Unsiloed AI

Cover Block

PUBLIC

Attribute	Value
Company Name	Unsiloed AI
Tagline	APIs parsing multimodal unstructured data into LLM-ready JSON and Markdown
Headquarters	San Francisco, United States
Founded	2024
Stage	Pre-Seed
Business Model	API / Developer Platform
Industry	AI / Machine Learning
Technology	AI / Machine Learning
Geography	North America
Growth Profile	Venture Scale
Founding Team	Co-Founders (2)
Funding Label	Pre-seed
Total Disclosed	~$500,000 [PitchBook]

Links

PUBLIC

• Website: https://www.unsiloed.ai/
• LinkedIn: https://www.linkedin.com/company/unsiloed-ai
• Y Combinator: https://www.ycombinator.com/companies/unsiloed-ai

Executive Summary

PUBLIC

Unsiloed AI provides APIs that convert complex, unstructured documents into structured formats for AI systems, a critical data plumbing problem that has gained urgency as enterprises move beyond simple chatbot deployments. The company, founded in 2024 by Aman Mishra and Adnan Abbas, emerged from Y Combinator's Winter 2025 batch with a focus on accuracy-sensitive domains like finance and legal [Y Combinator]. Its core product uses proprietary vision models to parse PDFs, images, and spreadsheets into LLM-ready JSON and Markdown, aiming to make unstructured data as queryable as structured databases [Unsiloed AI]. The founding team's backgrounds are not detailed in public profiles, but their selection by YC and membership in the Forbes Business Council suggest an operational and network-driven approach [Forbes]. The company has raised a pre-seed round of $500,000 and reported $550,000 in revenue as of November 2025, a figure that requires independent verification given its single-source nature [PitchBook] [getlatka, Nov 2025]. Over the next 12-18 months, the key watchpoints are the validation of its claimed enterprise customer base, the expansion of its technical differentiation beyond open-source alternatives, and the scaling of its revenue beyond its initial reported figures.

Data Accuracy: YELLOW -- Core product and YC backing are confirmed; revenue and customer claims rely on single, unverified sources.

Taxonomy Snapshot

Axis	Classification
Stage	Pre-Seed
Business Model	API / Developer Platform
Industry	Other
Technology	AI / Machine Learning
Geography	North America
Growth Profile	Venture Scale
Founding Team	Co-Founders (2)
Funding	Pre-seed (total disclosed ~$500,000)

Company Overview

PUBLIC

Unsiloed AI was incorporated in 2024 and is headquartered in San Francisco [Crunchbase]. The company's founding narrative centers on the challenge of making the world's vast unstructured data,locked in PDFs, images, and spreadsheets,directly usable by large language models and AI agents [Unsiloed AI]. Its primary public milestone came in early 2025 with acceptance into Y Combinator's Winter 2025 (F25) batch, a move that provided initial capital and network access [Y Combinator]. A pre-seed round of $500,000 was subsequently closed in 2025, with participation from Y Combinator, Entrepreneurs First, Orange Collective, Spot VC, and Transpose Platform Management [PitchBook, Crunchbase].

By late 2025, the company reported generating $550,000 in revenue, according to a single external source [getlatka, Nov 2025]. Public claims also state the company processes millions of document pages weekly for large financial and public companies, though these customer references are not named [Y Combinator].

Data Accuracy: YELLOW -- Key dates and investor names are corroborated by Crunchbase and PitchBook; revenue and customer claims rely on a single source.

Product and Technology

MIXED

The product is a developer-focused API that ingests documents in a range of unstructured formats and outputs structured data. According to the company, the service converts PDFs, PowerPoint files, DOCX documents, tables, charts, and images into structured Markdown and JSON, formats optimized for consumption by large language models and AI agents [Y Combinator]. The core technical claim is the use of proprietary dual-stream vision models, which combine computer vision with OCR-based techniques to extract information from complex layouts [Unsiloed AI]. This approach is positioned to handle enterprise-grade accuracy requirements, particularly for compliance-sensitive workflows in sectors like finance [Y Combinator].

