/Companies

Papr

Techstars '26

Dublin, US · Founded 2024 · 4 employees on LinkedIn · 3 known investors

Find your way into Papr

Sign up to see every warm intro you have to Papr

  • Paths you didn't know you had: your email and LinkedIn already hold routes to the Papr team. We find them for you
  • 2nd- and 3rd-degree connections: the friend-of-a-friend routes that take hours to manually find through your inbox or LinkedIn
  • Answers now, not in days: momentum is everything in a raise. Skip asking around whether someone knows someone

Days of research, done the moment you sign in, with every route ranked by how warm it is.

Papr offers Papr Work, a locally-run desktop application where users discover, customize, and run AI agent-powered apps that automate go-to-market workflows such as prospect research, CRM updates, outbound, meeting prep, and pipeline reporting. It targets sales, marketing, and customer teams looking to consolidate their GTM software stack into one system.

Also known as Papr · Papr Memory · papr.ai

Founders & leadership

Papr was founded in 2024 by Shawkat Kabbara.

SKShawkat Kabbara
Shawkat KabbaraFounder & CEOShawkat Kabbara is founder and CEO of Papr, a desktop application offering AI agent-powered tools for sales and marketing workflows. He has previously led product development and strategy for consumer products used by over a billion users.

Investors · 3

Company profile

researched Sep 2026

Papr provides a memory and retrieval platform that converts unstructured data — documents, conversations and stored memory — into structured intelligence used by AI agents and applications. Its developer platform is organized around several products that can be used standalone or combined: graph-aware vector search with domain-tuned retrieval for areas such as code and science, knowledge graphs that map entity relationships beyond vector similarity, document intelligence that extracts structure and meaning from PDFs, Word documents and images, and chat memory with automatic compression for long-running context. Additional platform concepts documented include predictive memory, a unified graph, structured data handling, and evaluation and comparison tooling.

The platform is exposed through a REST API (the Papr Memory API) with TypeScript and Python SDKs, API-key authentication, sample projects, integration guides and a quick-start flow intended to let developers store and search a first memory in about five minutes. Documented use cases include document question answering, adding graph-aware search to an existing stack by transforming embeddings from providers such as OpenAI or Cohere or reranking existing results, conversational AI with persistent memory, intent-based code search, fraud detection via relationship mapping, recommendation systems, multi-tenant team knowledge management with access controls and namespace isolation, and scientific claim verification.

Papr also markets industry solutions spanning customer experience, enterprise and SaaS, healthcare (including HIPAA-compliant assistants), legal technology, and financial services, positioning the memory layer as the basis for context-aware applications in regulated and knowledge-intensive settings.

Business model

Papr sells a developer platform and API for memory and retrieval, offered in a managed cloud tier that starts free and scales to enterprise, a hybrid cloud option deployed as a managed service inside a customer's AWS, Azure or GCP account, and a self-hosted open-source distribution licensed under AGPL-3.0. Enterprise and custom requirements are handled through a sales motion.

Tiered platform access, with a free entry point on managed cloud scaling to enterprise plans, alongside hybrid-cloud managed deployments and an open-source self-hosted option under AGPL-3.0.

▸Full profile — market position, technology, go-to-market

Market position

Papr positions itself on retrieval quality and deployability: a stated first-place result on Stanford's STaRK benchmark with 91%+ accuracy and sub-150ms cached retrieval, combination of graph structure with vector search rather than vector similarity alone, a no-migration plugin path that works on top of existing embedding providers, and a deployment range from managed cloud to data-sovereign hybrid cloud to AGPL-3.0 self-hosting.

Technology

The system combines graph-aware vector search with knowledge graphs, document intelligence for extracting structure from PDFs, Word files and images, and chat memory with automatic compression for long-running context. Retrieval can be domain-tuned (for example for programming languages and API patterns, or scientific content), and the platform can operate as a plugin layer that transforms embeddings from third-party providers such as OpenAI and Cohere or reranks existing search results without requiring migration. Multi-tenant deployments support access controls and namespace isolation. The company cites a first-place ranking on Stanford's STaRK benchmark with over 91% accuracy and sub-150ms retrieval when cached.

Go-to-market

Distribution is developer-led: self-service account creation and API keys, a five-minute quick start, TypeScript and Python SDKs, published API reference, tutorials, guides, example projects and integrations, plus a Discord community for support. Enterprise and custom needs route to a sales team, and the documentation includes demo requests, case studies and industry-specific tutorials.

Developers and engineering teams building AI agents and context-aware applications, with named industry segments covering customer experience and support, enterprise and SaaS, healthcare, legal technology, and financial services, as well as teams needing multi-tenant knowledge management.

Compiled by commissioned research from 7 cited public sources — announcements, filings, and press listed under research sources below.

Key figures

latest reported
HeadcountAug 20264
Retrieval latency (cached)Jan 2025150 milliseconds
STaRK benchmark accuracyJan 202591%

Company-reported or press-reported figures, each dated to when it was claimed — not independently audited.

Related companies · 4

Companies working in the same space as Papr.

▸Research sources · 7

primary sources listed

7 public sources were cited for this profile; the first-party ones are listed here.

Frequently asked questions

What does Papr do?
Papr turns unstructured documents, conversations and memory into retrievable intelligence for AI agents and applications.
Who founded Papr?
Papr was founded by Shawkat Kabbara in 2024.
Who are Papr's investors?
Papr's investors include Tachles VC, Techstars, The E14 Fund.
Where is Papr headquartered?
Papr is headquartered in Dublin, US.