A high-throughput deterministic inverter for unstructured research corpora, historical archives, contracts, and scans — bitemporally anchored, cryptographically auditable, and free of LLM guesswork.
No slow, expensive LLM guessing — hard, deterministic engineering built for peer-reviewed reproducibility.
An asynchronous high-throughput endpoint accepts direct batch uploads, encrypted .kira-pkg containers, or listens continuously to S3 / MinIO buckets.
True structural understanding instead of naive text chunking — proven on 20+ million Wikipedia documents in under 3 days.
Connect Python scripts, R packages, or econometric models directly via clean HTTP API — unthrottled in the research setup.
Provenance, bitemporal snapshots, scientific controversy modeling, corpus throughput, and programmatic R / Python integration.
Every document fed into the pipeline receives an immutable cryptographic signature (SHA-256 / BLAKE3) and is archived in the Raw Vault. Every API response references these primary sources via stable UUIDs. Every single data point at any stage of your research project can be reconstructed deterministically and traced back to its exact primary sentence.
KIRA uses a strict bitemporal data structure separating Valid Time (when a fact held true in reality) from Transaction Time (when it was recorded in the system). This eliminates classic distortions like Look-Ahead Bias. Researchers can query exact bitemporal snapshots — reconstructing the precise state of knowledge available on a specific historical cutoff date (e.g. March 1, 2020), even if records were amended years later.
No — because our architecture models multidimensional evidence rather than binary dogma. Edges and claims carry explicit evidence links, source reputation weights, and epistemic uncertainty metrics (Belief-Scores). When a new study challenges an established consensus, prior claims are never silently overwritten; KIRA surfaces the full structure of the scientific controversy along with every underlying chain of evidence.
Our global knowledge database was initially populated with the complete Wikipedia dumps across the top 10 languages: full ingestion and end-to-end processing of 20 million documents took less than 3 days.
You connect your Python scripts, R packages, or quantitative models directly to a clean HTTP REST API returning structured payloads (JSON subgraphs and streaming NDJSON) instead of unstructured prose. Intentionally pure API access (zero proprietary SDK lock-in), millisecond response latencies, and zero rate limits in the academic research setup. And if your study requires a specific function or feature, contact us — we gladly implement it for you.
Tell us briefly about your chair/institute, your research corpus, and your available server hardware — we will get in touch personally.