Account Executive – GTM Lead

The AlleyWatch startup daily funding report: 4/8/2026. The latest venture capital, seed, pre-seed, and angel deals for NYC startups for April 8, 2026 featuring funding details for Patlytics, Mosaic, and much more. This page will be updated throughout the day to reflect any new fundings. Patlytics - $40M Series B. ENTERPRISE Patlytics, an AI platform for the full patent lifecycle, from invention disclosure and application drafting to infringement analysis and portfolio management, has raised $40M in Series B funding led by SignalFire, with participation from N47, Myriad Venture Partners, Relativity, Alumni Ventures, Antiportfolio Ventures, and BAM Corner Point. Founded by Paul Lee and Arthur Jen in 2023, Patlytics has now raised a total of $65M in reported equity funding. REACH NYC TECH LEADERS Advertise with AlleyWatch and get your brand in front of the NYC tech community. Mosaic - $18.4M SEC filing. ENTERPRISE Mosaic, an AI-powered platform that automates the creation of deal models and financial analyses from confidential information memoranda for private equity and investment banking professionals, has raised $18.4M in funding according to a recent SEC filing. The filing indicates that the total offering is for $18,437,489 and there were 43 investors in this close. Founded by Ian Gutwinski in 2020. About alleywatch. Contact.

Beyond patent databases: using AI to expand prior art search and de-risk product launches. Sometimes the most important prior art isn't found in a patent database, instead it's buried in a ten-year-old paper, university thesis, or technical conference proceeding. For modern IP teams, limiting research to traditional patent repositories creates blind spots. At the same time, rising costs and timelines for Freedom to Operate (FTO) analysis make it difficult to proactively assess risk before product launch. These challenges point to a broader shift: IP workflows must expand beyond static databases and manual review into AI-driven, integrated research environments. Patlytics addresses both sides of this equation, bringing 250 million non-patent literature (NPL) sources into invalidity workflows while enabling faster, more scalable FTO analysis for in-house teams. Expanding prior art search beyond patents. Traditional invalidity workflows often focus on patent databases. While essential, these sources do not capture the full universe of prior art. Technical innovation is frequently documented first in: * Academic journals * Conference papers * Industry whitepapers * Preprints and technical reports Missing these sources can lead to incomplete invalidity strategies. Patlytics integrates the OpenAlex database, providing access to over 250 million publications across 250,000+ sources, including journals, books, and conference proceedings. This allows practitioners to move beyond patent-only search and conduct comprehensive prior art analysis across both patent and non-patent sources. Precision search across massive NPL datasets. Access to data is only useful if it can be searched effectively. To navigate this scale, Patlytics provides advanced Boolean search capabilities, allowing users to construct precise queries using: * AND (to combine concepts) * OR (to expand search scope) * NOT (to exclude irrelevant results) These queries can be applied across: * Titles * Abstracts * Full-text content This level of control allows practitioners to target specific technical concepts and refine results quickly, reducing noise while improving relevance. Interacting with prior art through AI. Finding relevant non-patent literature is only part of the challenge. Many of these documents are dense, technical, and time-consuming to review. Patlytics addresses this through a built-in AI Chat Agent, which allows users to interact directly with Open Access NPL sources. Instead of manually reading through entire papers, practitioners can: * Ask targeted questions about disclosures * Clarify technical concepts * Identify relevant passages tied to claim limitations This transforms prior art review from a static reading process into an interactive, AI-assisted analysis workflow. De-Risking product launches with ai-driven FTO analysis. While invalidity focuses on defending patents, Freedom to Operate (FTO) analysis is about avoiding risk before it materializes. For in-house teams, this process is traditionally expensive and slow, often requiring external legal investigations costing $20,000 to $50,000 per matter. As product cycles accelerate, this model becomes increasingly difficult to sustain. Patlytics enables teams to run FTO analysis earlier and more efficiently, reducing reliance on external reviews and enabling faster decision-making. Automating Feature Extraction from product materials. FTO analysis begins with understanding the product. Instead of manually translating technical documentation into search queries, Patlytics uses Automated Feature Extraction. Teams can upload: * Product specifications * Engineering documents * Slide decks (.pptx) * PDFs, Word documents, or images The system automatically parses these materials and extracts key product features, which are then used as the basis for patent searches. This reduces manual effort and ensures that searches are grounded in the actual technical details of the product. Ensuring Quality with document validation. One of the risks in automated workflows is relying on incomplete or insufficient input data. To address this, Patlytics includes an FTO Document Quality Check, which evaluates whether uploaded materials contain enough technical detail to support a meaningful search. This step helps ensure that: * The analysis is based on substantive information * Results are reliable and actionable * Teams avoid false confidence from weak inputs Clear risk assessment across potentially blocking patents. Once the analysis is complete, the platform provides a clear, aggregated risk view. Rather than requiring manual interpretation of claim charts, Patlytics evaluates how closely product features align with patent claims and assigns: * High risk * Medium risk * Low risk This scoring is based on alignment across independent claims and their limitations, providing an at-a-glance understanding of potential blocking patents. This allows teams to: * Quickly triage risks * Prioritize deeper analysis where needed * Make faster go/no-go decisions From fragmented research to integrated IP workflows. Historically, invalidity research and FTO analysis have been treated as separate, manual workflows. By integrating: * Patent and non-patent literature search * AI-assisted document analysis * Automated feature extraction * Real-time risk assessment Patlytics creates a connected environment where research and decision-making happen together. A new standard for patent research and risk management. The increasing complexity of technology and the scale of global data requires a new approach to IP workflows. Teams can no longer rely solely on: * Patent-only databases * Manual document review * Late-stage risk assessments Instead, the shift is toward: * Comprehensive data coverage (including NPL) * AI-assisted analysis and interaction * Proactive, early-stage risk evaluation From discovery to decision, faster. The ability to search 250 million NPL sources and run FTO analysis from real product materials fundamentally changes how IP teams operate. Instead of reacting to risk after the fact, teams can: * Identify prior art more comprehensively * Understand technical disclosures more quickly * Evaluate product risk earlier in development * Make decisions with greater confidence Sometimes the most valuable insight isn't in a patent, it's in a paper you didn't know to look for. With AI-driven workflows, this becomes easier to manage. To try out Patlytics' features for NPL, prior art, and the rest of the patent lifecycle, book a demo today.

