Service 07

Search by meaning,
not just keywords.

State-of-the-art embedding models and vector search that turn your manufacturing data into a semantic intelligence layer. Find similar jobs, surface tribal knowledge, and power RAG pipelines — all from the meaning in your data. The infrastructure that gives American manufacturers a genuine AI advantage.

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Vector embeddings blueprint — 3D vector space with neural network and semantic search
Capabilities

Your data understands
itself.

We integrate leading embedding models and vector databases so your manufacturing systems can search, match, and reason over unstructured data at scale.

01
Multi-Provider Embeddings
Gemini Embedding 2, Voyage AI, OpenAI, and open-source models — all behind a unified interface. Switch providers with a config change, no code rewrite. Automatic fallback if a provider goes down.
02
Semantic Knowledge Search
Ask questions in plain English and find setup notes, machine tips, and process decisions by meaning. Vector similarity search across your entire knowledge base, powered by pgvector.
03
Similar Job Matching
When a new RFQ arrives, vector search finds the most similar historical jobs — matching on material, tolerances, geometry, and process — so estimators price with data, not guesswork.
04
RAG Pipeline
Retrieval-augmented generation pulls the most relevant knowledge entries into the AI Copilot's context window. Every answer is grounded in your shop's real data, not generic training data.
05
Hybrid Search
Combine vector similarity with PostgreSQL full-text search and structured tag filters. Semantic understanding meets precision filtering — maximum recall without the noise.
06
Domain Benchmarking
Generic MTEB scores don't tell the whole story. We benchmark embedding models against your actual manufacturing data — RFQs, setup notes, part descriptions — to find the best fit for your domain.
Technical Details

Embedding & vector
architecture.

The AI infrastructure that converts unstructured manufacturing text into searchable, comparable, and retrievable vectors at production scale.

Embedding Models
Gemini 2 + Voyage + OpenAI + OSS
Primary: Google Gemini Embedding 2 (#1 MTEB, 3072 dims, multimodal). Fallback: local sentence-transformers for zero-latency on-device embedding. Configurable per-tenant.
Vector Database
pgvector on PostgreSQL
ACID-compliant vector storage in the same database as application data. IVFFlat and HNSW indexing for sub-100ms approximate nearest neighbor queries at million-vector scale.
Search Architecture
Hybrid: vector + BM25 + filters
Cosine similarity over pgvector embeddings, combined with PostgreSQL full-text search and structured metadata filters (machine, material, process, part family).
Dimensions & Performance
3,072 dims / sub-100ms p95
Gemini Embedding 2 default dimensionality with Matryoshka reduction available for cost/speed trade-offs. Async embedding via Celery workers for non-blocking ingestion.
Upgrade Path
pgvector → Qdrant / TurboPuffer
Current architecture scales to millions of vectors. If requirements grow to billions, the abstraction layer supports migration to dedicated vector databases without application code changes.
Modules Powered
Knowledge + Quoting + Copilot
Semantic search in Knowledge Fabric, similar job matching in Intelligent Quoting, and retrieval-augmented generation in the AI Copilot — all share the same embedding infrastructure.
Intelligence for American manufacturing

Make your data
searchable by meaning.

State-of-the-art embedding models, deployed on your infrastructure, turning your manufacturing data into a semantic intelligence layer. Built for the shops that build America.

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