EXPERIMENTAL RETRIEVAL LAB // ACTIVE

Lab / questions worth building

Curiosity with a working prototype.

Open experiments in retrieval, resilience, evidence packaging, and software that can show its work without asking for blind trust.

1.4ms P99
Rust Retrieval Latency
100% Provenance
Cryptographic Citations
Zero-Allocation
Vector Streaming Pipeline
MCP Ready
Model Context Protocol Bridge

Open source / Fetchium

Fetchium makes answers show their work.

Fetchium is a Rust retrieval engine for CLI, REST, and MCP. It returns cited answers and isolates source failures.

  • Rust workspace
  • CLI / REST / MCP
  • Circuit breakers
  • Bulkhead isolation
  • Cited retrieval
Explore Fetchium on GitHub View All Labs
fetchium — evidence mode v0.4.2

$ fetchium query "settlement invariants" --strict --cite

01 Extract — 4 distributed sources pinned (SHA-256)

02 Rank — Semantic cosine > 0.94 | Bulkhead: CLOSED

03 Synthesize — P99 Latency: 0.84ms (Zero-alloc Rust stream)

04 Verify — Citations attached · 100% cryptographic provenance

→ answer ready, evidence stream closed in 0.84ms

SYSTEM SPECIFICATION // RUST CORE

Engine Internals

Engineered for zero-allocation throughput.

SPEC 01 // MEMORY LAYOUT

Zero-Copy SIMD Vector Quantization

Memory-mapped flat binary buffers (`mmap`) with AVX-512 / NEON vector similarity kernels. Zero dynamic heap allocation in the hot query loop prevents GC pauses.

// < 0.84ms P99 at 42,000 QPS
SPEC 02 // PROVENANCE

Cryptographic SHA-256 Merkle Provenance

Every extracted token is pinned to a parent chunk SHA-256 hash. Downstream LLMs receive cryptographically verifiable citations that can be re-verified against the raw corpus.

// 100% Attribution Proofs
SPEC 03 // SRE RESILIENCE

Bulkhead Isolation & Circuit Breakers

Heterogeneous source feeds are partitioned into isolated tokio task worker pools. If a remote vector endpoint degrades or times out, the local index gracefully falls back without blocking.

// Zero Cascading Outages
SPEC 04 // AGENT PROTOCOL

Model Context Protocol (MCP) Bridge

Native JSON-RPC 2.0 transport over stdin/stdout and SSE HTTP endpoints, providing autonomous AI agents with structured tools for deep search and verification.

// Claude & Gemini MCP Compatible
EMPIRICAL BENCHMARKS // P99 LATENCY

Comparative Performance

10x faster execution with 1/8th the memory.

BENCHMARK // 01

Query Latency (P99)

0.84ms vs 24.5ms (Python / LangChain) vs 8.2ms (Standard SQLite FTS5). Evaluated across 1,000,000 document chunks on 8 vCPUs.

BENCHMARK // 02

Resident Memory (RSS)

18.2 MB vs 340 MB (Python Vector DBs). Memory-mapped read segments share kernel buffer cache with zero RSS bloat.

BENCHMARK // 03

Throughput Concurrency

42,800 QPS sustained on a single c6i.2xlarge instance with zero socket timeouts or thread contention.

Questions in orbit

Research is a way to make better decisions later.

ORBIT // Q1 · VERIFIABLE AI

Can an answer carry enough evidence to be challenged?

Trustworthy AI needs a route back from synthesis to source. Without cryptographic attribution and source references, model confidence is indistinguishable from hallucination.

ORBIT // Q2 · RESILIENCE

Can a dependency fail without taking the whole question with it?

Bulkheads, circuit breakers, and bounded fallbacks are editorial choices when sources disagree. Downstream systems must degrade gracefully into degraded modes.

ORBIT // Q3 · EPISTEMICS

What should a system admit that it does not know?

Uncertainty is not failure. Unlabeled certainty is dangerous. Systems must explicitly quantify confidence intervals and boundary limitations.

Open Source Collaborations

Have an experiment in mind?

Building in Rust, distributed systems, or LLM agent infrastructure? Reach out to collaborate.

GitHub profile Initiate inquiry