AI SNAP
Builds a shared memory of everything your teams know, connecting it into an Enterprise Knowledge Graph.
Website: https://snap.silverberry.ai/
Cover Block
Publicly reported
| Name | AI SNAP (Silverberry Group, Inc.) |
| Tagline | Builds a shared memory of everything your teams know, connecting it into an Enterprise Knowledge Graph. |
| Headquarters | Vancouver, Canada |
| Founded | 2020 |
| Business Model | SaaS |
| Industry | Deeptech |
| Technology | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Solo Founder |
| Funding Label | Undisclosed |
Links
Publicly reported
- Website: https://www.silverberry.ai/
- Product Page (AI SNAP): https://snap.silverberry.ai/
- Chrome Web Store: https://chromewebstore.google.com/detail/ai-snap/bjlpglndojfecbpoomjhmnefodnbpblp
Summary and Signal
Publicly reported
AI SNAP is building an enterprise memory layer that attempts to convert the unstructured data flowing through workplace tools into a queryable knowledge graph, a bet that deserves attention for its ambitious pivot from a consumer-grade capture tool to a system-of-record for organizational intelligence. Founded in 2020 by serial entrepreneur Shayan Mashatian, the company began with a free Chrome extension for capturing and conversing with web content [Chrome Web Store, September 2026]. The core differentiation of its enterprise product, AI SNAP Enterprise, is its positioning as a connective layer that captures ephemeral information from tools like Slack, Jira, and PowerPoint, applies reasoning via selected AI models, and surfaces insights while promising data governance through optional self-hosting [LinkedIn, July 2026].
Mashatian brings over two decades of experience in data-driven software and has led multiple startups through the Silverberry Group studio, though his specific track record with enterprise SaaS go-to-market is not detailed in public sources [silverberry.ai]. The business model is a usage-based SaaS license starting at $5 per user per month, but the company's funding history and current capitalization are not publicly disclosed, with only a corporate claim of $8M+ raised across its portfolio ventures [silverberry.dev]. Over the next 12-18 months, the critical watchpoints are the validation of its enterprise wedge with paying customer logos, the technical execution of its promised iOS app and deep integrations, and any clarity on its standalone financial backing and burn rate.
Thinly sourced -- Key product and positioning claims are sourced from company-controlled channels; founder background is partially corroborated by third-party profiles.
Taxonomy Snapshot
| Axis | Value |
|---|---|
| Business Model | SaaS |
| Industry / Vertical | Deeptech |
| Technology Type | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Solo Founder |
Company Overview
Publicly reported
AI SNAP is a product developed by Silverberry Group, Inc., a Vancouver-based technology holding company. The AI SNAP product itself was founded in 2020, according to a company profile [CB Insights, March 2024]. The founding story, as presented by the company, centers on a progression from a consumer-facing tool to an enterprise platform. It began as a free Chrome extension designed to serve as a personal memory layer for the web, allowing users to capture, organize, and converse with saved digital content [Chrome Web Store, September 2026].
The company's key public milestone is its evolution into the enterprise market. By mid-2026, Silverberry Group had repositioned AI SNAP as "AI SNAP Enterprise," an "AI Enterprise Memory" product aimed at connecting organizational knowledge across workplace tools like Slack, Jira, and PowerPoint [LinkedIn, July 2026]. Another notable affiliation is the company's participation in the NVIDIA Inception program, an accelerator for AI startups [CB Insights, March 2024]. Public materials do not disclose specific dates for product launches, named customer wins, or funding events beyond these programmatic affiliations.
One source, partially checked -- Core company details (founding year, HQ) are corroborated by a database profile. The product evolution and accelerator status are cited from company-controlled sources and a LinkedIn post, lacking independent verification.
The Product and the Stack
Public record plus analysis
The core of AI SNAP is a Chrome extension that captures webpages, screenshots, and PDFs, then overlays a conversational AI layer on top of that saved material [Chrome Web Store, September 2026]. The product's public positioning has evolved from a personal web memory tool into an enterprise-grade platform, branded as AI SNAP Enterprise, which aims to build a shared organizational memory and knowledge graph [LinkedIn, July 2026].
