Introducing Aspects Database Semantic Index 2.0
A new architecture that combines semantic text and structured metadata into one query. Search your structural metadata and unstructured content in a single, unified vector space.
AspectedResults generated from a production dataset of ~240,000 anonymized tickets from an enterprise-grade issue tracking system. Comparison reflects standard vector retrieval vs. Aspected retrieval pipelines using the same GPT-4o baseline. Skeptical? to verify the raw logs.
The Silent Failure of the "Single Vector" Architecture
Stop trying to fix a math problem with better prompt engineering.
You didn't become a Senior Architect to write string concatenation scripts. Yet, for the last two years, the industry has forced you into the "Concatenation Hack".
To get your vector database to respect basic business logic—like dates, user IDs, or legal jurisdictions—you are forced to glue structured metadata directly into your text chunks before embedding them. You paste "Date: 2024" or "Category: Legal" into the context window and hope for the best.
This is the math of Metadata Dilution.
When you compress 5 tokens of metadata (constraints) and 500 tokens of content (meaning) into a single array of numbers, the semantic weight of the content overwhelms the constraints. The signal gets "drowned out.".
The Alternative is just as bad: The "Filtering Trap."
If you don't concatenate, you rely on pre-filtering or post-filtering. Both fail at scale. Pre-filtering creates empty search spaces, killing your recall. Post-filtering searches the whole haystack but misses the needle because the "Top K" cutoff excluded the right document before the filter could apply.
The result is a Silent Failure. The database doesn't crash; it simply returns the wrong document with a high confidence score.
It retrieves a "Refund Policy" from 2019 instead of 2026 because the semantic similarity was high, but the "Date" constraint was ignored.
It's not a hallucination. It's a retrieval failure caused by a "Muddy Vector."
Your Metadata Isn't a Garnish. It's the Guardrail.
We don't just filter your vectors. We reconstruct them.
The industry standard forces you to choose between the "Muddy Vector" (Concatenation) or the "Leaky Filter" (Multi-stage queries). We believe this is a false dichotomy.
A specific date (2025-01-01) is a rigid constraint.
A topic ("liability") is a fuzzy concept.
Gluing them together muddies the signal. Filtering them separately breaks the context. Both approaches destroy utility.
Aspected abandons the "Single Vector" paradigm entirely. Instead of compressing your data into a "Blob," we utilize our patented Aspect-Based Embeddings. This architecture physically separates your data into two distinct channels:
These aspects are then mathematically combined into a Unified Search Embedding.
The Result
One Query. Zero Friction.
You get the ability to perform fuzzy search over both metadata and content in a single query, delivering the deterministic precision of a database with the fluid understanding of an LLM.
Three Modes. One Deterministic Engine.
Whether you are building a chatbot, a legal discovery tool, or a recommendation engine, Aspected exposes its architecture through three distinct interfaces.
Prompt-Based Search
Stop parsing dates with regex. Pass a raw natural language string, and Aspected handles the rest. Our engine automatically parses the query, routes constraints (like "from Q3 2024") to their specific Aspect embeddings.
Best For
Chatbots, AI Agents, NL Interfaces.
Reference Document Search
Find "More like this", but strictly. Provide a "Golden Record" to find its nearest neighbors. Aspected uses the document's full aspect profile to find matches that align on both semantic meaning and metadata constraints.
Best For
Legal Precedent, Case Law, Recommendations.
Embedding-Based Search
Raw power. No leaks. For advanced pipelines, interact directly with the vectors. Perform traditional similarity searches enhanced with Aspect guardrails. You get the raw speed of vector search without the "leaky filters."
Best For
Custom RAG Pipelines, Hybrid Replacements.
Burn Your Concatenation Scripts.
Ingestion shouldn't require string manipulation. Stop being a "Data Janitor" writing fragile glue code.
Why "Hybrid Search" is a Broken Band-Aid.
The industry wants you to fix retrieval failures by tuning alpha parameters (balancing Keyword vs. Vector scores). This is not engineering; it's guessing. Stop fighting the Matrix of Failure.
Constraint Enforcement
Leaky Filters
Pre-filtering creates empty search spaces (low recall). Post-filtering searches the haystack but misses the needle because the 'Top K' cutoff excluded the right doc.
Aspect-Based Retrieval
We don't filter; we route. A 'Time Aspect' is a hard architectural boundary. If the metadata doesn't match, the vector is mathematically invisible. Zero leaks.
Data Synchronization
The Sync Nightmare
Decoupled architecture forces you to re-embed massive text blobs just to change a 'Status' field. You live in a state of permanent 'eventual consistency'.
Atomic Aspect Updates
Update the 'Status Aspect' instantly without touching the heavy Content Vector. Your search index stays in 1:1 sync with your Source of Truth.
Explainability
The Black Box
Score: 0.89. Why? Who knows. You cannot debug a cosine similarity score. You are feeding data into a void.
Deterministic Transparency
Matched: Content (0.92) + Jurisdiction (Exact). The engine tells you exactly which Aspect triggered the retrieval. Finally, you can debug your RAG pipeline.
Performance at Scale
Timeout / OOM
pgvector hits a wall at 10M vectors. Weaviate throws Deadline Exceeded errors on complex sorts.
Optimized Density
By separating aspects, we avoid the massive scan-and-filter penalties that cause timeouts. Precision doesn't cost latency.
Tuning Required
Tuning Hell
Constantly adjusting alpha parameters (0.7 vector? 0.3 keyword?) and re-ranking models to fix hallucinations.
Zero Tuning
Structure is built-in. You define the Schema, we enforce the geometry. No magic numbers required.
Ready to stop fighting your database?
Join the engineers migrating from "Magic" to "Structure." Choose your initialization sequence below.
"By January 2026, the 'AI Revolution' is no longer about magic. It's about reliability."