Unsiloed AI GTM brain

category

market profile

• text: AI application teams need structured, reliable context from PDFs, scans and spreadsheets for retrieval and agent workflows.
• text: The company targets both self-service prototypes and regulated, high-volume production deployments.

personas

• title: Head of AI or ML engineering
  pains:

  • Document pipelines stall when layout changes break OCR and post-processing.
  • Nested tables and charts corrupt the context supplied to agents.
• title: AI application or RAG engineer
  pains:

  • Stitching parsers, OCR and chunking delays deployment.
  • Needs structured output and consistent extraction across customer document formats.
• title: Enterprise security and data platform lead
  pains:

  • Sensitive documents require retention, residency and deployment controls.
  • Production workloads need contractual support and SLAs.

icp

• text: Enterprise banks, mortgage servicers and other document-intensive financial businesses with sensitive files and complex tables.
• text: AI startups and software teams building production RAG or voice-agent products across multiple customer document formats.
• text: High-volume or regulated enterprises requiring dedicated deployment and data controls are a fit for custom plans.

message pillars

• text: Turn complex files into agent-ready structured formats.
• text: Maintain document structure through vision-based parsing.
• text: Use a single API from prototype to production.

objections

• objection: Our existing OCR works well enough.
  response: Run a sample of nested tables and changing layouts through both systems and compare downstream retrieval accuracy.
• objection: We cannot send sensitive documents to a shared service.
  response: Evaluate the stated zero retention, residency and private deployment options against your security requirements.
• objection: Page volume could make costs unpredictable.
  response: Test representative documents using the free allowance, then request volume pricing and model expected page counts.

competitors

• name: Reducto
  why: Document parsing and extraction for AI applications.
• name: LlamaParse
  why: Parsing complex documents into formats for LLM pipelines.
• name: Amazon Textract
  why: Managed OCR and document extraction alternative.
• name: Unstructured
  why: Transforms unstructured documents for AI pipelines.

influencers

• name: Aman Mishra
  url: https://www.linkedin.com/feed/update/urn%3Ali%3Aactivity%3A7370692080702177280/
  why: Co-founder announcing the company's YC batch and sharing its progress.

keywords

• document parsing API
• PDF to markdown API
• RAG document extraction
• table extraction OCR
• multimodal document AI
• enterprise document processing

voice

• technical and direct
• outcome-focused
• benchmark-conscious

signals

• text: Hiring AI engineers for a RAG or document automation pipeline suggests a need for reliable parsing.
• text: A regulated document workflow moving from pilot to high-volume production creates demand for controls and deployment options.

stack

• REST API
• JSON
• Markdown

hiring

• Founding Software Engineer - Backend and Infrastructure
• Founding ML Researcher

pricing

motion

channels

• SEO
• LinkedIn
• developer documentation
• self-serve playground

confidence

Founders

• name: Aman Mishra
  title: Founder/CEO
  role: CEO
  bio: Co-founder at Unsiloed AI • IIT Kharagpur Previously built an ultra low-latency trading system moving billions at a hedge fund. Founding Engineer (#1) at an SF-based startup building AI copilots for firms like Goldman Sachs and Charles Schwab. Launched a P2P rental platform from my dorm room, scaling it to thousands of orders within 2 months of operation.
• name: Adnan Abbas
  title: Founder/CTO
  role: CTO
  bio: Co-founder & CTO at Unsiloed AI • MIT • IIT Kharagpur Built multi-modal models deployed at a Fortune 10 company. Was building autonomous navigation systems at Mercedes Benz. Launched India’s first Web 3.0 audio app while in college, scaling it to thousands of users within a month.