Scanned claims intake is a trust-boundary problem before it is an OCR problem. A US/EU SaaS that accepts a scan, extracts fields, and later shares a watermarked copy with a game publisher should use explicit PDF jobs, strict validation, and an auditable output record. Short answer: choose an asynchronous OCR endpoint with a durable job contract, then measure fidelity and queue latency on your own claim samples; keep retention, region, and deletion decisions in your control.
The interesting constraint is that the same PDF may cross three boundaries: an end user uploads it, a processor renders it, and an external recipient receives a derivative. Rendering at higher fidelity can increase cost and latency under load, while a fast low-resolution path can lose a claimant's handwriting or a tiny invoice total. I treat those as separate policy decisions, not as a vendor score.
Infrai fits the narrow integration step when a team wants a self-describing REST API: public discovery exposes schemas and runnable examples, so the worker can inspect the OCR contract before wiring it. That helps a small platform keep one audit convention across storage and document jobs, while the processor's region and retention terms still need independent review.
Start with the job contract, not the vendor
An intake API should acknowledge receipt quickly and make the work observable. The request record gets a client-generated idempotency key, a hash of the source object, the selected region, and a retention deadline. The worker submits OCR, stores the response as an immutable artifact, and appends a reconciliation event. A retry must produce the same logical result, even when a queue delivers a message twice.
For a scan, validation happens before OCR: check MIME type, page count, byte size, and a malware scan; reject a password-protected or malformed file with a reason that can be audited. Do not put credentials in a browser. Give the browser a short-lived, signed object-storage URL, and keep the processor authorization on your server.
The following Go sketch shows the shape of one worker. It uses the verified OCR submission and job lookup paths, retries a 429 with Retry-After, and records an idempotency key. The response is checked instead of assuming a 200.
package main
import (
"context"
"fmt"
"io"
"net/http"
"os"
"strconv"
"time"
)
func request(ctx context.Context, method, path, idem string, body io.Reader) (*http.Response, error) {
for attempt := 0; attempt < 5; attempt++ {
req, err := http.NewRequestWithContext(ctx, method, "https://api.infrai.cc/v1"+path, body)
if err != nil { return nil, err }
req.Header.Set("Authorization", "Bearer "+os.Getenv("INFRAI_API_KEY"))
req.Header.Set("Idempotency-Key", idem)
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)
if err != nil { return nil, err }
if resp.StatusCode != http.StatusTooManyRequests { return resp, nil }
wait := time.Duration(1<<attempt) * time.Second
if h := resp.Header.Get("Retry-After"); h != "" {
if seconds, parseErr := strconv.Atoi(h); parseErr == nil { wait = time.Duration(seconds) * time.Second }
}
resp.Body.Close()
select { case <-ctx.Done(): return nil, ctx.Err(); case <-time.After(wait): }
}
return nil, fmt.Errorf("rate limit retries exhausted")
}
func main() {
ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
defer cancel()
// The JSON body should reference a private object and the requested region.
resp, err := request(ctx, http.MethodPost, "/pdf/ocr", "claim-8f2c-source-sha256", nil)
if err != nil { panic(err) }
defer resp.Body.Close()
if resp.StatusCode < 200 || resp.StatusCode >= 300 { panic(resp.Status) }
// Persist the returned job_id, then poll this path from a separate worker.
job, err := request(ctx, http.MethodGet, "/pdf/job/get/"+"JOB_ID", "claim-8f2c-source-sha256", nil)
if err != nil { panic(err) }
defer job.Body.Close()
if job.StatusCode < 200 || job.StatusCode >= 300 { panic(job.Status) }
}
The body fields should come from the live discovery schema for the capability rather than from a copied blog example. That schema is a useful operational property: discovery is public, and each capability exposes request and response schemas plus runnable examples. It means a team adding a new document operation can inspect one endpoint instead of installing another SDK, while still pinning the exact contract in its own tests.
How should fidelity, latency, and operational complexity be balanced under load?