Job postings for founding engineering roles provide some inferred detail on the technology stack and operational priorities. The company is seeking a Founding ML Researcher to work on vision-language models, multimodal reasoning, and retrieval-augmented generation (RAG) systems [Y Combinator]. A separate posting for a Founding Software Engineer emphasizes building scalable, low-latency backend infrastructure on cloud platforms, suggesting a focus on API reliability and performance at volume (inferred from job postings). No public roadmap or specific feature release dates have been announced.

PUBLIC The market for unstructured data processing is expanding as enterprises accelerate AI adoption, creating a direct demand for tools that can reliably convert documents into formats that large language models can consume. This shift is not merely about data extraction but about enabling the next wave of agentic workflows, where the quality of the input directly determines the reliability of automated decisions.

Third-party market sizing specific to unstructured data parsing for LLMs is not yet widely published. Analysts can approximate the opportunity by looking at adjacent, well-defined markets. The global market for intelligent document processing, which includes traditional OCR and data capture, was valued at approximately $1.5 billion in 2023 and is projected to grow at a compound annual rate of 30% through 2030 [Grand View Research, 2023]. The broader AI in the enterprise market, which this capability feeds, is measured in the hundreds of billions. Unsiloed AI's stated focus on accuracy-sensitive verticals like finance, legal, and healthcare suggests its serviceable obtainable market (SOM) is a high-value slice of this larger ecosystem, where compliance and precision command premium pricing.

Demand is driven by several converging tailwinds. The proliferation of multimodal AI agents requires structured, machine-readable inputs to function, moving beyond simple text prompts. Regulatory pressures in sectors like finance (e.g., Basel III, MiFID II) and healthcare (HIPAA) mandate rigorous document review and audit trails, processes that are increasingly automated. Furthermore, the legacy of digital transformation has left organizations with vast archives of PDFs, scanned contracts, and presentation decks that are now seen as untapped data assets rather than static records.

Key adjacent markets include traditional enterprise content management, robotic process automation platforms that increasingly bundle AI capabilities, and the foundational model providers themselves who are building native ingestion tools. The regulatory environment presents both a barrier and a catalyst. Data privacy laws (GDPR, CCPA) govern how documents containing personal information are processed, while sector-specific rules in finance and healthcare dictate retention and accuracy standards, potentially favoring vendors that can demonstrate compliant, auditable workflows.

Metric	Value
Intelligent Document Processing (2023)	1.5 $B
Projected CAGR (2023-2030)	30 %

The projected growth rate for the intelligent document processing market indicates strong underlying demand, though Unsiloed AI's specific wedge,high-accuracy, LLM-ready output,targets a newer, more specialized segment within it.

Data Accuracy: YELLOW -- Market sizing is drawn from an analogous, broader sector report; specific TAM for LLM-ready document parsing is not independently verified.

Competitive Landscape

MIXED

Unsiloed AI enters a crowded but fragmented market for document parsing, where its positioning hinges on a vision-first approach to multimodal data extraction for enterprise AI pipelines.

The competitive map can be segmented by technical approach and go-to-market focus. Incumbent data extraction platforms like Hyperscience and Rossum have built deep enterprise workflows with human-in-the-loop validation, often targeting specific verticals like accounts payable. Open-source libraries and frameworks, such as those from the Unstructured.io ecosystem, provide a low-cost, developer-friendly baseline for parsing common formats. A newer cohort of API-native challengers, including LlamaParse and Reducto, directly target the LLM application layer, promising simpler integration and output optimized for models like GPT-4. Unsiloed appears to sit within this last group but with an explicit emphasis on combining vision models with OCR for high-accuracy handling of charts, tables, and handwritten content [Y Combinator].

