Use Case · Documents

Document processing on the Software Lifecycle Cloud

Document processing on KERNSTACK turns an uploaded PDF into structured records your systems can use: the file lands in a bucket through a presigned upload, a worker extracts the fields — with inference help where layout defeats templates — extracted values land in a table, and the documents a machine is unsure about pause in a workflow for a person to confirm before anything downstream trusts them.

Object BucketsContainer WorkersApproval WorkflowsNoSQL Tables
Talk to us Read Technical Documentation → Software Lifecycle Cloud →

The engineering bottleneck with assembled stacks

Back-office teams retype PDFs into a system of record because the document is where the truth arrived and the database is where the truth must live. The typing is slow, the error rate is human, and the obvious automation — an extraction API — still leaves the team owning the awkward residue: the crooked scan, the missing page, the field that two extractors read differently.

Architecture on the Software Lifecycle Cloud

Upload is a presigned URL into a bucket prefix reserved for unprocessed files. A worker claims each new document, extracts text and fields, and writes a record keyed by document id, with per-field confidence. High-confidence records flow onward; low-confidence ones become a human workflow step showing the page beside the extracted values, so confirmation is a glance instead of a retype.

Confidence is a routing decision

The pipeline’s important output is not the fields; it is knowing which documents not to trust yet. Recording confidence per field lets the workflow route precisely — approve the supplier name automatically, ask a human about the total — and every human correction improves the record of where extraction struggles, which is the dataset you tune templates and prompts against.

  • Originals stay in the bucket beside their records, so any extracted value can be traced to its page
  • Human review is a titled workflow step, and the correction is stored with the document id
  • Failed extractions are table rows with reasons, not files forgotten in an error folder

What stays manual on purpose

The last five percent of documents — damaged, handwritten, adversarial — will keep needing people. The architecture’s claim is narrower and more useful: people stop touching the ninety-five percent that machines read reliably, and their attention concentrates exactly where the confidence scores say it should.

FAQ

Frequently asked questions

Browsers and partner systems upload with presigned URLs into the bucket, so large PDFs never pass through your function code. The worker picks work up from the unprocessed prefix and marks progress in the documents table.

Whichever fits the document family: deterministic parsing in the worker for stable layouts, inference models for variable ones. The pipeline shape — bucket, worker, confidence, review — is identical either way, so extraction methods can change per document type.

They become human workflow steps with the page rendered beside empty fields. Nothing is silently dropped: every uploaded document is a table record in exactly one state — extracted, in review, confirmed, or failed with a reason.

Connected in the Project Graph

Related capabilities, architectures, and comparisons

Category PillarThe Software Lifecycle CloudWhy people and agents build, deploy, and operate on a single vertically integrated stack.Service · DataBucketsObject storage with presigned uploadService · ComputeWorkersContainer workers for long jobsService · OrchestrationWorkflowsDurable workflows with human pausesService · DataTablesServerless key-value and document tablesIndustry SolutionHealthTech Cloud SolutionsHealthTech products on KERNSTACK hold care-adjacent data — patient documents, encounter records, consent forms — in one declared project in a European zone on named infrastructure. The needs that dominate are unglamorous and absolute: know exactly where every document lives, who touched it, how consent was captured, and how a record can be frozen the day a legal hold arrives.HealthTech BlueprintDocument processing for HealthTechDocument processing for HealthTech: referrals and findings extracted into the record, uncertain pages confirmed by staff, hashes kept.Use Case · ProductExperiment assignmentAssignment is a pure function of the subject key and the experiment’s salt — stable across requests, no coordination needed. The function records the assignment row once and serves the variant; outcome handlers were already writing events, which now simply carry the experiment id the assignment returned. Analysis reads assignments and outcomes from neighbouring tables with the same keys.

Ship your stack on the Software Lifecycle Cloud.

Closed preview on dedicated European infrastructure. Bring a repository or start from a blueprint.

Talk to us See pricing & credits →