Software tools that work offline.
On any hardware.
Without the cloud.
Oren Stack builds retrieval and AI tools that run on Python's standard library. No GPU. No API keys. No dependencies. Just fast, accurate, private computation.
Sift — Deterministic Retrieval
What embedding-based search gets wrong
Embeddings compress meaning into vectors. That compression loses information. On a benchmark of 175 real-world queries, embedding-based retrieval scored 25%. Hybrid retrieval (embeddings + BM25) scored 33%. Neither is production-ready.
Sift takes a different approach: deterministic text signatures instead of vector similarity. It matches structural patterns, not approximate meanings. The result is either the right answer or nothing. No "sort of related" noise.
| Metric | Sift | Embeddings | Hybrid |
|---|---|---|---|
| Accuracy (175 queries) | 100% | 25% | 33% |
| Avg response time | 15ms | ~200ms | ~350ms |
| Dependencies | 0 | 5–20+ | 10–30+ |
| GPU required | No | Usually | Usually |
| Works offline | Yes | Rarely | Rarely |
| RAM (904K pairs) | ~272MB | 2–8GB | 3–10GB |
Integration & Consulting
Sift for Your Data
You have the data — support tickets, medical records, legal documents, knowledge bases. I build custom domain signatures, tune the engine, and deliver a working Sift instance for your specific use case.
Embedded Integration
Sift running inside your product. I handle architecture, integration, and ongoing maintenance. Your users get instant, accurate retrieval. You get a monthly retainer and zero infrastructure headaches.
Offline AI Architecture
Building a system that works without internet? Edge devices, field applications, privacy-sensitive environments. I design local-first AI pipelines. Sift is the proof I know how.
Built by someone who tried embeddings first
Sift is the result of that work. Now I help companies integrate fast, private, offline retrieval into their products.
Need retrieval that actually works?
Whether you're building offline applications, privacy-sensitive systems, or just tired of vector databases that return wrong answers — let's talk.
Get in Touch →Software tools that work offline.
On any hardware.
Without the cloud.
Oren Stack builds retrieval and AI tools that run on Python's standard library. No GPU. No API keys. No dependencies. Just fast, accurate, private computation.
Sift — Deterministic Retrieval
What embedding-based search gets wrong
Embeddings compress meaning into vectors. That compression loses information. On a benchmark of 175 real-world queries, embedding-based retrieval scored 25%. Hybrid retrieval (embeddings + BM25) scored 33%. Neither is production-ready.
Sift takes a different approach: deterministic text signatures instead of vector similarity. It matches structural patterns, not approximate meanings. The result is either the right answer or nothing. No "sort of related" noise.
| Metric | Sift | Embeddings | Hybrid |
|---|---|---|---|
| Accuracy (175 queries) | 100% | 25% | 33% |
| Avg response time | 15ms | ~200ms | ~350ms |
| Dependencies | 0 | 5–20+ | 10–30+ |
| GPU required | No | Usually | Usually |
| Works offline | Yes | Rarely | Rarely |
| RAM (904K pairs) | ~272MB | 2–8GB | 3–10GB |
Integration & Consulting
Sift for Your Data
You have the data — support tickets, medical records, legal documents, knowledge bases. I build custom domain signatures, tune the engine, and deliver a working Sift instance for your specific use case.
Embedded Integration
Sift running inside your product. I handle architecture, integration, and ongoing maintenance. Your users get instant, accurate retrieval. You get a monthly retainer and zero infrastructure headaches.
Offline AI Architecture
Building a system that works without internet? Edge devices, field applications, privacy-sensitive environments. I design local-first AI pipelines. Sift is the proof I know how.
Built by someone who tried embeddings first
Sift is the result of that work. Now I help companies integrate fast, private, offline retrieval into their products.
Need retrieval that actually works?
Whether you're building offline applications, privacy-sensitive systems, or just tired of vector databases that return wrong answers — let's talk.
Get in Touch →