Zparse
The data intelligence layer for AI

The missing layer between your data and your AI.

Generic ETL doesn’t know what AI needs. Vector DBs don’t know what’s in your data. Zparse is the missing middle — engineering, intelligence, and production retrieval patterns. One product.

01 · engineering
Pipeline
extract · transform · load
02 · intelligence
Understanding
structure · entities · quality
03 · patterns
Best practices
chunking · embedding · ranking
02 — why AI underperforms

Your AI is only as smart

as the data you feed it.

The model isn’t the problem. The data is. Bad chunks, broken tables, lost structure, generic embeddings — and your RAG hallucinates, your agent misses context, your copilot gets things wrong. Every quarter you patch it; every quarter it slips again.

The four things teams keep getting wrong:

01
Naive chunking that splits tables, breaks paragraphs, loses hierarchy
02
No semantic understanding — every chunk treated like a blob
03
No quality signal — you can’t tell what’s retrieved from what’s missed
04
No baked-in patterns — every team reinvents what’s already known to work

Zparse fixes the layer your AI actually depends on.

03 — proof of the bottleneck
87%

of enterprise AI projects die before reaching production.

It’s not a model problem. Today’s models are powerful enough to create enormous value. The problem is upstream: getting data from your internal systems — fragmented, badly structured, legacy — into a shape your AI can actually use.

Most teams pick one of three losing strategies:

—
Outsource the pipeline to a vendor that locks them in
—
Build it themselves, and watch it break six months later
—
Hand it to a consultancy that walks away with the knowledge

Zparse exists so none of these has to be yours.

04 — three things, one product

Engineering. Intelligence.
Production patterns.

01 · engineering

The pipeline, solid.

Connect to any source: PDFs, Excel, XML, SharePoint, S3, internal APIs, SFTP. Transform with structure preservation. Deliver to any consumer — vector DB, agent, model, dataset. Observable, deployable, versioned.

The plumbing you’d build in three months. Working on day one.

Connectors · 20+ sources
PDFOCR readya
XLSXmulti-sheet∑
XMLlegacy</>
JSONnested{}
CSVstreaming,
JSONLnewline-delim↵
MDmarkdown∞
PARQUETcolumnar▦
02 · intelligence

We understand what’s in your data.

Section hierarchies. Tables intact. Entities extracted — parties, dates, amounts, references. Quality scored per chunk. Metadata your retriever can actually filter on. Not a wall of generic blocks.

Your AI sees structure, not soup.

Document · contract_q3.pdf● analyzed
structuresection.detect 14 sections · 3 levels deepok
structuretable.preserve 6 tables · 142 rowsok
entitiesparty Acme Corp, Lumenreq SAS2
entitiesdate 2026-01-15 · 2027-01-152
entitiesamount €420,000 · annual1
qualitylow.score page 88 · scan quality0.42
03 · patterns

Production patterns, baked in.

Chunking strategies tuned per content type. Embedding models picked for your retrieval task. Hybrid search, re-ranking, query rewriting — when they actually help. Default to what works in production, override when you need to.

Patterns proven across hundreds of deployments. Without you having to learn them the hard way.

Retrieval recipe · auto-selected
chunkingfixedsemantichierarchical
embeddinge5-mistralcohere-v3openai-3-l
retrievaldensehybridrerank
queryrawhydemulti-query
05 — our thesis

Build the intelligence layer once.
Every AI use case inherits it.

the usual way
A new pipeline for every AI project
Sales copilot · pipeline · €15k
Legal RAG · pipeline · €18k
HR assistant · pipeline · €12k
Ops agent · pipeline · €22k
…next use case · pipeline · €?
12-month bill€200k+

Every project rebuilds the plumbing, the parsing, the chunking, the choice of embeddings. Five teams. Five fragile systems. Quality is whatever each project's intern figured out.

vs
our way
One data intelligence layer, shared.
Sales copilotLegal RAGHR assistantOps agentNext use case
Z
Your AI data intelligence layer
engineering · intelligence · patterns
12-month bill€40k

Build the layer once. Every new AI use case inherits the same connectors, the same understanding, the same proven retrieval patterns. Quality compounds instead of starting from zero.

Your AI quality isn’t a feature you ship project by project. It’s an asset you build once.

06 — where we sit

What we are.
What we’re not.

The AI stack has clear categories: tools for moving data, tools for storing vectors, tools for building agents. Zparse sits in the gap between them — and does what none of them does on its own.

we’re nota generic ETLFivetran, Airbyte and friends move data between systems. They don’t know what AI needs. They don’t chunk, they don’t embed, they don’t preserve semantic structure.
we’re nota vector DBQdrant, Pinecone, pgvector store and search embeddings. They don’t know what’s in your raw data. They don’t extract, they don’t parse, they don’t enrich.
we’re nota frameworkLangChain and LlamaIndex are code libraries. You build with them. They don’t deploy, they don’t observe, they don’t carry opinions about what works in production.
we arethe missing middleA product that does ETL and data intelligence and production retrieval patterns. Built for AI consumption end-to-end. Feeds any vector DB, any agent, any model.
A new category sits between your data and your AI. This is it.
07 — customer cases

Three organizations.
One pipeline. Many AI use cases.

Aerospace · 4 AI use cases
CAC 40 aerospace group
12,000 technical specs ingested through Zparse, feeding a RAG for engineers, a maintenance copilot, a supplier qualification agent, and a regulatory Q&A bot. One pipeline. Four shipped use cases.
consumersQdrant + local Mistral
Legal · 2 AI use cases
International law firm
40,000 client contracts processed by Zparse, powering a contract-search RAG and a clause-extraction agent for due diligence. Same ingestion, two different products.
consumerspgvector + Claude EU
SaaS · multi-tenant
European SaaS scale-up
Per-customer ingestion at scale, feeding customer-specific AI agents. New use cases ship in days, not months — the ETL doesn’t change.
consumersPinecone + OpenAI
08 — forward deployed engineering

More than a platform.
A team that installs it with you.

For critical deployments, our team works directly with you on-site. This isn’t technical assistance. It’s a strategic skill transfer.

01
Design the data architecture that will serve your AI for the next 5 years
02
Integrate Zparse into your existing IT without adding technical debt
03
Train your teams to become autonomous — not dependent
04
Deliver infrastructure that belongs to you
We deliver infrastructure that belongs to you. Not one you belong to.
deployment · enterprise● in progress
week 01–02Discoverydone
week 03–04Architecture designdone
week 05–08IT integrationin progress
week 09–10Production launchplanned
week 11–12Autonomy handoffplanned
Success rate93%
09 — security & compliance

The requirements your auditors verify.

HostingEU (Germany) by default · France on request · On-premise available
Client storageEphemeral by design · no persistence without explicit instruction
AI consumersAny vector DB, agent framework, model · full audit trail of routing
ComplianceGDPR · ISO 27001 in progress
Sovereign cloudCompatible with SecNumCloud (OVH, Scaleway)
IsolationSecure multi-tenant · single-tenant · air-gap possible
AuditFull trail · exportable logs · annual pentests
AuthenticationSSO · SAML · OIDC · MFA · granular RBAC
EncryptionAt rest AES-256 · in transit TLS 1.3 · customer keys on-premise
10 — the missing layer

Stop feeding your AI bad data.

Engineering, intelligence, production patterns — in one product. The missing layer between your data and your AI.

Build it once. Every use case inherits it.

11 — FAQ

Frequently asked.