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Bring the capability in,
Data stays in place

Nexora AI does one thing: it deploys AI capability into the R&D intranet of automotive and advanced manufacturing, so that documents, knowledge, and tests truly flow without ever leaving the domain.

3 Product lines
0 External RAG dependencies
100% Data never leaves the domain

The Name

The name Nexora

Nex comes from nexus — the connection point. What matters most in R&D is never a single document, but the connections between documents, between requirements and test cases, and between this change and the last defect.

ora traces back to the Latin mark of "time" — we want these connections retrievable at the very moment you need them: no digging, no asking around, no waiting for a meeting.

So the three products are one chain, not three tools. NexSpec Hub governs documents, Nex Knowledge Base makes knowledge retrievable, and NexTest Studio turns knowledge into assets. Each works on its own, but only together do they form a complete connection.

Why On-Premise

Why we insist on private deployment

Public-cloud AI is already very usable. The problem is that in automotive R&D, most specifications, signal definitions, diagnostic specs, and defect records require approval even to circulate across departments.

Because the data can't leave

Sending R&D data to an external service in exchange for AI capability is the first red line for many teams. If that line can't move, bring the capability in instead.

Because answers must be verifiable

Engineers won't sign off just because "AI says so". Every conclusion must trace back to the source text, with sources and scores visible.

Because the chain must leave a trace

Versions, changes, reviews, audits — in the automotive industry these records aren't a bonus, they're part of the deliverable.

Architecture

Technical approach: a self-built thin retrieval layer

We do not wrap an external RAG platform. The chunking pipeline, embedding calls, hybrid retrieval, reranking, and source assembly are all built in-house — about six core modules in total.

Chunking Structure-aware chunking. Clause numbers, section headings, and table rows all inform boundary decisions — no cutting by character count.
Embedding An embedding endpoint on the intranet, with 1024-dimensional normalized vectors. The query side gets an Instruct prefix; the document side doesn't.
Hybrid Retrieval Dual-path retrieval: dense vectors + a full-text inverted index (Chinese bigram, English word-level, a BM25 variant).
Fusion RRF fusion, k=60. It looks only at rank, not score scale, so it adapts to heterogeneous retrieval sources.
Rerank A reranker refines candidate chunks; on failure it degrades to pure hybrid retrieval, marking the state without blocking search.
Provenance When generating an answer it concatenates sources, labels source numbers and relevance scores, and lets you click back to the original text.
File OFFICE / PDF Structure-aware chunking Clauses · sections · table rows Dense-vector recall 1024-dim · L2-normalized Full-text inverted recall Chinese bigram · English word-level RRF fusion k = 60 Reranker refinement Candidate chunk reranking Generation + sources Numbers & relevance scores Intranet boundary · zero external requests end to end

Figure 8 · Nex Knowledge Base's self-built retrieval pipeline; all six steps stay on-premises

The same trade-offs apply to the test pipeline. All ten NexTest Studio agents run on the intranet, and parsing and generation produce no external requests; the way models connect and the choice of endpoint always stay in your hands.

Product Lines

Three product lines, one chain

Source · govern documents

NexSpec Hub

Specification and document master data hub

The single source of truth for specification master data, source files, and version history. Source files can't be overwritten, SHA-256 validation, and a draft-to-release status flow.

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Know · make knowledge usable

Nex Knowledge Base

On-premises Knowledge search and Q&A

Hybrid retrieval + RRF + refinement, all completed on the intranet. Answers carry sources and scores and link back to the original text.

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Test · turn it into an asset

NexTest Studio

AI test closed-loop platform for automotive software

Specification parsing, atomic requirement extraction, test specification generation, end-to-end traceability, and change impact analysis.

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Principles

Five principles of what we don't do

The most expensive decision in a product is often what not to build.

  • Never overwrites source files. Revisions are appended as new versions, and old versions are kept forever — because the version from back then is often the one you need when assigning responsibility.
  • No physical-deletion API. Archiving is only a status change; the file and all of its versions are kept together.
  • Never lets AI output land unchecked. Any auto-generated content must support human confirmation, editing, rejection, and version records.
  • Never lets retrieval break under degraded mode. When reranking or embedding fails it falls back instead of erroring, marking only the state.
  • Never hides confidence. Sources and relevance scores are shown outright, letting people judge for themselves rather than deciding for them whether to trust it.
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