Company	Positioning	Stage / Funding	Notable Differentiator	Source
Unsiloed AI	Vision model APIs for converting PDFs, images, spreadsheets into LLM-ready JSON/Markdown.	Pre-seed, YC F25. ~$500k disclosed [PitchBook].	Proprietary dual-stream vision models; claims compliance-ready workflows for finance.	[Unsiloed AI], [Y Combinator]
Unstructured	Open-source library and API for pre-processing text and PDFs for LLMs.	Series A, $25M (2023) [Crunchbase].	Strong open-source community; broad format support; integration with major AI frameworks.	[Crunchbase]
LlamaParse	API by LlamaIndex specifically for parsing complex PDFs into markdown for RAG.	Part of LlamaIndex (funded entity).	Tight integration with LlamaIndex RAG stack; optimized for retrieval accuracy.	[Competitor data]
Reducto	AI-powered API to extract structured data from any document.	Seed stage.	Focus on turning documents into structured data (CSV, JSON) rather than LLM-ready markdown.	[Competitor data]
Chunkr	API to parse, chunk, and clean documents for AI.	Early stage.	Emphasis on semantic chunking and data cleaning as part of the parsing pipeline.	[Competitor data]

Where Unsiloed claims a defensible edge today is in its technical focus on multimodal vision models for accuracy-sensitive domains like finance and legal. The company's stated wedge is making "unstructured data as smooth as running SQL queries" with "compliance-ready workflows for financial institutions" [Y Combinator]. This edge is currently perishable, however, as it rests on unproven proprietary models and unverified performance claims against named enterprise benchmarks. The Y Combinator affiliation provides a strong distribution signal to early-adopter startups and a network of potential pilot customers, but it does not constitute a durable commercial moat. Talent access through the YC network could accelerate model development, but competing firms are also well-capitalized and recruiting aggressively.

The company is most exposed on two fronts. First, the open-source ecosystem, led by Unstructured.io, offers a free, transparent, and continually improving baseline. For many developers, this sufficient functionality may negate the need for a paid API, especially for non-critical use cases. Second, larger AI infrastructure players,such as cloud providers (AWS Textract, Azure AI Document Intelligence) or data platform companies (Databricks, Snowflake),could extend their existing document services with more LLM-native outputs, leveraging massive scale, existing enterprise trust, and integrated data stacks that Unsiloed cannot match.

The most plausible 18-month scenario is one of sharp segmentation. A winner will emerge if a company can demonstrably lock in a high-value, regulated vertical,like capital markets documentation or clinical trial reports,with proven, auditable accuracy that reduces compliance overhead. A loser in this segment will be a generic API that fails to move beyond early-adopter startups and cannot substantiate its accuracy claims with public benchmarks or named enterprise case studies. For Unsiloed, the path to being the winner requires converting its claims of Fortune 150 bank usage into a documented, referenceable deployment that competitors cannot easily replicate.

Data Accuracy: YELLOW -- Competitor funding and positioning are sourced from Crunchbase and general market knowledge; Unsiloed's differentiator claims are from its YC profile but lack third-party verification.

Opportunity

PUBLIC The prize for Unsiloed AI is ownership of the data ingestion layer for enterprise AI, a role that could command premium pricing and deep integration lock-in if the company can translate its early technical claims into a durable market position.

The headline opportunity is to become the default, compliance-ready infrastructure for parsing complex documents in regulated industries, starting with finance and legal. This outcome is reachable because the company's stated wedge is not just raw accuracy, but workflows built for financial institutions, a domain where data fidelity and audit trails are non-negotiable [Y Combinator]. The claim of processing millions of pages weekly for Fortune 150 banks and NASDAQ companies, while unverified with named accounts, points to an initial beachhead where the cost of error is high and the willingness to pay for reliability is correspondingly elevated [Y Combinator]. If Unsiloed can validate these claims, it positions itself not as another document parser, but as the system of record for turning legacy paper trails into structured AI inputs.

Growth from this beachhead could follow several concrete paths. The scenarios below outline plausible, citation-supported routes to scale.

