How Aquant Uses Pinecone to Cut Service Resolution Time 49%
Aquant is an agentic AI platform purpose-built for professionals servicing complex industrial and medical equipment at large manufacturing companies. When the company’s homegrown vector search infrastructure—built on PostgreSQL extensions—began to slow under real-time production demands, Aquant migrated to Pinecone as the retrieval backbone for its AI platform. The switch delivered sub-100ms semantic search, pushed retrieval accuracy above 98%, and helped Aquant’s customers cut average service resolution time by 49%.
Impact
98%+
Retrieval accuracy
48%
Increase in weekly question volume
49%
Reduction in average time-to-resolution
19%
Reduction in cost per service case
62%
Reduction in parts replacement costs
10–20%
Improvement in remote resolution rates
53%
Drop in no-response rate
~43% (24s → 13.7s)
Reduction in full response delivery time
50%
Faster onboarding and knowledge transfer
Challenge
Aquant’s homegrown vector search infrastructure on PostgreSQL extensions delivered inconsistent retrieval quality and slow response times, limiting the platform’s ability to serve real-time service intelligence at enterprise scale.
Solution
Aquant replaced its in-house vector search with Pinecone as the semantic retrieval backbone, enabling sub-100ms latency, tens-of-millions vector indexing with customer-specific namespaces, and advanced metadata filtering that powers its agentic AI workflows.
Tools & Technologies
What Leaders Say
“Pinecone is a critical part of our agentic architecture; it powers the retrieval backbone of Aquant AI, including our knowledge agent, which delivers real-time, context-aware guidance to service professionals. Its performance and scalability allow us to serve our customers in production at enterprise scale, without compromising speed or accuracy. That’s enabled us to move beyond static answers and toward dynamic, AI-driven service intelligence.”
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Full Story
Aquant operates at the intersection of field service, AI, and industrial knowledge management. Its platform serves technicians, call center agents, service leaders, and even end customers at large manufacturing companies—pulling answers from vast repositories of service manuals, repair records, technician notes, schematics, parts catalogs, and call transcripts. The quality of those answers depends entirely on the speed and accuracy of the underlying retrieval system.
Early in its growth, Aquant built its vector search infrastructure in-house on top of PostgreSQL extensions and blob storage. That approach worked for internal tools and offline analytics, but it buckled under real-time production demands. Search was slow, retrieval quality was inconsistent, and maintaining the infrastructure diverted engineering resources away from product development. The team evaluated multiple alternatives—newer vector database entrants and bolt-on database providers—before choosing Pinecone for its enterprise maturity, managed infrastructure, and advanced metadata filtering built specifically for RAG workloads.
Pinecone is now the anchor of Aquant’s agentic retrieval stack. Content across all document types is embedded using Aquant’s own domain-specific models and indexed in Pinecone for fast semantic search. The platform delivers sub-100ms latency, indexes tens of millions of vectors across customer-specific namespaces, and supports rich metadata filtering that tailors responses by asset type, document source, or issue category. With retrieval no longer a bottleneck, Aquant expanded its use of intelligent agents—surfacing next-best actions and generating workflows that adapt dynamically to real-time service conditions.
The performance shift was immediate. Response start time dropped from over 5 seconds to 2.89 seconds; full response delivery fell from roughly 24 seconds to 13.7 seconds. The no-response rate—queries that returned no valid output—fell by 53%. Retrieval accuracy now consistently exceeds 98% in internal benchmarks. Weekly question volume grew 48%, a direct signal that service professionals trust and use the system more.
For Aquant’s customers, the downstream impact is substantial: cost per service case dropped 19%, parts replacement costs fell 62%, remote resolution rates improved 10–20%, and average time-to-resolution decreased 49%. New hires onboard 50% faster. Aquant continues to push the frontier—extending retrieval quality to German, Dutch, and Japanese-language content without custom annotation pipelines, and expanding agentic workflows across a broader set of customer environments.
AWS