vertical depth enables understanding subtle differences between DCF methodologies, automatically formatting outputs to match firm standards, and handling the idiosyncratic definitions that financial analysts use daily—capabilities that are hard to achieve with a horizontal platform serving multiple industries. Note that Shortcut’s strength is in generating new models that adhere to financial conventions, not necessarily in interpreting complex existing ones. Its performance can vary significantly depending on the task, with notable limitations when working with existing spreadsheet models rather than creating new ones from scratch.
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2. Start with Concrete Problems, Not Vague Ambitions
Speed in product development is impossible without clarity. Vague goals like “using AI to improve e-commerce” are too ambiguous for an engineering team to act on decisively. Different team members will interpret the goal in completely different ways, leading to wasted cycles. A concrete idea, by contrast, is specified in enough detail that it can be built and tested immediately.
Instead of a broad ambition, a concrete proposal might be: “Build a feature for Shopify store owners that, given a product’s title and images, automatically generates three distinct product descriptions: one focused on technical specifications, one on lifestyle benefits, and a concise version for social media posts.” This idea may or may not be successful, but its concreteness allows a team to build it quickly, learn from the market, and either validate or discard the hypothesis without delay.
The process of developing concrete ideas typically requires sustained domain expertise—either from founders who have spent considerable time understanding user problems or from subject matter experts who can provide detailed workflow knowledge.
3. Balance Feedback Speed with Signal Quality for Faster Iteration
Early-stage AI products attract significant “tourist traffic” from users driven by curiosity rather than genuine need, creating noise that obscures product-market fit signals. The most valuable feedback comes from extreme reactions—users who either love the product intensely or hate it after serious usage. In a recent conversation with the founders of Huxe, they described how their most valuable early users fell into two distinct categories: those who became passionate advocates despite barely understanding why the product worked so well for them, and those who had strong negative reactions after attempting to use it seriously. The latter group’s frustration often stemmed from unmet expectations about what the AI should be capable of, revealing crucial insights about market readiness and product capabilities.
Effective feedback collection requires balancing speed with accuracy using a hierarchy of methods. Domain experts with deep problem understanding can make surprisingly accurate gut decisions instantly, while broader validation requires progressively slower approaches: consulting colleagues, gathering input from strangers in high-traffic locations, distributing prototypes to larger groups, and conducting formal testing. The goal is not just collecting data to make individual decisions, but using each feedback cycle to refine intuitive judgment, enabling faster and more accurate gut decisions in subsequent iterations. Teams that master this progression can maintain rapid development cycles while filtering for the polarized reactions that indicate genuine product-market fit.
Winning AI products go deep, not wide—vertical mastery builds a moat generic models can’t cross.
4. Design for Modality-Specific Workflows
Different interaction modalities unlock fundamentally different use cases, not just different interfaces for the same functionality. Voice interactions surface conversational patterns that text interfaces rarely touch, while visual inputs enable entirely new categories of analysis. In a recent conversation with one of the founders of Huxe, Raiza Martin observed how switching from text to audio completely changed the types of questions users asked and the depth of personal information they were willing to share.
This principle extends beyond input methods to output formats as well. Users consuming information during a commute need different packaging than those reviewing detailed analysis at their desk. The most successful AI products deliberately choose modalities that align with specific user contexts rather than trying to be universally accessible through every interface.
5. Design for Persistent Workflows, Not One-Shot Interactions
A fundamental shift is occurring from transactional prompt-and-response tools toward persistent agents that learn workflows and execute tasks over time. While traditional AI applications require users to repeatedly specify similar requests, intelligent agents function as dedicated workers that accumulate context, remember preferences, and proactively deliver value without constant supervision.
The founder of Boosted articulated this distinction clearly: their agents “learn a specific task and then perform that task repeatedly and forever.” Rather than answering isolated questions, these systems continuously monitor earnings calls for specific companies, scan email for relevant analyst updates, or track map data for new store locations. This persistent approach creates compound value as agents accumulate domain knowledge, making competitive displacement increasingly difficult.
6. Build for AI-First Architecture, Not Human Interface Simulation
The most effective AI integrations avoid the crude approach of simulating human computer use—moving cursors, reading pixels, or typing into UI elements designed for people. As Hjalmar Gislason (CEO of GRID) observes, current “AI computer use” often involves unnecessary complexity, with systems spinning up virtual machines to complete tasks through user interfaces rather than accessing underlying functionality directly.
For common, repeatable tasks like spreadsheet calculations, document generation, or data analysis, headless systems that operate directly on files, data, and logic without UI interference prove far more efficient. While operator-style approaches may remain necessary for the long tail of obscure software interactions, everyday productivity tasks benefit from clean, machine-friendly APIs and protocols designed specifically for AI consumption.
