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No more brittle templates. We use vision-capable models that read a document’s layout and context directly, so a scanned invoice from a new supplier extracts correctly the first time, not after weeks of template tuning.
Every incoming file gets sorted by type and sent down the right processing path automatically, whether it’s a PO, a lab report, or a KYC bundle. No one is manually sorting a shared inbox anymore.
Extracted fields are checked against a defined schema and your own business rules before anything moves downstream. A due date outside your fiscal year gets flagged, not silently posted.
Fields the model is unsure about get routed to a reviewer with the source page and the flagged value attached. High-confidence extractions post automatically. Nothing sits in a single all-or-nothing queue.
Bring us a sample batch and we'll show you what a working extraction pipeline actually catches, before you commit to anything.
Get a Sample ExtractionA single vision-based classifier handles format drift across suppliers and partners without a retraining cycle for every new layout.
Mixed-language documents and handwritten fields (approval stamps, signatures, annotations) get read in context instead of silently dropped.
Preprocessing corrects skew, noise, and low resolution before extraction runs, instead of just failing on anything under a quality threshold.
Fields that carry compliance weight (tax IDs, patient identifiers, policy numbers) get validated against business rules, not just extracted and trusted.
Every invoice re-keyed by hand is a few minutes nobody gets back, multiplied across thousands of documents a month. That math is familiar.
What’s less obvious is the second-order cost: the approval that sits for three extra days because someone’s waiting on a manually validated field, the compliance audit that takes twice as long because extraction logs don’t exist, the new hire spending their first month doing data entry instead of the job they were hired for. None of that shows up as a line item. It shows up as your best people doing work a pipeline should be doing instead.
We design the schema and the confidence thresholds before we write a line of extraction code. That order matters more than the model you pick.
Files arrive from email, a portal, scanners, or mobile capture. We normalize format, correct scan quality issues, and route each file into the pipeline before any extraction runs.
A vision-capable model reads the document, classifies its type, and extracts fields directly from layout and context, without a per-template training cycle for every new format.
Every extracted field is checked against a defined schema and your business logic. This is where we build NLP pipelines that classify contracts and extract clause-level detail for anything beyond simple field capture.
Fields below your confidence threshold go to a reviewer with the source page attached. High-confidence fields post automatically. You set the threshold, not us.
Validated data writes into your ERP, CRM, or internal systems through the data integration work that keeps extracted records in sync across ERP and CRM rather than a one-off export.
Line items, tax codes, and PO matching extracted and validated before posting, with a three-way match against purchase orders and delivery receipts.
Clause extraction, obligation tracking, and renewal-date flagging across MSAs, NDAs, and SOWs, anchored against your own contract playbook instead of a generic template.
Passports, licenses, and corporate registry documents extracted and cross-validated across a bundle before handoff to your sanctions and screening process.
Multi-document FNOL bundles classified and checked for completeness so an adjuster isn't the one sorting police reports from repair estimates.
Bills of lading, packing lists, and customs declarations extracted despite stamps, handwriting, and inconsistent table layouts that break classic OCR.
Intake forms, lab reports, and prior-authorization documents processed inside a HIPAA-aware pipeline built for PHI from the start
Their agile way of working, competence, and friendliness are impressive.December 7, 2024
Citrusbug Technolabs handles a data extraction project for an education company. They're also responsible for managing an AI generation project for the client.
Citrusbug Technolabs has laid out and followed a clear project outline and consistently met deadlines. The team communicates well and demonstrates a strong understanding of the client's vision and goals. Their agile approach, high level of competence, and personable approach stand out.
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Their communication and ability to capture exactly what I was asking for were impressive.November 14, 2024
Citrusbug Technolabs built an automated system for a veteran medical company. The system needed to collect and store user data, generate customized letters based on surveys, and use AI to summarize reviews.
Citrusbug Technolabs delivered a functional automated system that successfully organized the client's records and auto-generated statements. The team was quick to respond, addressed all roadblocks, and communicated effectively. Overall, their ability to understand the client's needs stood out.
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They were very calm and talented, and they did bring some of their own ideas.January 28, 2025
Citrusbug Technolabs has developed an MVP for a freight and capacity marketplace. The team has created three different interfaces for three market segments.
Citrusbug Technolabs has successfully developed an effective MVP. The team has delivered the project on time and almost within budget. Moreover, their responses have been timely. Overall, the team has been calm, talented, and innovative. This has led to the client's satisfaction.
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What stood out most about them was their personalized approach and sense of ownership in every aspect of the project."February 27, 2024
Citrusbug Technolabs built a Flutter cross-platform mobile app for an education company. They also developed two web apps and an admin panel and provided DevOps support for the client's infrastructure.
Citrusbug Technolabs' support successfully boosted the client's user engagement by 8x, resulting in an 85% retention rate. The reliable team demonstrated a dedication to delivering on time and promptly responding to concerns and adjustments. Their personalized, client-centric approach stood out.
