LLM pipelines that turn messy documents into structured data you can trust
Causal Labs engineers multi-stage LLM extraction pipelines — OpenAI, Gemini, the OpenAI Batch API, vector retrieval and Pydantic schema validation — that convert unstructured documents into clean, structured, queryable data. A prior engagement used multi-stage GPT/Gemini chains with the Batch API to cut inference cost 50–75% while keeping every extracted field traceable to its source passage.
Causal Labs engineers multi-stage LLM extraction pipelines — OpenAI, Gemini, the OpenAI Batch API, vector retrieval and Pydantic schema validation — that convert unstructured documents into clean, structured, queryable data. A prior engagement used multi-stage GPT/Gemini chains with the Batch API to cut inference cost 50–75% while keeping every extracted field traceable to its source passage.
Why a single prompt-and-parse call isn't a production pipeline
A single LLM call that returns JSON works in a demo. In production it breaks in ways a demo never shows: the model hallucinates a field that isn't in the source document, a malformed response fails silently downstream, or costs spiral because every document — long or short, simple or complex — gets routed through the most expensive model available.
The fix isn't a better prompt. It's a pipeline: cheap models filtering and routing documents, schema validation catching malformed output before it reaches your database, and a traceability layer so every extracted value can be checked against the exact source passage it came from — which matters as much for debugging as it does for auditability.
We've built this pattern for financial documents, contracts and operational reports where a wrong extracted number has real consequences, and the batch-processing discipline that comes with it is usually where most of the cost savings live.
How we build an LLM extraction pipeline
Requirements gathering
We look at real sample documents — not idealized ones — to find the edge cases that will actually break a naive pipeline.
Scoping & proposal
We define the extraction schema (Pydantic), the accuracy bar per field, and whether Batch API latency (hours, not seconds) is acceptable for your volume.
Design
Multi-stage architecture: cheap-model triage → targeted extraction → schema validation → optional human review queue for low-confidence fields.
Build
Pipeline stages, retry logic for malformed responses, and source-passage traceability so every field is auditable.
QA & review
We benchmark accuracy against a held-out labeled set and tune the routing thresholds between cheap and expensive models.
Handover & support
You get the pipeline, its cost/accuracy dashboard, and documentation for adjusting the schema as document formats change.
What's included
Multi-stage extraction architecture
Cheap-model triage, targeted extraction, and schema validation stages instead of one expensive call per document.
Batch API cost engineering
Routing high-volume, non-urgent extraction through the OpenAI Batch API for material cost reduction over synchronous calls.
Schema validation
Pydantic models reject malformed or out-of-schema output before it reaches your database.
Source traceability
Every extracted field links back to the source passage it came from, for audit and debugging.
Vector / RAG retrieval
Where extraction needs context beyond a single document, retrieval-augmented generation grounds the model in the right source material.
Cost & accuracy monitoring
Dashboards showing per-document cost and field-level accuracy so drift is caught early.
- Production extraction pipeline (multi-stage)
- Pydantic schema definitions
- Batch API cost-routing logic
- Source-passage traceability layer
- Accuracy benchmark against labeled data
- Cost/accuracy monitoring dashboard
Primary model provider for extraction and reasoning stages, including the Batch API for cost-sensitive volume.
Used where its context window or pricing fits a specific extraction stage better.
The main lever for cost reduction on high-volume, non-real-time extraction.
Grounds extraction in the right source context when a single document isn't self-contained.
Schema validation that rejects malformed LLM output before it reaches production data.
Engineered multi-stage GPT/Gemini chains with the OpenAI Batch API for 50–75% cost reduction and full source traceability.
LLM inference cost reduction via Batch API routing
typical delivery window
Questions about this service
How do you stop the model from hallucinating data that isn't in the document?
Schema validation rejects out-of-bounds output, and every extracted field is required to trace back to a specific source passage — if a value can't be located in the source, it's flagged for review rather than silently accepted.
Is the OpenAI Batch API always the right choice?
No — it trades latency (results in hours, not seconds) for roughly half the per-token cost. It's the right default for high-volume, non-urgent extraction, and the wrong choice for anything needing a real-time response.
Can this pipeline work with documents in multiple languages, including Bahasa Malaysia or Mandarin?
Yes — modern multilingual models handle this well for most document types. We validate accuracy per language during the QA stage rather than assuming parity with English.
What happens to extraction accuracy as document formats change over time?
The monitoring dashboard tracks field-level accuracy over time, so a format change that starts degrading extraction quality is visible before it becomes a data-quality incident, not after.
Drowning in PDFs, reports or scanned forms that someone still re-types by hand?
Send us a sample document. We'll tell you what a working extraction pipeline for it actually looks like.
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