This architectural distinction becomes crucial as more work shifts to autonomous systems. Rather than forcing AI to “pretend to be human,” successful products separate their interfaces: one optimized for human users, another designed for programmatic access by agents and AI systems.
7. Build Systems That Orchestrate, Not Single Models
The most reliable AI applications function as sophisticated orchestration systems that delegate tasks to specialized components rather than relying on a single, all-purpose model. This architectural approach separates probabilistic reasoning from deterministic computation, routing summarization tasks to language models while directing mathematical operations to traditional calculators or databases. The result is greater accuracy, improved auditability, and reduced risk of unpredictable failures.
Boosted exemplifies this through what they call a “large language model choir.” When processing complex financial analysis requests, their system employs a reasoning model to decompose tasks, specialist models optimized for specific operations like data extraction, and authenticator models that verify results against source materials. Similarly, Shortcut integrates directly with Excel’s native calculation engine, allowing the AI to focus on model construction while leveraging proven mathematical accuracy.
8. Architect Context Management at the Application Layer
Creating personalized, continuous AI experiences requires sophisticated memory systems, but feeding entire conversation histories to models is inefficient and raises privacy concerns. An alternative approach involves building durable context layers at the application level that intelligently curate and provide only relevant information for specific tasks while maintaining strict data boundaries between users.
Huxe’s architecture simulates human memory patterns by storing conversation history in their application infrastructure and algorithmically determining what minimal context to provide for each model interaction. This design ensures that sensitive personal data from emails or calendars enhances only that individual user’s experience rather than contributing to global model training, while still enabling relevant historical context when appropriate.
9. Implement Radical Transparency for Professional Contexts
Professional users require complete visibility into AI decision-making processes before trusting systems with high-stakes tasks. Opaque systems that provide conclusions without explanation are unacceptable in domains like finance, law, or healthcare. Building trust requires comprehensive auditability where reasoning processes, data sources, and methodologies are fully transparent and verifiable.
Shortcut addresses this through detailed review interfaces that allow users to inspect every AI-generated modification, distinguish between formula-driven and hard-coded values, and trace all inputs back to primary sources. This transparency transforms AI from an inscrutable oracle into a verifiable collaborator, enabling users to understand exactly how conclusions were reached while ensuring consistency across repeated analyses.
The most compelling AI business model isn’t about usage—it’s about results.
10. Invest in Domain-Specific Evaluation Frameworks
Public benchmarks provide useful initial filtering for model capabilities, but they rarely predict performance on specific business tasks. The Boosted team developed proprietary benchmarks for tensor manipulation, foreign-language data processing, and financial metric extraction with nuanced variations. These custom evaluations become intellectual property that guides model selection and optimization decisions.
Effective evaluation frameworks test both individual components and complete workflows under realistic conditions. They should capture the tradeoffs between intelligence, cost, and latency that matter for specific use cases. Teams often underinvest in evaluation infrastructure early in development, then struggle to optimize performance as requirements become more sophisticated.
11. Price Based on Outcomes, Not Usage
The most compelling business model innovation in AI products involves shifting from traditional seat-based or usage-based pricing to outcome-based models where customers pay only for successful results. Rather than charging for access or computational resources consumed, companies like Sierra and Intercom now price their AI agents based on resolved customer service tickets. This approach fundamentally aligns vendor incentives with customer value, creating a relationship where both parties benefit from improved AI performance.
Unlike consumption-based pricing, outcome-based pricing is tied to tangible business impacts—such as a resolved support conversation, a saved cancellation, an upsell, or a cross-sell. This model transforms software purchases from cost centers into direct investments in measurable business improvements, while forcing AI companies to continuously optimize their systems for reliability and effectiveness rather than just maximizing usage.
12. Secure Against Novel Attack Vectors
As AI agents gain capabilities to process external data and execute commands, they introduce previously unknown security vulnerabilities. Recent research from HiddenLayer demonstrated how malicious actors can embed hidden instructions in seemingly benign files like GitHub README documents, manipulating AI coding assistants to steal credentials or execute unauthorized commands without user knowledge.
This vulnerability extends to any AI system processing external data sources, requiring fundamental changes to security architecture. Product teams must implement robust input validation, strict capability sandboxing, and real-time anomaly monitoring from the initial design phase. As agents become more autonomous and powerful, treating security as a core design constraint rather than an afterthought becomes essential for maintaining user trust and system integrity.
According to a recent Microsoft study, Generative AI demonstrates broadest reach when augmenting information-based work — helping users gather information, write content, and explain concepts across the full breadth of these activities. However, its coverage narrows significantly for tasks requiring physical interaction, personal verification, or complex coordination, and consistently shows more limited effectiveness when performing tasks autonomously versus assisting users. For developers, this data indicates that AI solutions should prioritize augmentation over automation, particularly for knowledge work where users retain control while AI provides comprehensive support across entire workflows.
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