Problem and Solution Report

Enterprise back offices contend with high volumes of heterogeneous documents—KYC packs, invoices, claims, shipping forms, contracts, receipts, and loan files—many of which are scanned, handwritten, or of variable quality. Traditional OCR/template approaches often struggle with generalization, leading to manual rework, compliance risk, and long cycle times. The problem is acute in regulated verticals like BFSI and healthcare, where accuracy, auditability, and data protection requirements are stringent.

EigenPal addresses this by providing an end‑to‑end platform for document workflows: AI OCR that claims 99 % accuracy on difficult inputs (handwritten/scanned/damaged) without requiring templates; understanding, extraction, and validation steps with guardrails; and a workflow builder to create, test, and deploy processes with guaranteed accuracy targets. The platform emphasizes pre‑deployment evaluations on historical data, enabling teams to launch only when accuracy is proven—reducing production risk and improving trust in automated decisions.

Beyond extraction, EigenPal provides full observability into accuracy, costs, and compliance, with integrations into enterprise monitoring stacks (e.g., Grafana, Datadog, ELK). Security and compliance are highlighted with SOC 2 Type II, GDPR, and CCPA certifications, and on‑prem readiness for customers with strict data residency or control requirements. The solution spans key use cases across finance (bank statements, loan verification, KYC), insurance (claims, policy extraction, risk assessment), manufacturing (invoices, purchase orders, shipping docs), and healthcare (patient records, claims, HIPAA‑aligned workflows). This architecture and focus aim to deliver production‑ready outcomes with human‑like performance and a measurable path to quality and ROI.

Market and Competitors Report

EigenPal competes within Intelligent Document Processing and broader Document AI. The competitive landscape includes: hyperscaler document AI services (Google Document AI, AWS Textract, Microsoft Azure Document Intelligence), IDP specialists (Rossum, Indico Data, Hyperscience, ABBYY, Tungsten/Kofax), and automation platforms that embed IDP (UiPath, Appian, IBM, OpenText). Industry sources and buyer review platforms list many of these vendors as top alternatives or leaders in the space. These tools vary in focus—from generalized APIs to verticalized IDP and from RPA‑integrated suites to AI‑native platforms.

Key trends shaping competition include increased use of LLMs and domain‑specialized models to improve accuracy across unstructured formats, with some vendors emphasizing proprietary LLMs. Analysts and market reports highlight players like ABBYY, UiPath, and IBM among notable IDP providers, while buyer‑review ecosystems show alternatives considered by Document AI users, including Rossum, AWS Textract, Microsoft Azure Document Intelligence, ABBYY FlexiCapture, and Tungsten TotalAgility.

EigenPal’s competitive angle centers on being an end‑to‑end, enterprise‑grade platform that combines high‑accuracy AI OCR with guardrails, evaluation/QA workflows, observability, and on‑prem deployment—features important for regulated, large‑scale environments. The emphasis on pre‑deployment evaluations, guaranteed accuracy targets, and full monitoring may differentiate it from point solutions or less opinionated toolkits. Its security posture (SOC 2 Type II, GDPR/CCPA) and on‑prem readiness further address enterprise adoption barriers. The company’s YC backing and founders’ backgrounds in AI and large‑scale systems engineering underscore its focus on production readiness and enterprise reliability.

Founders and Background Report

EigenPal is co‑founded by Jedrzej Blaszyk and Matej Novak. According to Y Combinator, EigenPal was founded in 2025, is based in San Francisco, and is part of the Winter 2026 batch. The company focuses on automating enterprise document workflows such as KYC documents, invoices, claims, shipping forms, contracts, receipts, and loan documents, with options for on‑premises deployment or cloud.

Jedrzej Blaszyk is listed by YC as a co‑founder of EigenPal and previously an engineer at Elastic and Yelp, with a Computer Science background from Imperial College London. His personal site provides additional detail: he completed an MEng in Computing with an AI specialization at Imperial College London (2016‑2020), and he has worked on building an enterprise‑grade document understanding platform. He also maintains QueryBox, a platform for website AI search and chat, indicating hands‑on experience in applied AI/ML and developer‑facing tooling.

Matej Novak is listed by YC as a co‑founder, with education at MIT and Imperial College London, and as a “3‑time founder of B2B AI companies.” Public sources associate him with Assetario, where he served as CEO and co‑founder; interviews and podcasts further corroborate his leadership and AI product experience, including machine‑learning applications for predictive personalization and revenue optimization. These experiences suggest a strong background in building and commercializing AI products in enterprise settings.

Overall, the founders combine deep technical experience in large‑scale software (Elastic/Yelp), advanced AI/ML education (Imperial, MIT), and entrepreneurial track records in B2B AI (Assetario and other ventures). This profile aligns well with EigenPal’s enterprise‑grade document AI ambitions, which demand both cutting‑edge model capabilities and robust production engineering.