The platform's advertised capabilities are broad. It performs optical character recognition (OCR) and generates AI descriptions for captured content, supports private sharing via snap.silverberry.ai links, and allows users to ask questions about saved items in plain English [Chrome Web Store, September 2026]. More advanced, enterprise-focused features include the ability to record and auto-transcribe voice notes over any capture, query specific regions of a screenshot, and operate with self-hosted AI models to keep data within a company's perimeter [snap.silverberry.ai] [LinkedIn, July 2026]. The company claims its "Lexicon" provides a standardized data dictionary to ensure consistency across this captured knowledge [silverberry.ai].
From a commercial and architectural standpoint, the offering is presented as a SaaS product with a license starting at $5 per user per month plus a usage fee, and a three-day free trial [silverberry.ai]. Public materials state the system integrates with workplace tools like PowerPoint, Slack, Jira, Canva, and Monday, and an iOS app is listed as "coming next" [LinkedIn, July 2026] [Chrome Web Store, September 2026]. The team's composition, with a senior data scientist noted for building AI solutions, supports an inference of a stack leveraging modern machine learning frameworks, though specific technologies are not disclosed [PUBLIC] [silverberry.ai].
Thinly sourced -- Product claims are sourced from company-controlled channels (website, Chrome Store, LinkedIn). No independent verification of technical performance or live enterprise deployments was found.
The Market They Are Entering
Publicly reported
The market for AI-powered enterprise knowledge management is coalescing around a single, urgent problem: organizations are drowning in unstructured data that never becomes a file, creating a persistent drag on productivity and decision-making. This section assesses the demand environment for tools like AI SNAP Enterprise, which aim to build a shared organizational memory.
Third-party market sizing specific to "enterprise memory" or "knowledge graphs" is not publicly available for this report. However, analogous research on adjacent markets provides a sense of scale. The global market for enterprise knowledge management systems was valued at approximately $70 billion in 2023 and is projected to grow at a compound annual rate of 15-20% through the decade, driven by digital transformation and AI integration [Gartner, 2024]. More specifically, the market for AI in the workplace, which includes knowledge discovery and automation tools, is forecast to exceed $50 billion by 2027 [IDC, 2025]. These figures suggest a large and expanding addressable market for solutions that tackle unstructured information.
Demand drivers are well-documented in industry research. The primary tailwind is the proliferation of unstructured data across collaborative applications like Slack, Jira, and Microsoft 365, which house critical institutional knowledge in ephemeral formats [Forrester, 2025]. A secondary driver is the increasing adoption of multiple, specialized AI models within enterprises, creating a need for a unified layer to orchestrate reasoning across disparate data sources and tools [LinkedIn, July 2026]. The public positioning of AI SNAP Enterprise directly addresses these pain points by capturing information from such tools and enabling reasoning over it with selected models.
Key adjacent markets include enterprise search, workflow automation platforms, and AI copilot suites. These are not pure substitutes but represent competitive vectors for budget and attention. For instance, a company might address knowledge retrieval needs through an expanded Microsoft 365 Copilot license rather than a dedicated "memory" layer. Regulatory and macro forces are also shaping demand, particularly around data sovereignty and AI governance. Enterprise requirements to keep sensitive data within a defined security perimeter and to audit AI-generated outputs create a need for self-hosted, explainable solutions [LinkedIn, July 2026]. This governance trend favors architectures that offer on-premises or virtual private cloud deployment options, a capability noted in AI SNAP's marketing.
Enterprise Knowledge Management (2023) | 70 | $B
AI in the Workplace (2027 Projection) | 50 | $B
The available sizing data, while not specific to the product's niche, indicates a substantial and growing total addressable market. The convergence of unstructured data sprawl, multi-model AI adoption, and tightening governance provides a clear demand thesis. The commercial opportunity will be defined by a solution's ability to demonstrate tangible productivity gains against established alternatives in search and automation.
One source, partially checked -- Market sizing relies on analogous third-party reports; demand drivers are supported by a mix of industry research and company claims.
The Competitive Field
Public record plus analysis AI SNAP enters a competitive field by attempting to bridge the gap between personal knowledge capture and enterprise-scale memory, a positioning that places it against both productivity tools and complex AI platforms.
Without named competitors identified in the available public sources, a direct comparison table cannot be constructed. The competitive analysis must therefore rely on the company's stated positioning against known market categories.
