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Pinecone

Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications Multi-domain RAG in n8n: why one knowledge base is not enough Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone | Pinecone Building RAG workflows in n8n: choosing the right Pinecone node Knowledge needs a meta-knowledge layer Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale When "Performance" Means Two Different Things Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access True, Relevant, and Wrong: The Applicability Problem in RAG Use the Pinecone Plugin for Claude Code to develop AI Applications Faster Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone | Pinecone Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge RAG with Access Control Pinecone Dedicated Read Nodes are now in Public Preview Inside Pinecone: Slab Architecture New Bulk Data Operations: Update, Delete, and Fetch by Metadata The Hidden Cost of Building: Lessons from Aquant Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities Pinecone joins Microsoft Marketplace as a Launch Partner GTM Engineering: Clay + Pinecone for AI-powered Sales Outbound Build an AI knowledge assistant with Google Docs and Pinecone Moving Pinecone forward with Ash Ashutosh as CEO and Edo spearheading our growing AI ambitions as Chief Scientist Pinecone Founder Edo Liberty to Spearhead Pinecone’s Growing AI Ambitions; Appoints Ash Ashutosh as CEO to Expand Vector Database Market Leadership Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone | Pinecone Announcing Pinecone Pioneers: A Program for Builders, Organizers, and Community Leaders What is Context Engineering? Chunking Strategies for LLM Applications Beyond the hype: Why RAG remains essential for modern AI Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone | Pinecone Build more knowledgeable AI applications with new LLMs and greater control in Pinecone Assistant #NYTECHWEEK 2025 Retrieval-Augmented Generation (RAG) Accurate and Efficient Metadata Filtering in Pinecone’s Serverless Vector Database | Pinecone Terminal X AI Agents, Powered by Pinecone, Turn Complex Financial Data Into Production-grade Insights at Scale | Pinecone Aquant Delivers Scalable, Expert-level Service Intelligence with Pinecone | Pinecone Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness Vector databases aren't just for large-scale enterprise AI Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval | Pinecone Fast and Effective Early Termination for Simple Ranking Functions | Pinecone Domain-specific AI Agents at Scale: CustomGPT.ai Serves 10,000+ Customers with Pinecone | Pinecone Using Pinecone asynchronously with FastAPI A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings | Pinecone Build secure, scalable agentic AI workflows with Rubrik Annapurna and Pinecone Tool up: Pinecone’s first MCP servers are here Add context to your agent with Pinecone Assistant MCP remote server E2Rank: Efficient and Effective Layer-wise Reranking | Pinecone ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring | Pinecone Efficient Constant-Space Multi-Vector Retrieval | Pinecone How Vanguard Worked with Pinecone to Boost Customer Support with Faster Calls and 12% More Accurate Responses | Pinecone Pinecone Named to Fast Company's Annual List of the World's Most Innovative Companies of 2025 Launch Week: Pinecone for agents, search, recommendations, and more Optimizing Pinecone for agents (and more) Retrieval Inference for scale and performance How 1up Turns Sales Reps Into Product Experts with Pinecone | Pinecone Don’t be dense: Launching sparse indexes in Pinecone Unlock High-Precision Keyword Search with pinecone-sparse-english-v0 Evolving Pinecone's architecture to meet the demands of Knowledgeable AI Pinpoint references faster with citation highlights in Pinecone Assistant Bringing the leading vector database to your cloud Getting started with llama-text-embed-v2 Natural Language Counterfactual Explanations for Graphs Using Large Language Models | Pinecone Easily build knowledgeable chat and agent-based applications in minutes with Pinecone Assistant, now generally available How to build an agentic, chat or RAG knowledge system using Pinecone Assistant Real-time RAG with Pinecone and Estuary Flow BigQuery to Pinecone in Real-Time with Estuary Flow Stravito Turns Market and Consumer Data Into Actionable Insights with Pinecone Inference | Pinecone Accelerate prototyping and development with Pinecone Local First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Strengthening security and increasing control with CMEK and API key roles Introducing Pinecone Rerank V0 Introducing cascading retrieval: Unifying dense and sparse with reranking From Idea to Action: How Pinecone Assistant Meaningfully Accelerates AI Business Building AI apps on Azure with Pinecone just got a lot easier Building a reliable, curated, and accurate RAG system with Cleanlab and Pinecone Four features of the Assistant API you aren't using - but should Deploying Pinecone with Infrastructure as Code (IaC) Streamlining CI/CD with Pinecone Local September 2024 Product Update Results of the Big ANN: NeurIPS'23 competition | Pinecone Introducing import from object storage for more efficient data transfer to Pinecone serverless Simplify, enhance, and evaluate RAG development with Pinecone Assistant, now in public preview Vectors and Graphs: Better Together August 2024 Product Update Pinecone Helps Deep Talk Deliver World-Class AI Assistants with Lower Engineering Overhead | Pinecone Assembled Delivers Better, Faster AI- Driven Support with Pinecone | Pinecone Llama 3.1 Agent using LangGraph and Ollama Build knowledgeable AI with Pinecone serverless, now generally available on Microsoft Azure Pinecone serverless is now generally available on Google Cloud, adding knowledge to AI assistants and other applications Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI Bridging Dense and Sparse Maximum Inner Product Search | Pinecone Refine Retrieval Quality with Pinecone Rerank Introducing reranking to Pinecone Inference to simplify building accurate AI July 2024 Product Update Connect to Pinecone within your platform to enable a seamless AI development experience Introducing Pinecone API Versioning RAG Brag with Inkeep Co-Founder Nick Gomez LangGraph and Research Agents Introducing Pinecone Inference to streamline your AI workflow
Chipper Cash Thwarts Fraudsters in Real-time with Pinecone | Pinecone
2023-04-12 · via Pinecone

Chipper Cash is a financial technology company on a mission to unlock barriers to banking in Africa. Providing a frictionless way to send and receive money cross-border and offering financial inclusivity, Chipper Cash has revolutionized money transfers for more than five million customers in Africa and beyond.