Patlytics' Claim Construction module. Patlytics has introduced a dedicated Claim Construction module, transforming claim interpretation from a manual bottleneck into a structured, AI-assisted process. The result is faster, more consistent, and fully citation-backed claim construction. Centralizing Claim Construction in a dedicated workflow. Historically, claim construction work has been scattered across notes, claim charts, and separate research tools. This fragmentation makes it difficult to maintain consistency and track how interpretations evolve. Patlytics brings this work into a centralized Claim Construction module, allowing practitioners to manage interpretations in a single, structured environment. Users can: * Select and manage multiple claims within one workspace * Define and refine key claim terms * Track interpretations across the lifecycle of a matter This centralization ensures that claim construction is no longer an isolated task, it becomes a core, connected component of the broader litigation workflow. Automated term identification and interpretation. One of the most time-consuming steps in claim construction is identifying which terms actually require interpretation. Patlytics automates this process. During claim charting, the platform: * Identifies key claim terms that warrant construction * Proposes initial interpretations based on the specification * Surfaces relevant language that informs claim scope This allows practitioners to focus immediately on substantive analysis, rather than spending time identifying terms manually. Citation-Backed interpretations for defensibility. In litigation, every claim construction must be grounded in evidence. Patlytics ensures this through pin citations directly to the specification, providing a clear link between each interpretation and its supporting text. Practitioners can: * Click citations to view the exact referenced language * Verify how the interpretation is supported * Ensure alignment with intrinsic evidence This approach reduces the risk of unsupported interpretations and makes outputs more auditable and defensible in legal proceedings. Consistency across the entire workflow. Inconsistent claim interpretations are a common source of error in patent litigation. When the same term is interpreted differently across claim charts, arguments, or reports, it can weaken the overall case strategy. Patlytics addresses this by maintaining shared claim construction across workflows. Once a term is defined: * The same interpretation is applied across infringement analysis * It carries through invalidity charts and detection reports * It remains consistent across drafting and litigation materials This ensures that the entire work product reflects a unified understanding of claim scope. Unlocking the file wrapper with AI. Claim construction cannot be separated from prosecution history. Key details, such as disclaimers, amendments, and applicant arguments, often define how a term should be interpreted. However, extracting this information from large file wrappers is traditionally a manual and time-intensive task. Patlytics' Prosecution History Agent automates this process. The system: * Analyzes full file wrappers in minutes * Identifies statements that impact claim scope * Surfaces potential disclaimers or estoppel Instead of manually reviewing hundreds of pages, practitioners can ask targeted questions such as: * How was this term narrowed during prosecution? * Did the applicant disclaim any embodiments? * What arguments were made to overcome prior art? This enables faster, more precise claim construction grounded in the full prosecution record. Testing Claim Construction hypotheses in real time. Claim construction is not static, it is iterative. Attorneys often test multiple interpretations to understand how different constructions impact infringement or invalidity positions. Patlytics enables this through dynamic hypothesis testing. Practitioners can: * Broaden or narrow specific term interpretations * Add or remove constructions for selected terms * Revert to plain and ordinary meaning when appropriate Once an interpretation is updated, the system allows users to regenerate claim charts instantly, applying the new construction across all analyses. This makes it possible to see, in real time: * How a narrower construction affects infringement reads * Whether a broader interpretation introduces prior art risk * How claim scope changes impact overall case strategy This level of iteration allows teams to move from static analysis to data-driven strategy development. Connecting Claim Construction to litigation strategy. The real value of claim construction lies in how it informs downstream decisions. By integrating claim construction directly into infringement, invalidity, and detection workflows, Patlytics ensures that interpretations are not just defined, but actively used. This creates a connected litigation workflow, where: * Claim construction informs infringement analysis * Prosecution history shapes invalidity strategy * Adjustments to scope immediately reflect across reports Instead of treating claim construction as a standalone exercise, it becomes a dynamic input into case strategy. From manual review to strategic advantage. The traditional approach to claim construction is slow, fragmented, and resource-intensive. By automating term identification, structuring interpretations, analyzing prosecution history, and enabling real-time iteration, Patlytics transforms this process into a scalable, AI-assisted workflow. The impact is clear: * Reduced time spent on manual review * Improved consistency across work product * Stronger, citation-backed interpretations * Faster development of litigation strategy A new standard for Claim Construction. Claim construction will always require legal judgment. But the process of getting there no longer needs to rely on manual review and disconnected tools. With a dedicated Claim Construction module, Patlytics enables practitioners to move faster, work more consistently, and build interpretations that are both defensible and strategically aligned. To try out Patlytics' claim construction, book a demo today.