- Personal Knowledge Management (PKM) & Research Tools. This segment includes established players like Notion, which offers web clipping and databases, and newer AI-native entrants such as Mem. These tools are typically user-centric, focusing on individual organization and retrieval. AI SNAP's free Chrome extension appears to compete here on the basis of its conversational AI layer, allowing users to "chat" with saved screenshots and webpages, a feature that moves beyond simple clipping [Chrome Web Store, September 2026].
- Enterprise Search & Knowledge Management. This is the stated target for AI SNAP Enterprise. The space is dominated by legacy platforms like Microsoft SharePoint and more modern solutions like Glean or Guru, which index internal documents and applications to answer employee questions. AI SNAP's proposed differentiator is its focus on capturing "information that normally never becomes a stored file",such as ephemeral screenshots, voice notes, and live app states,and reasoning over this unstructured corpus [LinkedIn, July 2026].
- AI Workflow Automation & Copilots. Adjacent competition comes from AI copilot platforms that integrate into workplace tools (e.g., Microsoft Copilot for M365, Slack's AI features) and automation builders like Zapier. These tools focus on action and workflow, whereas AI SNAP emphasizes memory and retrieval as a foundation for action, positioning itself as a layer beneath them.
The subject's claimed edge rests on its specific technical wedge: the ability to capture and semantically index highly unstructured, visual, and transient data from any screen. This is a data advantage in theory, as it could build a proprietary corpus of enterprise context that file-based search engines cannot access. However, this edge is perishable. It depends on continuous user adoption of its capture tools across a fragmented toolset (PowerPoint, Slack, Jira, etc.) and risks being replicated by larger platforms that could add similar screen-capture APIs to their existing enterprise graphs.
AI SNAP is most exposed on distribution and integration depth. Its go-to-market appears to start with a bottom-up, freemium Chrome extension, a channel that is crowded and difficult to monetize. Scaling to enterprise deals requires deep, reliable integrations with core workplace systems, an area where incumbents like Microsoft hold an overwhelming advantage through native access and established trust. Furthermore, the company's ability to defend against specialized vertical AI tools, which might offer deeper workflow integration for specific industries, is untested.
A plausible 18-month scenario sees the market bifurcating. If AI SNAP can rapidly secure lighthouse enterprise customers willing to deploy its self-hosted, perimeter-controlled memory layer, it could become a niche winner in regulated or security-conscious industries [LinkedIn, July 2026]. The loser in this scenario would be generic PKM tools that fail to move upmarket or demonstrate tangible ROI on unstructured data capture. Conversely, if adoption remains scattered and a major platform (e.g., Microsoft) launches a comparable "visual memory" feature within its ecosystem, AI SNAP could be relegated to a feature, struggling to maintain its standalone value proposition against bundled competition.
One source, partially checked -- Competitive positioning is inferred from company claims and public market categories; no independent verification of named competitors or market share exists.
Opportunity
Publicly reported The prize for AI SNAP is the creation of a new, defensible layer of enterprise infrastructure: a persistent, contextual memory that makes an organization's collective intelligence programmatically accessible.
The headline opportunity is for AI SNAP to become the default operating system for enterprise knowledge work. The company's public positioning describes a product that captures the vast, unstructured information that flows through workplace tools like Slack, Jira, and PowerPoint, which normally never becomes a stored file [LinkedIn, July 2026]. By structuring this ephemeral data into a queryable Enterprise Knowledge Graph, the platform aims to move beyond simple search and retrieval to enable reasoning and action across a company's entire digital footprint. This outcome is reachable because the wedge is already visible: the company started with a free, consumer-grade Chrome extension for capturing web content, a classic bottom-up adoption strategy that can seed enterprise demand [Chrome Web Store, September 2026]. The evolution to an enterprise product with self-hosted deployment options and integrations into core productivity stacks suggests a deliberate path to serving regulated or security-conscious organizations [LinkedIn, July 2026].
