Document Intelligence
Pipelines that turn unstructured documents into structured data your systems can use.
Extract, classify, summarize — at scale
Invoices, contracts, clinical notes, intake forms — the data is there, the problem is getting it out. We build extraction and classification pipelines that combine vision models, LLMs, and deterministic validators to produce structured output with measured accuracy.
Outcomes
Our approach.
Sample & schema
Collect a representative sample across every variant in the wild. Define the target schema — what fields, what types, what's required vs. optional.
Parse + extract
Layout-aware parsing for structure, then LLMs with structured output for extraction. Strong typing at every boundary.
Validate & route
Regex, cross-field, and business-rule validators run on every extraction. Low-confidence items go to a human review queue with the original doc attached.
Backfill + monitor
Historical archives get processed in parallel batches. Production traffic is sampled continuously to catch drift before it hurts.
What you get.
Production-shaped, from day one.
// Schema-constrained extraction with validation
const doc = await parse(file, { layout: true })
const record = await extract(doc, {
schema: PatientIntakeSchema, // zod schema
model: "gpt-4.1",
temperature: 0,
})
const validation = validate(record, {
cross: patientRules,
required: ["patient_id", "dob", "allergies"],
})
if (validation.confidence < 0.9) {
queueForReview(record, doc, validation)
}A proven shape for this solution.
We adapt it to your cloud, data, and compliance requirements. Nothing here is boilerplate — every layer is justified by the numbers.
Where this shows up.
- Healthcare intake and record mapping for patient chat
- Contract review and clause extraction
- Invoice and receipt processing
- Research paper and report summarization
What we use.
We’re not religious about tools. We pick what fits your constraints and team.
Shipped examples.
Healthcare patient data mapping & health information chat
Mapped and normalized patient data to power a grounded chat experience where patients can ask questions about their own health information — safely.
What teams usually ask.
What accuracy can we expect?
+
>95% field-level on typical business documents after tuning. Accuracy depends heavily on document variance — we report it honestly per field, not as an overall headline number.
How do you handle low-confidence extractions?
+
Configurable confidence thresholds route items to a human review queue. Reviewed items become training signal for prompt and schema refinement.
Can this run on-prem or in a private VPC?
+
Yes. For sensitive data (healthcare, legal, finance) we deploy into your VPC with private model endpoints via Bedrock, Azure OpenAI, or Vertex.
Related solutions.
Retrieval-Augmented Generation
End-to-end RAG pipelines from ingestion to retrieval to answer generation, built for accuracy and cost control.
Conversational AI & Chat Lookup
Production-grade chat systems that answer from your sources with citations, guardrails, and session memory.
Cloud AI Infrastructure
We stand up the platform layer so your AI systems are secure, observable, scalable, and cost-governed from day one.
Ready to accelerate your tech growth?
Schedule your free consultation today and let's discuss how we can help your business scale efficiently.
