As the use of vector databases surged in mainstream engineering conversations, most discussions focused narrowly on semantic search and retrieval augmented generation. These early applications made embeddings accessible, but they also created the misconception that vector databases begin and end with search. Today, technology is evolving far more rapidly than the public narrative suggests.
In this expert contribution, Pranav Prabhakar, a recognised leader in applied machine learning and scalable systems, offers a broader lens shaped by years of leading ML initiatives across fintech, edtech, and AI-first product companies. As Senior Machine Learning Engineer at ManyPets, Pranav has consistently driven high-impact engineering transformations, mentoring teams, designing ML-driven workflows, and deploying production systems used at scale. His work demonstrates not only technical depth but an ability to turn emerging technologies into measurable business value, a key reason he is frequently invited to share insights with engineering communities.
Here, Pranav examines how vector databases, when used intentionally, unlock far more sophisticated and business-critical applications: deduplication of large-scale customer datasets, entity resolution in complex transactional environments, anomaly detection for fraud and operational risk, and more. These patterns, drawn from real-world deployments, highlight how ML engineers and technical leaders can extract strategic value beyond the traditional boundaries of search.
This article serves as a practical guide for organisations seeking to elevate their ML infrastructure maturity and for engineers looking to build high-impact, production-grade vector workflows.
From Search to Something Bigger
Vector databases really operate as similarity engines. They respond tremendously well to “what else is similar to this?” by projecting data as points in high-dimensional space and quantifying
relationships with distance measurements. This naturally fits search use cases, but it creates a giant blind spot.
The default developer workflow is a reliable one: start with embeddings from OpenAI or Hugging Face, experiment with semantic search over docs, maybe build a chatbot. These applications will work and provide quick payback, validating the premise that vector databases are primarily search infrastructure.
But the same similarity computation that powers semantic search can be used to address fundamental data quality problems that afflict all current applications. By shifting our thinking from “finding relevant information” to “identifying patterns and relationships within data,” vector databases are not only a search tool, but an enterprise data intelligence solution.
The breakthrough is realizing that similarity is much more about relevance than text. Two customer records may be similar because they are the same person with subtleties. Two patterns of transactions may be similar in patterns that signal fraud. All of them tap the same math that semantic search employs, but to respond to very different business queries.
Tackling Duplication at Scale
Duplicate data is one of the oldest issues in modern data management. Older approaches are largely founded on exact matching or simple heuristics, and they exclude the nuances of similarity that characterize true duplicate data. Vector databases tackle deduplication as a similarity problem.
Think about product names deduplication in online store catalogs. There is a seller who lists “Silver iPhone 17 Pro Max 256GB” and another seller who lists “Apple iPhone 17 Pro Max – 256 GB Storage – Silver Color.” Vector embeddings can represent semantic similarity that extends beyond such surface differences.
The algorithm produces product description embeddings and then uses distance calculations to identify sets of similar products. Systems automatically flag prospective duplicates for review or merge them based on confidence levels. The approach handles millions of products and scales to new categories without having to be updated by manual rules.
The business effect extends beyond cost savings in storage. Cleaner data pipelines reduce computational overhead, improve recommendation engines, and provide a superior user experience by eliminating duplicate content.
Smarter Entity Resolution
Entity resolution involves systems deciding when multiple different records point to the same actual entity. Conventional methods depend on matching fields exactly or rule-based fuzzy matching, but are not able to handle real-world variability and inconsistency.
Vector databases learn representations that capture inherent properties across diverse sources of data. Instead of comparing individual fields in each other’s isolation, the system considers the overall pattern of attributes. This global approach is particularly powerful with incomplete or inconsistent data.
Customer database consolidation exemplifies this capability. Companies store customer information in several different systems that have differing standards of quality. Conventional methods have difficulty with differences such as “John A. Smith” vs. “J. Smith” or addresses in varying forms.
Vector representations embed customer profiles highlighting significant similarities and suppressing insignificant differences. The system could possibly identify that customers who share the same email domain, have similar buying patterns, and have common address components most likely cover the same person despite having various names on record.
Detecting the Unusual
Anomaly detection is perhaps the most sophisticated application, leveraging vector databases’ ability to project typical patterns of behavior and identify outliers. It is effective because anomalies often manifest themselves in relationships and patterns between observations, not individual points of data.
Financial fraud detection showcases this power. Traditional rule-based systems flag transactions based on predetermined criteria like amounts or locations. Sophisticated fraud schemes often operate within these boundaries while exhibiting subtle behavioral patterns distinguishing them from legitimate activity.
Vector representations encode transaction patterns, user behavior histories, and contextual factors into high-dimensional spaces where normal behavior clusters together while anomalous activity appears as outliers. The system learns typical behavior for different user segments and identifies transactions that deviate from established patterns.
Network security is another compelling example. Network traffic exhibits complex patterns of normal business traffic, but the occurrence of bad intentions introduces subtle anomalies. Vector embeddings capture temporal and structural aspects of network flows that identify threatening behavior that might elude traditional signature-based approaches.
Operational Realities
Building these systems is the simple part, keeping them efficient in production is another matter. Most vector-based pipelines share a pattern of candidate generation, then re-ranking or combination with structured filters to get an accurate result. Engineering for this two-stage process ensures efficiency and accuracy at scale.
In practice, such design choices don’t come up that often on their own. Choices like recall vs latency, or re-indexing frequency vs compute cost, are best handled when the whole team understands the principles at play. At Irin AI, for instance, some of my biggest contributions were in explaining these issues in a form that junior engineers could consider them and even propose alternative solutions — creating system bottlenecks as learning opportunities for the team.
Evaluation too is difficult. Annotated datasets are typically scarce in enterprise settings, so teams have to work with proxy measures like cluster coherence, temporal retrieval consistency, or human-in-the-loop validation. These lightweight checks allow measuring whether embeddings are behaving as desired without requiring fully annotated corpora.
Operational vectors are active. Embedding models move when language, behavior, or product catalogs change, so re-indexing is fundamental to the life cycle. That is expensive and creates latency trade-offs: larger indexes improve recall but increase compute overhead, and constant re-indexing keeps models current but strains resources. Successful teams balance those forces
with hybrid solutions, reserving dense retrieval for “tough” cases and applying more rapid symbolic rules for the humdrum majority.
Beyond Search
Vector databases are actually transforming how we think about data intelligence in business systems. While search applications initially drove adoption, most revolutionary use cases come from envisioning vector databases as general-purpose similarity machines that can solve a wide range of operational issues.
The mathematical computation enabling semantic search also powers sophisticated deduplication systems that maintain data quality at scale, entity resolution systems that match heterogeneous data sources, and anomaly detection systems that identify uncommon patterns within complicated operational data. These applications deliver measurable business value in the form of better data quality, operational efficiency, and risk reduction.
The key insight for development teams is that vector databases represent a new tier of intelligence in enterprise data stacks capable of solving much more than information retrieval. Teams that are willing to think more boldly will position themselves better to unleash the full potential of vector databases in solving real business problems.