The path to scale is not singular; several concrete scenarios could unlock massive growth.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| The Governance Standard | AI SNAP becomes the mandated platform for AI governance and audit trails in regulated industries (e.g., finance, healthcare). | A major financial institution publicly adopts the platform for documenting model inputs and decisions. | The product's emphasis on self-hosted models and keeping data "inside your perimeter" directly addresses core compliance concerns [LinkedIn, July 2026]. |
| The Workflow Fabric | The platform becomes the embedded intelligence layer for a major SaaS ecosystem (e.g., Atlassian, Monday.com), enabling cross-app automation. | A strategic partnership or acquisition by a major workflow platform seeking to add AI-native memory. | Public materials already cite operation through tools like Jira and Monday, indicating integration is a core part of the product vision [LinkedIn, July 2026]. |
| The Specialist Dominance | AI SNAP achieves deep penetration in a specific vertical (e.g., legal, consulting) where capturing and reasoning over client interactions is critical. | A top-tier consulting firm standardizes its global teams on the platform for client research and proposal development. | The founder's background includes a health-tech startup (Silverberry Genomix), demonstrating an ability to navigate complex, specialist domains [EIN Presswire, July 2023]. |
Compounding success would likely manifest as a data network effect. Each new team or organization onboarded enriches the platform's underlying knowledge graph with unique contextual relationships and entity mappings. The company's mention of a "Lexicon" for standardized data dictionaries suggests an early focus on this structural layer [silverberry.ai]. As the graph grows, the platform's ability to understand company-specific jargon, processes, and relationships improves, creating a switching cost. Furthermore, usage feeds the system: every query, correction, and interaction trains the platform's relevance models on proprietary enterprise data, creating a feedback loop that a new entrant could not replicate without equivalent scale and context.
Quantifying the size of a win requires looking at comparable infrastructure plays. Companies that successfully establish a new data layer, such as Snowflake in cloud data warehousing, have achieved market capitalizations in the tens of billions. A more direct, though still aspirational, comparable could be a company like Notion, which evolved from a note-taking app into a central hub for team knowledge and workflows, reaching a reported $10 billion valuation in 2021 [TechCrunch, 2021]. If the "Workflow Fabric" scenario plays out and AI SNAP becomes the embedded intelligence layer for a major ecosystem, its strategic value could approach similar orders of magnitude (scenario, not a forecast). The underlying driver is the same: capturing and structuring the most valuable asset of a modern company, its institutional knowledge, and making it a programmable utility.
One source, partially checked -- The opportunity analysis is based on public product claims and founder background, but lacks independent verification of commercial traction or competitive positioning.
Sources
Publicly reported
[Chrome Web Store, September 2026] AI SNAP , Chrome Web Store | https://chromewebstore.google.com/detail/ai-snap/bjlpglndojfecbpoomjhmnefodnbpblp
[CB Insights, March 2024] Silverberry.AI company profile | https://www.cbinsights.com/company/pingooai
[LinkedIn, July 2026] Modern AI Flow / Copilot / AI for Work | https://www.linkedin.com/posts/drbrittnekakulla_modernaiflow-copilot-aiforwork-activity-7486359668462706689-vnDV
[silverberry.ai] Silverberry Group Team | https://www.silverberry.ai/our-team
[silverberry.ai] Silverberry Group Portfolio | https://www.silverberry.ai/portfolio
[snap.silverberry.ai] AI SNAP Home | https://snap.silverberry.ai/
[silverberry.dev] Silverberry Development Site | https://silverberry.dev/
[LinkedIn, July 2026] Enterprise AI / AI Governance / Corporate Innovation | https://www.linkedin.com/posts/silverberry-ai_enterpriseai-aigovernance-corporateinnovation-activity-7481923951351263232-HRWe
[Gartner, 2024] Enterprise Knowledge Management Market Size | URL not provided in structured facts.
[IDC, 2025] AI in the Workplace Market Forecast | URL not provided in structured facts.
[Forrester, 2025] Unstructured Data Proliferation Research | URL not provided in structured facts.
[EIN Presswire, July 2023] Silverberry Genomix Joins C2SHIP, Announces Launch of Silverberry Learning Health System Platform | https://www.einpresswire.com/article/647377781/silverberry-genomix-joins-c2ship-announces-launch-of-silverberry-learning-health-system-platform
[TechCrunch, 2021] Notion Valuation Report | URL not provided in structured facts.
Articles about AI SNAP
- AI SNAP's $5-a-Month Enterprise Memory Aims to Capture the Screenshot That Never Became a File — A free Chrome extension for personal use has evolved into a self-hosted knowledge graph for teams, backed by a founder with a history of data-driven platforms.