While the user base is growing quickly, Chipper Cash is constantly looking for ways to acquire new users and drive more transactions. One way they do this is through paid promotions for new users. For example, “Buy stocks worth at least $2 before the end of the week. Get a ₦500 reward!”

While these promos attract many new users, they also incentivize people looking for loopholes to create multiple accounts and maliciously access the promos. The company knew they needed a faster, more reliable way to verify new users and block duplicate users in real-time to prevent fraudulent sign-ups.

Challenge

Catching and preventing fraudsters in the act

Chipper Cash used a third-party service to provide security checks and verify identities of new users. Not only did these checks help identify duplicate accounts, but they also checked for fake or stolen documents (e.g. government-issued IDs) as part of their KYC checks . This solution worked well at first, but as the volume of users grew across the app’s seven supported countries, the verification process became a bottleneck — especially during promos.

This led to an uptick in malicious users creating multiple accounts and “gaming” the system. Incentivized by the new-user promos, these users would set up many fake accounts, resulting in delays of up to 20 minutes to validate their sign-ups. This not only created a poor experience for legitimate new users, but it also gave the fraudsters time to redeem new-user promos before they were caught.

During a six month period, Chipper Cash lost 16% of their promo budget to fraudulent sign-ups — whether duplicates or users with fake or stolen government IDs.​​ They also had to stop many promos earlier than planned, losing out on the opportunity to acquire more users.

Solution

Detecting duplicate sign-ups in real-time with Pinecone

Samee Zahid, Director of Engineering at Chipper Cash, took the lead in building an alternative, AI-based solution for faster in-app identity verification. The team wanted a solution with minimal latency at scale and lower overall costs. Their key idea was to use visual search to check if the selfie of a new users closely matches the selfie of any existing user. They learned they could accomplish this with an AI model (for turning images into embeddings) and a vector database (for storing and searching through embeddings by similarity).

The engineering team built a proof-of-concept using an open-source database with basic vector search functionality, and a ConvNet model for turning selfie photos into embeddings. Once the database was loaded with embeddings of existing user selfies, they could embed the selfie of each new user and search the database for similar embeddings. A high similarity score between the new selfie and an existing selfie would indicate the selfies are of the same person, meaning a duplicate account.

While the concept worked remarkably well, it became clear that making this work at low latencies and at their scale would require incredibly tedious and time-consuming management of the database. Zahid decided to look for a managed alternative that could provide performance at scale with minimal overhead. The search started and ended with Pinecone.

We have a high bar in terms of security and latency for our users. Many third-party solutions don’t meet our requirements, so we typically opt to build or host in-house. Pinecone proved to deliver so much value — with reduced overhead and ultra-low latencies at scale — we didn’t need to do much convincing to move forward.

Supported by Pinecone, Chipper Cash’s new identity verification solution is:

  • Built for real-time: With Pinecone, Chipper Cash was able to drastically reduce the amount of time needed to verify new sign-ups, especially during peak periods. This allows them to retrieve similar selfies of a user in under 200 milliseconds so they can catch and prevent fraudsters without slowing down legitimate new users.
  • Optimizing spend: With real-time verification, there are 10x fewer duplicate sign-up attempts. This means more money is going towards acquiring legitimate new customers — the intended audience — instead of fraudsters and bots. Chipper Cash can now allocate budget spend in a more effective way to help them meet their mission.
  • Highly scalable: Supporting billions of vectors at a time, Pinecone provides Chipper Cash with the long-term memory needed to continue scaling and adding context (i.e. selfies) to their dataset. The more context the application has, the faster and more accurate is the verification process.

Zahid and his team were able to fully implement Pinecone into their stack within a matter of weeks. Using Snowflake as a data warehouse, they generate embeddings using their Facial Similarity Service (FSS), and then store them in Pinecone. From there, FSS queries Pinecone to return the top three matches before querying the Chipper Backend to return match likelihood along with any other helpful metadata. See the full architectural diagram below for reference:

Zahid summarized the overall process of implementing Pinecone:

It took us less than a month to build our new in-house facial verification system. We have a real-time, scalable, and secure system thanks to Pinecone. End-to-end latencies for the entire system dropped from up to 20 minutes previously to less than 2 seconds now, with Pinecone doing the search in under 200ms.

Since launching the facial verification system with Pinecone, Chipper Cash has seen the number of fraudulent sign-ups decrease by 10x. The below graph shows the results for their user base in Uganda, which is representative of Chipper Cash’s other markets.

result

Catching more fraudulent sign ups, preventing losses

Chipper Cash has seen dramatic improvements in their identify verification workflow since launching Pinecone into production:

  • New users’ selfies are verified with over 95% accuracy in less than 2 seconds.
  • Promo budget gets spent efficiently to acquire legitimate new customers.
  • Fraudulent sign-ups have dropped by 10x across all markets.

Chipper Cash now provides a better user experience by enabling legitimate users to sign up and redeem promos quickly. And with better screening of fraudulent sign-ups, they can run longer, more effective promotions. Motivated by this success, Zahid and his team are now exploring other ways to make meaningful business impact with AI and the Pinecone vector database.