Stop searching your literature.
Ask it questions.
H4Graph turns thousands of papers into a knowledge graph your team can interrogate — and answers with citations, including the connections you didn’t know were there.
Hybrid 4-layer answering
Vector-only RAG answers what sounds similar. Real research questions need structure, ground truth, and judgment. Each layer covers the others' blind spot.
Vector
Finds passages by meaning, not keywords. Your question matches evidence phrased in entirely different vocabulary.
Graph
Traverses citations, methods, datasets, and concepts — multi-hop. Finds the contradiction published two years later that shares no words with your question.
Relational
Grounds every claim: exact source text, DOIs, dates, and licence rights. Turns candidate evidence into evidence you may legally show — and precisely cite.
Agent
Plans which registered traversals to run, notices gaps, re-queries, and synthesises a structured answer — every statement wired to its source.
Any single layer fails a real research question. H4Graph is the claim that four together don’t — compressed into seven characters.
The question every “chat with your PDFs” tool gets wrong
“Which methods outperform transformers on long documents — and has anyone contradicted those results?”
Vector-only RAG1 HOP
- ✕Finds chunks that sound like your question — and stops there
- ✕The contradicting 2025 paper shares no vocabulary → invisible
- ✕Answers the first half, silently misses the second
- ✕Citations generated, not guaranteed
H4GraphMULTI-HOP
- ✓Vector finds the entry points by meaning
- ✓Graph follows CITES → EVALUATED_ON → CONTRADICTS, three hops out
- ✓Relational layer pins every claim to source text, DOI and date
- ✓Agent notices the gap, re-queries, answers both halves — cited
- ✓Ambiguous entity? It clarifies rather than guesses
“Cited” isn’t a prompt. It’s the architecture.
Most tools ask their model to cite sources and hope it complies. H4Graph's answering layer is built so it cannot emit an untraceable value.
Registered traversals only
The agent never writes queries. It plans over a catalog of read-only, depth-limited query templates — each one profiled, index-backed, and tested like code. No injection surface, no runaway traversals.
Per-field lineage
Every value in an answer records the template, bound parameters, graph nodes, and source passage that produced it — written to an append-only store the application cannot rewrite.
Reproducible on demand
Any answer replays from its trace: template + parameters + graph version. An auditor walks from a sentence in an answer to the exact source passage and the exact query that retrieved it.
Four steps. Zero reading lists.
Bring the literature in
Search OpenAlex, Semantic Scholar, arXiv, CrossRef, PubMed — or bring your own licences: Scopus, Embase, IEEE, Springer, Wiley. Internal reports come straight from Snowflake or Databricks.
Agents build the graph
Papers are chunked, embedded, and mined for entities and relationships — authors, methods, datasets, citations — into one knowledge graph.
Interrogate the corpus
Natural-language questions, answered across all four layers. Multi-hop, cross-paper, quota-metered.
Every claim, cited
Answers link back to the exact source passage. Publishable provenance, not plausible prose.
Browse your literature as a living graph
Every paper, method, concept, and author your corpus contains — connected, searchable, and inspectable. Click any node to read exactly where it came from and which query retrieved it.
live demo · search a method, drag a node, click for provenance — the amber edge is the contradiction
Wire it into your pipelines
A versioned public API, generated SDKs, and signed webhooks. Your LIMS, internal tools, and data pipelines get the same cited answers your team does.
- ⌁/api/v1 — stable by contract. OpenAPI-pinned, additive-only changes, Pact-verified on every merge.
- ⇄Webhooks, not polling.
paper.ingested,answer.completed— HMAC-signed, retried. - M2M auth that fits. API keys for simple integrations, OAuth2 client-credentials with scopes for enterprise.
- ↻Idempotent by design. Safe retries on every mutating call.
from h4graph import H4Graph h4 = H4Graph(api_key="h4_live_…") # ingest — batched, webhook on completion h4.papers.submit(doi="10.1234/example.2025") # ask — hybrid 4-layer answer, cited answer = h4.questions.ask( "Which methods outperform transformers on long documents? Any contradictions?" ) print(answer.text) for c in answer.citations: print(c.doi, c.passage, c.hops) # provenance, always
Start free. Scale with your corpus.
Free
- ✓ 10 papers
- ✓ 25 questions / month
- ✓ Open-source connectors
- ✓ Cited answers
Researcher
- ✓ 200 papers
- ✓ 500 questions / month
- ✓ OpenAlex · Semantic Scholar · arXiv · CrossRef · PubMed
- ✓ Priority email support
Lab
- ✓ 2,000 papers · 5 seats
- ✓ 3,000 questions / month
- ✓ Shared team graph
- ✓ BYOL connectors (Scopus, IEEE…)
- ✓ Prepaid credit packs
Enterprise
- ✓ Unlimited papers · custom quotas
- ✓ Full BYOL catalog + internal docs
- ✓ Snowflake & Databricks doc connectors
- ✓ Public API · SSO · scoped keys
- ✓ Regional data residency
- ✓ Annual commit discounts
Heavy month? Prepaid credit packs from $25 — volume bonuses up to +25%, no surprise bills, ever.
Built to pass compliance review
Your corpus stays yours. Nothing you ingest is used to train models — architecturally, not just contractually.
Every answer traces to source text, DOI, date, and licence rights — evidence your compliance team can replay.
Pin storage and inference to the region you choose. Your data never leaves the geography your review requires.
Ingestion stores only what each source's licence permits — metadata, abstract, or full text, enforced per connector. Never relicensed.
The connections are already in your literature.
Start finding them.
Ten papers, cited answers, five minutes to your first “I didn’t know that.”
Start free