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They didn't like the phrase "impossible to do", which made them very solution-oriented.February 19, 2024
A healthcare company hired Citrusbug Technolabs to design and develop a user-friendly application. The team provided tailored assistance to meet the client's requirements.
Citrusbug Technolabs successfully delivered an application that was ready for market. The team was highly professional and well-organized; they stayed focused throughout the engagement and were receptive to changes. Overall, they stood out for their trustworthiness and commitment to excellence.
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They're 100% on time and transparent throughout each stage of the project.February 24, 2024
A financial technology company has hired Citrusbug Technolabs to build a custom web application. The team handles the development and design of the app.
The project is ongoing, but the client has been satisfied with Citrusbug Technolabs' services. The team is very professional — they always respect timelines and provide regular updates to ensure everyone's on the same page. Also, their transparent approach makes them a trustworthy partner.
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"I do believe they are exceptional at where they stand."
Citrusbug Technolabs took on a mobile app development project for a Singapore-based development house. They were provided with UX designs to execute. Currently, they're finalizing and fine-tuning the product.
Citrusbug Technolabs contributed to the successful launch of the product, which has garnered over 10,000 users in just three months. The product has also received support from investors. They consistently followed-up with their progress and delivered at a speedy rate. The team is truly stellar.
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Swap Motor is a user-friendly online platform that makes selling used cars simple, secure, and hassle-free.
Finn is a car subscription platform that includes features such as login, registration, and management of car details, brands, and models.
Pave.ai is an AI-driven vehicle inspection platform that enables users to conduct accurate and comprehensive inspections using just a smartphone…
We inventory the document types you actually process, sample real files, and map where the current process breaks down, whether that's a specific supplier format, a language, or a document age nobody accounted for. This tells us which document type to pilot first.
We define the fields you need extracted, the validation rules each field needs to pass, and the confidence thresholds that decide what auto-posts versus what a person reviews. Model choice comes after the schema, not before it.
One document type goes live end-to-end against a real downstream system, not a demo environment. This is where accuracy gets measured against your actual documents, not a vendor's sample set.
We adjust the review threshold against pilot results until the auto-approval rate is defensible, not just high. A threshold that auto-approves everything isn't accuracy, it's risk you haven't priced yet.
Once the pilot holds, we wire the pipeline into your ERP, CRM, or internal systems and bring the next document type onto the same pipeline, so each addition gets faster than the last.
Every engagement starts with a document audit, so these ranges reflect what we've seen once a pilot is scoped, not a guess before we've looked at your documents.
| Document Complexity | Example Document Types | Typical Timeline | Engagement Model |
|---|---|---|---|
|
Structured, single template |
Standard invoices, fixed-format forms |
3–5 weeks |
Fixed-Price |
|
Semi-structured, variable layout |
Contracts, multi-supplier invoices, HR forms |
6–10 weeks |
Fixed-Price or Time and Material |
|
Unstructured, multi-document bundles |
Insurance claims, KYC bundles, medical records |
10–16 weeks |
Time and Material or Dedicated Team |
Most single-document-type pilots land between $10,000 and $650,000 depending on document variability and integration depth. Tell us what you're processing and we'll scope it against real numbers, not a rate card.
Documents carrying patient data, financial records, or identity information need more than accurate extraction. They need a pipeline that can prove what happened to every field, which is why we build audit logging and access controls in from the start rather than bolting them on before a client's first audit.
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Read Article →A platform gives you a fixed schema and pricing per page. Custom build gives you a schema mapped 1:1 to your systems and no per-page licensing, at the cost of a build timeline instead of a signup.
It depends on the field, not the document. We report accuracy per field at a defined confidence level against your own sample documents, not a single headline number that hides which fields are weaker.
Yes. Vision-based extraction reads layout and context directly instead of matching a fixed template, so format drift and handwritten fields like approval stamps get handled without a retraining cycle.
It gets routed to a reviewer with the source page and flagged value attached. You set the confidence threshold, so the split between auto-approved and reviewed fields is your call, not ours.
A single document type with a stable layout can go live in 3-5 weeks. Multi-document bundles like insurance claims or KYC packets typically run 10-16 weeks depending on integration depth.
Yes. We build the downstream connector as part of the pipeline, whether that's NetSuite, SAP, Salesforce, or an internal system, so extracted data writes back automatically.
We build inside your compliance requirements rather than claiming certification ourselves. For HIPAA workloads that means PHI-aware handling and access controls; for GDPR, data residency options you control.
Single-document-type pilots typically run $10,000 to $50,000 depending on document variability and how deep the integration goes. We scope exact numbers after the document audit, not before.
Yes. Full source code ownership transfers at delivery, and the pipeline runs on your infrastructure or cloud environment, not a platform you're locked into.
Vision-based extraction handles new layouts within a document type without retraining. A genuinely new document type gets onboarded onto the same pipeline, which is faster than the first one.