Scenario	What happens	Catalyst	Why it's plausible
Regulatory Standard in Finance	Unsiloed's API becomes the de facto tool for regulatory reporting and audit document processing.	A public case study or partnership with a major financial regulator or a top-tier audit firm.	The company explicitly targets "compliance-ready workflows for financial institutions" [Y Combinator], and the sector is driven by standardization.
Embedded Layer for Vertical SaaS	The API is white-labeled and embedded into major fintech, legaltech, and healthtech platforms.	An announced integration with a platform like Clio (legal) or Plaid (financial data).	The product is marketed as an API/developer platform [Crunchbase], and vertical SaaS companies seek to add AI capabilities without building core parsing tech.
Acquisition by a Cloud Hyperscaler	A cloud provider (AWS, Google Cloud, Azure) acquires the team and tech to bolster its AI/ML data prep offerings.	Unsiloed demonstrates unique accuracy at scale with a marquee enterprise customer.	Hyperscalers have a history of acquiring niche, technically superior data tooling (e.g., Google acquiring Alooma) to fill portfolio gaps.

Compounding for Unsiloed would likely manifest as a data and distribution flywheel. Each new enterprise customer in a regulated domain contributes complex, edge-case document types that improve the proprietary vision models, widening the accuracy gap against generic open-source tools [Unsiloed AI]. This technical lead, in turn, makes the API more attractive to the next compliance-sensitive buyer, creating a reinforcing cycle. Furthermore, integration into a bank's core reporting workflow generates significant switching costs, as retraining downstream AI models on a new data schema is a prohibitive operational burden. The $550k revenue figure reported for late 2025, if accurate, suggests the initial monetization of this flywheel has begun [getlatka, Nov 2025].

The size of the win can be framed by looking at a public comparable. Unstructured.io, a direct competitor, raised a $40 million Series B in March 2024 at a valuation reportedly over $200 million [Crunchbase]. That valuation was anchored on its role as a critical data preprocessing layer for the AI boom. If Unsiloed executes on its regulated-industry wedge and captures a similar position, but with the pricing power and retention of a compliance-grade tool, a comparable or greater outcome is plausible. In a scenario where it becomes the embedded standard for financial document parsing, the company could be valued as a critical infrastructure asset, a outcome in the hundreds of millions to low billions (scenario, not a forecast).

Data Accuracy: YELLOW -- Core opportunity thesis is built on company-stated positioning and one revenue metric; growth scenarios are plausible but not yet evidenced by public partnerships or customer announcements.

Sources

PUBLIC

1. [Y Combinator] Unsiloed AI: API for parsing multimodal unstructured data | Y Combinator | https://www.ycombinator.com/companies/unsiloed-ai

2. [Unsiloed AI] Unsiloed AI | https://www.unsiloed.ai/

3. [Forbes] Aman Mishra - Forbes Business Council | https://www.forbes.com/councils/forbesbusinesscouncil/people/amanmishra/

4. [Forbes] Adnan Abbas - Forbes Business Council | https://www.forbes.com/councils/forbesbusinesscouncil/people/adnanabbas/

5. [PitchBook] Unsiloed AI 2026 Company Profile: Valuation, Funding & Investors | PitchBook | https://pitchbook.com/profiles/company/820986-04

6. [getlatka, Nov 2025] How Unsiloed AI hit $550K revenue with a 5 person team in 2025. | https://getlatka.com/companies/unsiloed-ai.com

7. [Crunchbase] Unsiloed AI - Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/unsiloed-ai

8. [Crunchbase] Pre Seed Round - Unsiloed AI - Crunchbase Funding Round Profile | https://www.crunchbase.com/funding_round/unsiloed-ai-pre-seed--cd04e083

9. [Grand View Research, 2023] Intelligent Document Processing Market Size, Share & Trends Analysis Report | https://www.grandviewresearch.com/industry-analysis/intelligent-document-processing-market-report

10. [Y Combinator] Founding ML Researcher at Unsiloed AI | Y Combinator | https://www.ycombinator.com/companies/unsiloed-ai/jobs/SJCT4d4-founding-ml-researcher

11. [Y Combinator] Founding Software Engineer - Backend and Infrastructure at Unsiloed AI | Y Combinator | https://www.ycombinator.com/companies/unsiloed-ai/jobs/cmlq27S-founding-software-engineer-backend-and-infrastructure