1. Too Many Joins
The core database for writes was SQL Server. When users executed interactive queries filtering requirements based on various properties and statuses, the system experienced severe latency.
Architectural Simplicity and 60% Faster Speed: Adept Concept’s Shift from Parallel Infrastructure to RavenDB
Case Study: Adept Concept’s Journey with RavenDB’s Built-in AI
Adept Concept helps organizations manage compliance through an AI-driven platform that digitizes standards, links them to internal procedures, and automatically evaluates coverage and gaps. As their AI engine grew in scope - processing vast regulatory changes and customer documentation - they needed a faster, more dependable system to support next-generation AI capabilities.
Arild Havnen CTO, Adept Concept
The Challenge: Architectural Complexity and Performance Bottlenecks
Adept’s development team encountered significant architectural challenges that impacted both application performance and development overhead.
Problem
The relational structure required “too many joins” for interactive filtering, demanding the processing of “too much data” for common queries.
Bottleneck
Slow, non-snappy filtering that highlighted the limitations of the relational model for these specific workloads.
2. Too Many Mechanics
Adept maintained a custom Retrieval-Augmented Generation (RAG) pipeline to power its AI engine. This stack, built in Python, utilized Docker containers, APIs, and bolted on tools for vector storage.
Pain Points
This parallel infrastructure introduced “too many mechanics” and “too many integration points,” creating “too many points of failure.”
Data Sensitivity
Managing this separate vector database complicated the governance of sensitive customer data, requiring extra infrastructure to avoid sending information to an external system.
The Solution:
Eliminating the Parallel Infrastructure
Adept strategically adopted RavenDB in two phases, leading to an architectural pivot that ultimately phased out the legacy system in favor of consolidation.
Superior Indexing and Querying
RavenDB was first introduced as a read layer on top of SQL Server. By denormalizing the data, Adept immediately gained the snappy filtering, indexes, and querying capabilities required for their interactive workloads. This performance difference was stark:
The filtering contrast was significant. With RavenDB, “more filters = faster performance (less to return),” the opposite of the strain seen in the relational model.
The success of the read layer established the foundation for integrating the AI stack. Adept recognized the advantage of eliminating the extra infrastructure and moved their entire RAG pipeline into RavenDB.
The Integrated AI Stack Today
AI FunctionPrevious SystemCurrent System
Vector Search & ChunksCustom PythonRavenDB (stores and manages chunks)
RAG PipelineDockerized API LayerRavenDB (runs semantic search embeddings, AI tasks)
Data SecurityMultiple Complex Integration pointsRavenDB (data stays secure within the existing environment)
Impact and Outcomes:
Velocity, Performance, and Feature Enhancement
The consolidation delivered tangible, measurable results across development and product delivery.
Performance and Pipeline Speed
Speed Gain:
60% faster retrieval and embedding performance after consolidating the entire RAG pipeline into RavenDB.
Development Velocity:
The team was able to prototype semantic search in “a few minutes” and replace the entire custom RAG stack in “just a few days.”
Architecture and Operations
Simplified Stack: The primary operational value was achieving a simplified, cleaner architecture by eliminating the parallel infrastructure. With “Most of our AI lives in Raven now,” the system is easier to debug and maintain, reducing technical overhead.
Product Capability
RavenDB’s native vector search capability enabled the implementation of genuine Semantic Search, providing a superior user experience beyond literal keyword matching:
Example:
A search query for “life-saving equipment” accurately returns documents referencing related concepts such as life vests, life rafts, and mustering stations, improving the accuracy of compliance validation.
Forward-Looking: Autonomous AI Agents
Adept continues to evolve its platform by leveraging RavenDB for powerful agent orchestration. They plan to build autonomous agents that can trigger on new documents, evaluate the relevance of new regulations, and suggest improvements in the background, further embedding RavenDB’s AI features into the core compliance workflow.
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