Interfaze builds an AI model on a new architecture that merges specialized DNN/CNN models with LLMs for developer tasks requiring deterministic output and high consistency. Target use cases include OCR, web scraping, classification, and speech-to-text. The model is aimed at developers who need reliable, repeatable results rather than the variability of general-purpose LLMs. The company is based in San Francisco and is part of YC's Spring 2026 batch.
Interfaze
ActiveAI model built for deterministic developer tasks
Total raised
$500K
1 round
Stage
Seed
Jan 2026
Team
1-10
since 2026
Pricing
—
Founded
2026
San Francisco, United States
Agent-ready
—
AI model architecture merging specialized DNN/CNN models with LLMs
Built for deterministic, high-consistency developer tasks
OCR for extracting text from documents and images
Web scraping and structured extraction
Classification tasks
Speech-to-text transcription
Repeatable outputs aimed at reducing LLM variability
Developer-focused integration for production pipelines
12/100
Early
MCP server
Public API
Webhooks
OAuth 2.0
SDKs
No public agent surfaces detected yet.
Jan 2026 Seed $500K ● Y Combinator
Capital network
$500K raised ·1 backer·10 network links
- Backers1
- Shared portfoliocompanies these backers also fund
- Extended networkfunds that co-invest alongside them
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- What is Interfaze?
- Interfaze is an AI model built for deterministic developer tasks like OCR, web scraping, classification, and speech-to-text, merging specialized DNN/CNN models with LLMs for consistent output.
- Why use Interfaze instead of a general-purpose LLM?
- It is designed for tasks that need reliable, repeatable results, reducing the output variability common with general-purpose LLMs.
- What tasks does it support?
- It targets OCR, web scraping, classification, and speech-to-text, among other deterministic developer tasks.
- Who is Interfaze for?
- It is aimed at developers building production systems who need consistent, deterministic outputs rather than variable responses.
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