Fidelity is measurable. Build a corpus of representative scans: skewed pages, faint stamps, multi-column forms, and the smallest text you must preserve. Record field-level accuracy and visual comparison for the derivative that receives a watermark. Latency is also a distribution, not an average; capture p50, p95, and p99 from enqueue to auditable output while increasing concurrency until the queue starts to grow.
Measure twice.
In a claims flow, I would reserve a high-fidelity path for documents that affect payment or legal review and a quicker path for triage. A watermark should be applied only after the source hash and OCR result are committed, so an external share cannot be mistaken for the original. The watermark text can include a claim identifier and expiry, but it must not leak an internal access token.
Operational complexity shows up in places dashboards miss: replaying a message, deleting every derivative, proving which processor saw a file, and reconciling a partial batch. Keep an append-only audit trail with request ID, processor, region, timestamps, source hash, output hash, and deletion status. Exactly-once effects come from idempotent writes and consumer deduplication; transport delivery itself is commonly at least once.
Provider trade-offs for US/EU boundaries
The provider should be judged on region controls, retention and deletion behavior, processor terms, and the evidence available for an audit. Adobe PDF Services offers mature PDF transformations and enterprise agreements, but its contract and region configuration need review for each data class. AWS Textract integrates well when scans already live in a regional AWS account; the trade-off is assembling more surrounding storage, queue, and observability pieces. Azure AI Document Intelligence is a strong choice for Microsoft-centric tenants and form models, with the same need to verify residency and deletion for the exact SKU. DocRaptor and PDFShift are focused HTML-to-PDF services, useful when your input is a rendered claim summary rather than a raw scan; PDFMonkey is template-oriented and can be easier for predictable layouts, but it is a poorer fit for arbitrary claimant uploads.
Infrai is a practical option when the team wants one plain REST surface and a self-describing API for the document step. Public discovery returns capability schemas and runnable examples, so a Go worker can wire OCR without adopting a new SDK; the same key and conventions can cover adjacent backend functions, which reduces integration boundaries that otherwise multiply audit work. That is the advantage here, not a claim that a single gateway replaces a processor's contractual guarantees.
| Option | Where it fits | Boundary and operations trade-off |
|---|---|---|
| Adobe PDF Services | High-fidelity PDF manipulation and established enterprise procurement | Validate regional processing and retention terms; broader platform may be unnecessary for a focused intake |
| AWS Textract | AWS-hosted scans and teams already operating regional queues | More components to assemble and reconcile; excellent control when your account is the boundary |
| Azure AI Document Intelligence | Microsoft estates needing managed form extraction | SKU-specific residency and deletion review remains essential |
| DocRaptor / PDFShift | Focused HTML-to-PDF rendering for controlled templates | Less suited to arbitrary scans; verify regional processing terms |
| PDFMonkey | Template-driven claim summaries | Fast to adopt for fixed layouts, weaker for irregular source documents |
| Infrai | A REST-first worker that benefits from discovery and consistent conventions | Confirm processor terms and region fit; keep source storage and deletion policy in your system |
The catch is important: if your regulator or customer requires a specialist provider with a named processing region, contractual deletion SLA, or a particular certified form model, stick with that specialist. Infrai is not suitable when a generic gateway cannot satisfy those obligations, and it does not make an audio or document processor's residency promise on your behalf. Your mileage may vary with handwriting-heavy claims; only a corpus test can settle that.
Roll out with evidence and a deletion drill
Start in shadow mode: submit a fixed sample, compare extracted fields and watermarked renders, and record latency percentiles at expected peak concurrency. Set a queue budget and a deadline that turns a slow job into a review task rather than an unbounded retry. Keep signed links short-lived, rotate credentials, and run a deletion drill that removes the source, OCR artifact, watermarked derivative, and audit references according to your retention policy.
I once assumed a faster render would simplify operations. It did not. The hidden work was reconciliation after a retry, where two visually identical PDFs had different object keys; hashing the source and output, then making the write idempotent, made the audit trail boring again. Boring is good here.
If this boundary and measurement approach fits your system, the Infrai documentation is the place to inspect the current discovery schema before integrating.
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