No external APIH
✳ RETRIEVAL INTELLIGENCE / 01

From documents
to defensible evidence.

Search a collection, inspect exactly where each result comes from, and compare transparent ranking methods. No invented answers. Just attributable evidence.

BM25TF-IDFRECIPROCAL RANK FUSION
DOCUMENTS INDEXED18Live local corpus
LABELLED QUERIES21Ground-truth tests
RETRIEVAL MODES03Deterministic engines
DATA LOCATIONLOCALBrowser-only analysis
01 / EXPLORE

Evidence search

Start with a question. Review source-level evidence before drawing conclusions.

EXPLAINABLE RANKING

Ranked evidence 0 results

Document passages with traceable IDs and lexical relevance.

DETERMINISTIC OUTPUT
ⓘ

Evidence is not an answer. Rankings measure term relevance, not factual truth or entailment. Check the complete original document and any conflicting sources before making a claim.

02 / QUANTIFY

Evaluation Lab.

Measure what the retrieval engine actually finds. Compare algorithms against explicit relevance labels, with per-query diagnostics and inspectable methodology.

Using demo relevance labels
BENCHMARK OVERVIEW

Algorithm comparison

21 LABELLED QUESTIONS

Retrieval quality by method

Higher is better. Macro averages from the same question set.

MRR@k nDCG@k

Query-level diagnostics

Identify failed retrievals and inspect expected versus returned documents.

SOURCE-LABELLED
QUESTIONLANGEXPECTEDFIRST HITRECALL@KSTATUS
ⓘ

Benchmark transparency: this demonstration uses synthetic documents with hand-authored relevance labels. These numbers are not claims about performance on real datasets. The system does not perform cross-language semantic retrieval.

03 / PREPARE

Corpus Workspace.

Bring your own documents and labelled questions into a private, client-side research environment. Nothing is sent to a backend by this application.

▤

Import documents

Accepts JSON, CSV, TXT and Markdown. CSV should contain id,title,body headers.

Max 2 MB per file · Up to 250 documents
▦

Import relevance labels

Upload JSON questions, each with query and relevant_ids for a defensible benchmark.

Up to 200 labelled queries · IDs must match corpus
DATASET INTELLIGENCE

Collection quality

SYNTHETIC DEMO

Languages

Categories

Integrity checks

Quality controls for incomplete or inconsistent evaluation inputs.

Indexed documents

Document IDs, provenance fields and corpus coverage.

04 / VERIFY

Audit & Export.

Turn an exploratory search into a repeatable evidence record. Record methods, parameters, ranked passages, known limitations and quality metrics.

REPRODUCIBLE ANALYSIS

Your research. An inspectable trail.

Exports are generated locally, immediately, as JSON files you can inspect, archive or reproduce.

{R}
⇩

Retrieval report

Query, method, ranked document identifiers, score, matching terms and verbatim evidence excerpts.

⇩

Full evaluation report

All ranking methods, corpus integrity profile, labeled questions, metrics, per-question retrieval and caveats.

Research methodology

01

Index and normalizeUnicode tokens with accent folding and stopword filtering. Titles receive 2× weight.

02

Retrieve and rankBM25 k1=1.5 / b=0.75, cosine TF-IDF or reciprocal rank fusion k=60.

03

EvaluatePrecision@k, Recall@k, reciprocal first-relevant rank and binary nDCG@k.

04

Examine evidencePassages are exact excerpts of the corpus; relevance does not imply factual support.