SID GTM brain

category

market profile

• text: RAG teams often assemble indexing, keyword and vector search, and reranking components to retrieve context for AI agents.
• text: SID positions SID-1 as a model that iteratively searches and reports better recall and speed than its selected baselines; results are vendor claims.

personas

• title: AI application engineering lead
  pains:

  • Single-pass embedding retrieval misses relevant evidence for complex questions
  • Maintaining search, reranking and query logic diverts engineering time
  • Retrieval latency limits agent responsiveness
• title: Head of applied AI or machine learning
  pains:

  • Generic retrieval benchmarks do not guarantee performance on proprietary documents
  • Needs a defensible quality and latency comparison before replacing a pipeline
• title: Security or data governance lead
  pains:

  • Must assess where sensitive corpora are hosted and whether inputs train models
  • Regulated information may require different processing terms

icp

• text: Teams building AI assistants, agents or search products on private document collections, where complex queries require high retrieval recall; company size is not specified.
• text: Early adopters already measuring RAG quality and latency who can evaluate a model alongside existing search infrastructure.

message pillars

• text: AI needs relevant context to be useful.
• text: Search is necessary to supply that context.
• text: SID emphasizes retrieval quality and speed.

objections

• objection: Will the benchmark gains hold for our data?
  response: Evaluate recall and latency on representative questions and labeled documents against your existing stack; homepage performance figures are SID's claims.
• objection: Can we use sensitive data?
  response: Review SID's terms and data-processing agreement: they describe a non-training commitment and restrictions on certain data categories.
• objection: What will it cost and when can we get access?
  response: Request current access and an order form or dashboard rates; the terms allow usage fees and minimums but no public starting price was verified.

competitors

• name: ZeroEntropy
  why: Developer-focused retrieval API identified alongside SID by TechCrunch.
• name: Voyage AI
  why: Retrieval model provider named in the same competitive field.
• name: Pinecone
  why: Managed vector retrieval alternative for teams building their own search pipelines.
• name: Elastic
  why: Search platform offering a broader alternative for hybrid retrieval.

influencers

• name: Max Rumpf
  url: https://www.linkedin.com/in/maximiliandavid
  why: Co-founder who shared SID-1's launch and retrieval benchmarks.
• name: Lotte Seifert
  url: https://www.linkedin.com/posts/activity-7478209854977957889-Qvkp
  why: Co-founder who discussed training search models with reinforcement learning.

keywords

• agentic retrieval model
• RAG retrieval accuracy
• AI search API
• retrieval augmented generation latency
• embedding search alternative
• agentic search for enterprise data
• document retrieval benchmark

voice

• technical and research-led
• ambitious and declarative
• concise

signals

• text: An AI team is benchmarking retrieval recall or replacing an embedding-only RAG pipeline.
• text: A product team is building agents over complex internal documents and has identified search quality as a bottleneck.

stack

• Microsoft Azure
• Baseten
• API

hiring

• Research Engineer
• Training Infrastructure Engineer

pricing

motion

channels

• research publications
• X
• LinkedIn
• waitlist
• developer events

confidence

Founders

• name: Maximilian-David Rumpf
  title: Founder
  role: Founder
  bio: Max is the CEO of SID.ai. Previously, he was a researcher at ETH Zürich, where he worked on AI accelerator design with the professor who developed Google's TPU architecture. Max was awarded a German national excellence scholarship, awarded to 0.5% of German students.
• name: Lotte Seifert
  title: Founder
  role: Founder
  bio: Lotte is determined to let people use their data for their own good. She holds a degree in CS from ETH Zürich and a degree in Economics from the University of St. Gallen.