LeaseAccounting.app
LeaseAccounting.app is a self-serve IFRS 16 and FRS 102 lease accounting platform built for finance teams who want audit-ready compliance without spreadsheets, implementation consultants, or six-figure setup costs. Made by ZenTreasury Oy in Helsinki, Finland with EU-only data hosting. Designed for SMEs reporting under IFRS 16 or FRS 102 (UK GAAP), typically managing 5 to 50 leases. The platform generates complete lease schedules, journal entries, modifications, remeasurements, terminations, and one-click audit evidence packs from any lease contract. AI-assisted contract extraction reads your PDFs and proposes lease terms with confidence scoring; you approve, and the deterministic calculation engine produces the numbers. Same inputs, same outputs, every time. Zen AI is advisory only and never touches a calculation. Other features: Discount Rate Advisor pulls reference rates from central bank sources and drafts a rate memo for review; continuous compliance monitoring flags indexations due, expiring leases, and overdue reassessments; multi-entity bookkeeping from day one; auditor portal access with activity logging (coming soon); journal export to SAP, Oracle, Dynamics, and NetSuite formats; Azure AD / Entra ID SSO with JIT provisioning. Pricing: free tier covers 2 leases with no credit card required. Paid plans start at €149 per month with no per-seat pricing and generous team access included on every tier. Differentiation: built IFRS-first (not ASC 842-first), EU-hosted, fully implemented FRS 102, and self-serve onboarding. The trusted alternative to spreadsheet-based compliance and consultant-heavy enterprise lease tools.
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RaimaDB
RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.
RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.
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IndexedDB
IndexedDB serves as a fundamental API designed for the client-side storage of large volumes of structured data, including files and blobs. It utilizes indexing to facilitate efficient searches, making it suitable for extensive datasets. While traditional web storage can handle smaller data quantities well, it falls short when it comes to managing larger structured datasets, a gap that IndexedDB effectively fills. Functioning as a transactional database system akin to SQL-based Relational Database Management Systems (RDBMS), IndexedDB diverges from them by operating as a JavaScript-based object-oriented database. This distinction allows it to store and retrieve objects indexed by keys, with support for any objects that comply with the structured clone algorithm. Users must outline the database schema, establish a connection, and execute retrieval and updating of data through a series of transactions. Additionally, like other web storage solutions, IndexedDB adheres to the same-origin policy, ensuring data security and integrity across different domains. With its versatility and capability, IndexedDB has become an essential tool for developers dealing with complex data needs on the web.
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Zilliz Cloud
Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements.
Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more.
Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.
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