Train on cleaner data
Deduplicated, schema-aligned, drift-monitored corpora. We absorb the messy work so your team focuses on architecture, not pipelines.
Index / Diagest
Raw data in.
AI-ready data out.
A pipeline that ingests at scale, cleans the noise, organizes what remains, and hands AI systems the exact data they can retrieve from. Live pipeline health is public.
Corpora
8
6 scheduled · 2 manual
Vectors
1,186
across all sinks
Records
1,107
post-dedup · embedded
Drift · 24h
—
eval cycle pending
Corpora · latest run
tech-blogs
Scheduled
579
arxiv-papers
Scheduled
500
news-feeds
Scheduled
25
beige-book
Scheduled
3
fed-minutes
Scheduled
0
fed-speeches
Scheduled
0
pdf-samples
Manual
0
s3-samples
Manual
0
Who it's for
Built for teams burning time on data prep.
Diagest replaces the bespoke ETL, cleaning, chunking, and embedding work your team is doing right now to make raw inputs retrievable.
One retrieval-ready surface
Bring private documents, databases, and event streams into a single retrieval surface. Vector store, Parquet, JSONL, or REST. Your call.
Convert archives to corpora
PDF archives, RSS feeds, SEC EDGAR, arXiv, and historical APIs become AI-queryable with provenance, quality scoring, and retrieval-recall measurement built in.
Stop burning weeks on prep
If you're building an AI product and burning weeks on data prep, that's exactly the work Diagest replaces.
How it works
From any source to any AI-ready sink.
01 · Connect
Any source
S3, HTTP APIs, webhooks, PDF, Postgres CDC, RSS, SEC EDGAR, arXiv, crypto WebSockets. New connectors on request.
02 · Process
8-stage pipeline
Ingest → parse → clean → validate → dedup → filter → chunk → embed. Quality + drift tracked end-to-end.
03 · Deliver
AI-ready outputs
Vector store, Parquet, JSONL, HF dataset, REST. One schema, full provenance, query-ready.
FAQ
Frequently asked.
What is Diagest?
A data-for-AI pipeline. It consumes large data volumes from any source, cleans and deduplicates them, filters out noise, and organizes what remains into AI-ready outputs: vectors, Parquet, JSONL, or REST API.
What sources does Diagest ingest?
Object storage (S3 and equivalents), HTTP APIs, webhooks, PDF archives, Postgres CDC, RSS feeds, SEC EDGAR filings, arXiv, and crypto exchange WebSockets. New connectors are added on request.
What output formats does Diagest produce?
Vector store (Qdrant-compatible), Parquet on S3, JSONL on S3, Hugging Face datasets, REST API, and on-demand sinks. All outputs share a single schema with provenance and quality scoring.
How does Diagest handle data quality and drift?
Schema validity, completeness, uniqueness, freshness, embedding coverage, retrieval recall, and KS-stat drift are tracked per corpus per 24h. A scheduled drift watcher fires backfills when per-corpus σ thresholds are crossed, with per-corpus cooldown and idempotency guards.
What is the latency profile?
Streaming sources reach AI-ready output in tens of milliseconds. Batch sources are throughput-limited but typically complete inside the same hour for typical corpus sizes.
How is pricing structured?
Per-GB processed plus a flat platform fee. Contact us for quotes calibrated to your sources, output sinks, and SLA needs.
From the blog
Notes on what we're building.
Buying Guide
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ComparisonVector Store Choices in 2026: Qdrant vs Pinecone vs pgvector vs Weaviate vs Milvus
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PitfallsWhy RAG Pipelines Fail: 5 Common Pitfalls
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