The defense sector is known for its complexity and opacity. For both government agencies shaping requirements and private companies seeking to contribute solutions, understanding how to engage—who to collaborate with, what capabilities are needed, and where opportunities exist—makes navigating this landscape a monumental task. Government data is often siloed, inconsistently formatted, and buried across a multitude of sources, making it difficult to identify the relationships and insights that truly matter.

Obviant was built to solve this problem. As a unified defense market intelligence platform, Obviant aggregates and synthesizes structured and unstructured data, from budget lines and contract awards to organizational charts and program histories, into intuitive dashboards with actionable insights. But delivering that knowledge at speed and scale required a retrieval engine capable of understanding more than just keywords. It needed to understand meaning, nuance, and connection.

From the beginning, Obviant's core mission was to close the gap between information and action. To do that, the team needed to surface highly relevant content and recommendations from vast datasets that included PDFs, webpages, presentations, government reports, and more. Traditional keyword-based search powered by document stores simply wasn’t enough.

These systems could match terms, but they couldn’t understand context. Important insights, such as the relationship between two government programs or the relevance of a past contract to a new funding line, were often buried too deep to retrieve without significant manual effort.

Obviant’s team began exploring semantic search and retrieval-augmented generation (RAG) approaches. But early experiments with other vector databases exposed additional challenges including:

• Scalability issues: Many struggled with efficient scaling to tens of millions of vectors, leading to latency spikes and bottlenecks as data or query loads increased.
• Indexing and query performance: Rigid or inefficient indexing strategies resulted in degraded search performance and high operational costs at scale.
• Infrastructure reliability: Weak fault tolerance and limited high-availability features made some solutions unsuitable for production environments.
• Database consistency: Inconsistent data updates and weak concurrency guarantees often led to stale or unreliable results, especially under hybrid workloads.
• Documentation and support: Incomplete or outdated documentation and slow support responses hindered deployment and troubleshooting.

To meet the expectations of users operating in mission-critical environments, Obviant needed a retrieval solution that could combine the precision of sparse retrieval with the semantic understanding of dense embeddings. It also had to be performant at scale, flexible, and reliable enough to sit at the heart of a product serving defense decision-makers every day.

Obviant turned to Pinecone after a thorough prototyping effort that evaluated scalability, developer experience, and performance under real-world load. What began as an exploration into dense vector search quickly evolved into a full-scale implementation of Pinecone’s hybrid retrieval architecture, combining sparse and dense indexes to deliver a deeper level of relevance and insight.

Obviant’s success with Pinecone was grounded in specific product requirements that other vector solutions failed to meet. Their criteria included:

• Ability to easily scale to hundreds of millions of vectors or more
• Hybrid search support (dense + sparse)
• Flexibility with vector lengths, types, and retrieval algorithms
• Metadata filtering
• TypeScript SDK support
• Competitive cost at scale

The first integration point came in Obviant’s data pipeline. The team built a wrapper around Pinecone’s TypeScript SDK to streamline ingestion and updates, allowing them to efficiently process and store vast quantities of vectorized content. Today, that system supports over 120 million vectors distributed across dozens of namespaces, each tuned to different aspects of the defense acquisition landscape.

As the team dug deeper into Pinecone’s capabilities, they began layering on more advanced retrieval techniques. Cascading retrieval pipelines were introduced, starting with the use of the pinecone-sparse-english-v0 sparse embedding model for converting text to sparse vectors for hybrid semantic/keyword search through Pinecone Inference. Hybrid search results then served as a filter for downstream reranking models. This sophisticated retrieval method of dense and sparse vectors became the default for recommendations, allowing the system to precisely understand not only what users were asking but what they were really trying to find.

Pinecone’s reliability, flexibility, and support for both high-performance read and write operations made it easy for every engineer at Obviant to interact with the system. By abstracting their interaction with Pinecone behind an internal client, they ensured consistency across their entire application stack.

Pinecone gave us the flexibility and performance we needed to move from basic search to something much more knowledgeable—retrieval that understands context, adapts to our data, and scales with our growth. — Max Tano and Harrison Linowes, Founding Engineers at Obviant

Pinecone gave Obviant the infrastructure to scale retrieval without friction. What previously required significant manual intervention, such as surfacing related government programs or connecting adjacent contract opportunities, could now be done automatically, in real time. With hybrid sparse-dense retrieval and a cascading architecture in place, Obviant saw a 30% increase in the relevance of recommended content, making it easier for users to find what they needed faster and with greater confidence.

The improvements were also felt internally. Developers gained a consistent interface to work with Pinecone through a custom SDK wrapper, streamlining updates and ingestion pipelines. Operational headaches around indexing, latency, and scaling effectively disappeared. Pinecone easily worked, even under production pressure.

Our goal has always been to get decision-makers closer to the signal. Pinecone enables that by helping us cut through the noise, fast. — Dylan Taylor, Co-Founder at Obviant

What improved under the hood:

• More than 120 million sparse and dense vectors indexed across dozens of namespaces
• <50ms P50 latency with 40 QPS in production
• High availability and fault tolerance across large-scale ingestion and retrieval workloads
• Streamlined developer operations through an internal Pinecone SDK integration

By powering an experience that provides deep knowledge and delivering consistent performance at scale, Pinecone has become an essential part of Obviant’s infrastructure and product strategy.

What's next

Building on the success of hybrid sparse-dense retrieval, Obviant is now focused on expanding its capabilities to deliver even richer, more actionable insights to defense decision-makers. Upcoming initiatives include integrating real-time feedback loops to continuously refine recommendation accuracy, incorporating additional data sources to broaden market coverage, and exploring deeper AI-driven analytics powered by Pinecone’s evolving infrastructure.

As Obviant’s platform scales to handle increasingly complex datasets and higher query volumes, Pinecone’s robust performance and scalability will remain central to supporting rapid innovation and mission-critical reliability. Together, they aim to push the boundaries of what unified defense intelligence platforms can deliver—making critical insights faster, more knowledgeable, and more accessible to users navigating one of the world’s most challenging information landscapes.