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Turn documents into LLM-ready datasets

FlexOrch extracts structured data from raw documents, scores quality, masks sensitive fields, and builds export-ready datasets — all through one automated pipeline.

Structured extractionUsable data from documents
Sensitive data controlPII findings and masking in-flow
Export-ready datasetsOutputs ready for AI and analytics
Quality visibilityScore, confidence and warning signals
Execution lineageTraceable steps and data lineage

For operations, compliance, and AI teams

Unify fragmented document work under one execution model and make data reusable for downstream AI and analytics.

Privacy built in

Masking decisions are made inside the pipeline before any data leaves.

LLM-ready dataset build

Structured output, export formats, and lineage stay in one execution record.

Automatic quality scoring

Every document produces a confidence score, quality grade, and warning signals.

Workflow

One pipeline from raw documents to trusted datasets

One execution model instead of separate parsers, manual cleanup, and bolted-on privacy checks.

Step 1

Upload

Files enter as tracked

records.

Step 2

Classify

Type is set before

extraction begins.

Step 3

Extract

Fields and entities are

structured and produced.

Step 4

Score quality

Quality stays

always visible.

Step 5

Protect privacy

PII detection and masking

stay in the workflow.

Step 6

Build & export

Datasets are ready

to export.

Operational controls

Execution visibility

Every step in the pipeline remains traceable from upload to export.

Execution trackingpipeline, status, usage
Quality visibilityscore, grade, warnings
Privacy layer

Sensitive data stays governed

Masking decisions are made before the dataset leaves the pipeline.

14PII findings

names, emails, ID numbers

Maskedsafe output for

AI and analytics

Pipeline output

Export-ready outputs

Reusable data products for analytics, AI, and internal systems.

JSON
CSV
Parquet
Platform

Turn fragmented document work into a platform capability

FlexOrch is built for teams where document understanding, privacy, and dataset readiness must work together — not in separate systems.

Operational overview

Document processing, privacy, and export converge in one product logic.

Replace scattered tooling with a single platform surface, one execution language, and reusable data outputs.

Ingestionregister, classify, store
Extractionstructured fields, key-value, entities
Governancequality, privacy, lineage, export

Execution-centered

Every upload, job, execution, and export becomes part of a visible operational record.

Privacy-native

Privacy controls are not an add-on. They are part of the product's core behavior.

LLM-ready output

Structured data, quality signals, and lineage make downstream AI usage easier.

API-first design

Every platform capability is accessible directly via API. The UI is a consumer, not the authority.

Developers

Built for platform teams, product teams, and API consumers

A stable resource model, Python and TypeScript SDKs, and a predictable path from file processing to dataset export.

from flexorch_audit import audit

result = audit("contract.pdf")

result.quality_grade    # "A"
result.quality_score    # 0.91
result.pii_findings
# [{ "type": "TCKN", "count": 3 },
#  { "type": "email", "count": 2 }]
API-first

Every platform capability is accessible directly via API. The UI is a consumer, not the authority.

Predictable outputs

Typed fields, quality signals, and export behavior are defined in one place.

Operational visibility

Jobs, executions, datasets, and masking steps stay visible throughout the product surface.

Open source —PyPI ↗npm ↗GitHub
Pilot Access

Start your pilot conversation today.

Scope and timeline are set in the first meeting — no commitment required.

Invite-only pilot30-day trialKVKK compliantGDPR compliant