Document AI Development
FISTA Solutions builds document AI pipelines that produce trustworthy data: classification by document type, field extraction with page-level citations and per-field confidence, validation against your master data, a fast review queue for uncertain values, and accuracy measured on your own documents.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does document AI development include?
Document AI work covers document classification, OCR and layout handling where needed, field extraction with citations, validation against business rules and master data, a review interface built for speed, and idempotent posting into your systems.
- 01
Classification
Routing each document to the right extraction path, including mixed and multi-document files.
Intake - 02
Extraction with citations
Field values with page and region citation plus per-field confidence rather than one document score.
Extraction - 03
Validation
Arithmetic, format, and master data checks so wrong values are caught before they reach your systems.
Quality - 04
Review interface
Source and value side by side, keyboard-first, with batch handling for common corrections.
Review - 05
Posting
Idempotent posting into ERP or line-of-business systems with full traceability per document.
Integration
Requirements
Which requirements shape document AI development?
Document AI is judged by whether the data it produces can be trusted. Requirements cover provenance per field, honest accuracy measurement on real documents, confidence thresholds by criticality, review efficiency, and safe reprocessing.
| Requirement | Why it matters | How FISTA builds to it |
|---|---|---|
| Provenance | Extracted values drive real decisions. | Page and region citation per field, retained with the record so any value is verifiable. |
| Measured accuracy | Demo accuracy is not production accuracy. | Labeled evaluation on your own document mix, reported per field and per source type. |
| Confidence by criticality | A total needs more certainty than a description. | Per-field confidence thresholds configured independently and tuned against cost of error. |
| Review efficiency | Slow review erases the saving. | Review interface with source and value together, keyboard-first, with batch corrections. |
| Safe reprocessing | Documents get reprocessed. | Idempotent posting with deduplication, so reruns never create duplicate records. |
Where AI fits
How should you sequence document AI development?
Go deep on one document type before going wide: pick the highest-volume type, label a representative sample including poor scans, tune extraction against it, then expand type by type with fresh evaluation each time.
- 01
1. Pick the highest-volume type
Depth on one type produces real savings; shallow coverage of many produces mediocre results everywhere.
- 02
2. Label a real sample
Including the faxes and phone photos, because clean PDFs flatter every extraction system.
- 03
3. Set thresholds by field
Critical fields tighter, descriptive fields looser, tuned against cost of error.
- 04
4. Build the review screen
Review speed determines net savings as much as extraction accuracy does.
- 05
5. Expand with evaluation
Each new document type or source gets its own labeled evaluation before being trusted.
Cost and timeline
How much does document AI development cost, and how long does it take?
Cost is driven by document variety and quality, field count, and validation complexity; timeline by sample availability and labeling. FISTA does not quote blind: the scoping call returns a pipeline design and a measured accuracy report.
Document quality is the dominant variable. Clean digital documents extract far better than scans of faxes, so FISTA measures on your real mix and reports by source rather than quoting one headline number.
Review interface design decides whether savings materialize. An accurate extractor behind a slow review screen produces no net benefit, so the reviewer experience is designed with the pipeline.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI system?
FISTA delivers AI in four phases: a discovery sprint that defines the success metric, data readiness, and specification; a design that fixes the model strategy, retrieval, guardrails, and evaluation plan; iterative builds scored against a golden set; and a production release with tracing, dashboards, cost budgets, and a change process.
- 1
Discover and define
Use-case selection, data audit, success metrics, risk review, and a written specification with an evaluation plan.
OutputSpecification, golden set, estimate
- 2
Design the system
Model strategy, retrieval and data pipelines, guardrails, human review points, and the deployment target.
OutputArchitecture, model decision record
- 3
Build and evaluate
Two-week increments, each scored on the evaluation harness for quality, latency, and cost, demoed on real data.
OutputEval reports, working system
- 4
Release and monitor
Production deployment with tracing, quality and cost dashboards, drift alerts, runbooks, and a change process that re-runs the evals.
OutputProduction AI system with SLOs
Why FISTA
Why choose FISTA Solutions for document AI development?
FISTA measures document AI accuracy on your own documents, cites every extracted value, and designs review for speed so the savings are real. Work is contracted through a US entity with full IP assignment.
Document AI specifics
- Accuracy is measured on a labeled sample of your real documents, including poor scans, and reported per field.
- Every field carries page and region citation plus its own confidence, not a single document-level score.
- Validation against arithmetic, format, and master data catches wrong values before they reach your systems.
- Posting is idempotent with deduplication, so reprocessing is safe by design.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What buyers ask before an AI build.
Straightforward guidance for evaluating scope, fit, and the next step.
01How accurate is document extraction?
It depends on document quality and field type, which is why accuracy is measured on your own labeled sample and reported per field and source. A headline number from clean test data does not predict your results.
02Do we still need human review?
For material fields, yes, below the configured confidence threshold. The aim is to reduce review to exceptions rather than eliminate it, because silent extraction errors cost more than a review queue.
03Can it handle handwriting and poor scans?
Partially, with lower accuracy and correspondingly lower confidence, which routes more of those documents to review. The measured report shows exactly how your worst sources perform.
04Can extracted data post into our ERP?
Yes, after validation, with idempotent posting and full traceability so reprocessing is safe and every value can be traced back to its source page.
05How long does a document AI project take?
One document type typically reaches production within weeks once a representative labeled sample exists; additional types follow with their own evaluations.
Scoped in writing before you commit
Turn the document backlog into validated data.
Bring a representative sample including the bad scans. The scoping call returns a pipeline design and a measured accuracy report.