SAIQL is a semantic query language and database engine purpose-built for AI systems. Deterministic retrieval. Agent memory. Semantic search. One stack, zero guesswork.
Vector search gives you "close enough most of the time." That got cargo-culted into critical systems without questioning the assumptions. ATLAS is a deterministic retrieval engine. Same query, same results, every time. Not approximate. Not probabilistic. Exact.
RAG retrieves approximate nearest neighbors from a vector store using neural embeddings -- learned weights, probabilistic similarity scores, results that shift between runs. There is no contract between what goes in and what comes back.
ATLAS is a BTI engine -- BM25 + TF-IDF. No embeddings. No vector similarity. No partial matches. Exact deterministic retrieval where the same query returns the same results, every time, on every machine. Content-addressed chunks, locked fusion weights, lexicographic tie-breaking. Nothing learned. Nothing approximate.
RAG is probabilistic search with a language model stapled on. BTI is deterministic retrieval with formal reproducibility guarantees. They are not the same category of system.
Three fused retrieval lanes -- metadata filtering, BM25 lexical scoring, and TF-IDF term weighting -- combined with locked fusion weights, content-addressable chunk IDs (SHA-256), and lexicographic tie-breaking. No exceptions. No drift.
Fixed fusion weights across all retrieval lanes. No runtime drift. Retrieval behavior is a configuration artifact, not an emergent property.
Every retrieval decision is traceable. Content-addressable chunk IDs prove exactly what was returned and why.
Same query. Same results. Every time. Lexicographic tie-breaking eliminates the last source of non-determinism.
Every answer traces to a specific source chunk. No hallucination from model memory. Grounded in data that exists.
Six purpose-built components. Each solves a problem that existing tools handle poorly or not at all for AI workloads.
Probabilistic index engine. 10x+ faster lookups than B-tree for AI query patterns. No external database required.
LSM-tree agent storage with event streams and deterministic clock. Agent memory that survives sessions, migrations, and restarts.
Three-mode semantic compression. Parametric pointer compression, structural data compression (2.6x), and temporal context compression.
Pre-query, pre-retrieval, post-output guards. Blocks prompt injection and unsafe output. Fails closed.
Three-tier RBAC with tenant isolation at the storage layer. Profile-based credentials -- never inline, never logged.
Translates SAIQL queries across any database dialect via intermediate representation. Bridges AI to legacy systems.
Schema introspection, type-safe conversion, proof-first validation, and immutable artifact bundles. 8+ database engines supported.
Extract complete source schema
Type-safe IR conversion
Target DDL, dry-run first
PK-ordered, checkpointed
Checksums & parity
Compliance-grade artifact
SAIQL is not a demo. It powers live products today.
Adversarial code security platform. 108 hostile AI agents scan for vulnerabilities across security, architecture, compliance, and performance. SAIQL handles retrieval, context, and agent memory.
shipitclean.com →Multi-agent AI platform demonstrating LoreCore persistent memory, LoreToken compression, and agent coordination at production scale.
agentsplex.com →Three preprints on Zenodo with permanent DOIs. The math is published. We are not asking you to trust us.
Available to select partners and investors. Contact Apollo Raines to discuss access.