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Citrusbug builds document automation systems that go past text capture. Each system reads context, sorts documents by type, moves data into the right downstream workflow, and keeps a record of every decision it made along the way.
Multimodal models interpret a document’s meaning, not just its layout, so a reformatted invoice or a scanned form with a coffee stain doesn’t break extraction the way a rules-based template does.
Incoming documents get classified by type and pushed into the right queue, whether that’s accounts payable, claims intake, or compliance review, without a human triaging the inbox first.
Every extracted field carries a confidence score. Low-confidence fields get routed to human review instead of silently passing through as a wrong number.
Every extraction, classification, and routing decision is logged, giving compliance teams a record they can actually produce during an audit.
Send us three real examples and we'll show you what accurate extraction looks like.
Talk to an EngineerOff-the-shelf models are tuned for the average document, not your invoice format, your claim forms, or your engineering drawings.
Consumption-based pricing means your automation gets more expensive exactly when it’s saving you the most time.
A vendor redesigns their form and your extraction accuracy drops overnight, with no visibility into why.
Generic platforms rarely give you a defensible audit trail for regulated document types like medical records or loan files.
We design document automation systems that combine multimodal large language models for document-level reasoning with a multi-agent pipeline, where a dedicated extraction agent, a validation agent, and a cross-reference agent each handle one part of the job before an orchestration layer reconciles their outputs. This replaces the single-pass, rules-based extraction that older OCR tools still rely on, and it's built alongside our broader natural language processing and document understanding work so the reasoning layer stays consistent across your document set.
Every document category needs a different extraction approach. Here's what we've built systems for, and what typically breaks generic tools on each one.
Line-item extraction, vendor matching, and automated three-way matching against purchase orders, cutting manual reconciliation out of your accounts payable workflow entirely.
Clause identification, obligation tracking, and renewal date extraction pulled consistently across contracts that rarely share the same template or structure.
Structured extraction from clinical notes, lab results, and handwritten intake forms, built to handle the inconsistency real medical documentation actually has.
Identity verification, income documentation, and cross-document consistency checks that flag mismatches before they become a compliance problem down the line.
Dimension, tolerance, and annotation extraction from technical drawings, replacing the manual review cycle engineering teams still run by hand today.
Bill of lading, customs forms, and packing list data reconciled into a single record, regardless of which format each document originally arrived in.
Document automation isn’t one build. A hospital finance team needs claim forms and clinical notes read without mishandling protected health information, which is where this connects to revenue cycle and claims documentation work we’ve done separately. A lender needs KYC and loan packets cross-checked against fraud signals in minutes, not days.
Real estate brokerages need title and closing documents reconciled across a dozen inconsistent formats from different counties. Insurance teams need first-notice-of-loss forms triaged the moment they arrive, not batched overnight. Logistics teams need customs paperwork read the same way whether it shows up as a scan, a photo from a driver’s phone, or a clean PDF.
We review a real sample of your documents, map the fields you actually need extracted, and identify where a generic model is likely to fail against your specific formats before any code gets written.
Before committing to a full build, we run a working extraction pass against your own documents so you can see actual accuracy numbers, not a vendor's marketing benchmark from someone else's dataset.
We design the extraction, validation, and orchestration agents around your document types, deciding where multimodal reasoning is worth the cost and where a lighter-weight approach is enough.
The system gets wired into your ERP, CRM, or core platform so extracted data lands where your team already works instead of sitting in a separate dashboard nobody checks.
We test against a held-out set of your real documents, not a synthetic benchmark, and tune the confidence thresholds until the exception queue matches what your team can actually review.
Once live, we monitor extraction accuracy over time, since document formats drift, and we retrain or adjust before accuracy quietly degrades. This is also where a lot of clients replace an older RPA-driven workflow automation layer that was brittle against format changes.
An intelligent document automation system that doesn’t connect to the rest of your stack just becomes another tab someone has to check. We build the integration layer alongside the extraction models, so structured data lands directly in your ERP and CRM systems, your EHR, or your core banking platform, in whatever format that system already expects.
ERP Systems — Extracted invoice, PO, and inventory data posts directly into your existing financial system without a manual export step.
CRM Platforms — Contract terms, renewal dates, and customer document data sync into the records your sales and account teams already work from.
EHR & Practice Management — Clinical documentation and claims data flow into your existing healthcare records system with the access controls it requires.
Core Banking & Loan Origination — KYC and loan document data feeds directly into underwriting and origination systems, cutting manual re-entry out of the process.
Cloud Storage & DMS — Processed documents and structured outputs land in the document management system your compliance team already audits against.
Document automation pricing depends almost entirely on how messy your documents are, not just how many you have. Here's roughly what to expect.
| Document Type | Complexity | Estimated Cost | Timeline |
|---|---|---|---|
|
Structured documents (invoices, receipts, forms) |
Low |
$8,000-$20,000 |
4-6 weeks |
|
Semi-structured documents (contracts, claims, applications) |
Medium |
$20,000-$45,000 |
6-10 weeks |
|
Unstructured & mixed-format documents (medical records, engineering drawings, handwritten forms) |
High |
$45,000-$90,000+ |
10-16 weeks |
Most builds range from $8,000 for a single structured-document workflow to $90,000+ for multi-format systems handling medical records or engineering drawings. Final cost depends on document variety, volume, and integration depth.
A focused test against your real documents to validate extraction accuracy before any commitment to a full build.
A production system covering one or two document types, fully integrated into a single downstream system.
A multi-document, multi-system deployment covering your full document workflow across departments.
Intelligent document automation touching medical records, loan files, or claims data isn't just an engineering problem. We build with the audit trail, access controls, and data handling regulated document types actually require, not bolted on after the fact.
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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We test extraction accuracy against your actual documents during a proof of concept, so you know what you’re building before you pay for the full system.
You get a system you own outright, not a consumption-priced platform that gets more expensive as your document volume grows.
Requirements, document samples, and field mapping happen before development starts, not as a mid-build correction.
Source code and trained models transfer to you at delivery. Nothing stays locked inside a vendor’s platform.
We architect the extraction pipeline around realistic document volumes, so you’re not paying for infrastructure sized for traffic you don’t have.
L1/L2/L3 support tiers are available once your system is live, for teams that want ongoing coverage without hiring in-house.
Designed for healthcare providers and researchers, the platform enhances early detection of neurological conditions.
Exii.co recommendation engine personalizes online shopping experiences, enhancing customer engagement and increasing sales.
This AI tool provides real-time, accurate renovation cost estimates for homeowners, contractors, investors, and insurance companies.
A custom build is trained and validated against your actual documents and priced once, not per document. Off-the-shelf platforms use generic models and consumption-based pricing that scales with your volume.
Most projects run 4-16 weeks depending on document complexity and how many systems it integrates with. Structured document types move faster than mixed-format or handwritten content.
We validate accuracy during the proof-of-concept stage before full development starts, so this gets caught early. Post-launch, we monitor and retrain as document formats drift.
It depends on your setup. Many clients replace brittle RPA-plus-OCR stacks entirely, while others keep RPA for task automation and route document understanding through the new system.
You do, in full. There's no vendor lock-in and no ongoing license fee tied to the extraction models themselves.
It depends on document type. Medical records need HIPAA-aligned handling, EU-based data needs GDPR compliance, and document-driven credit or claims decisions may fall under EU AI Act high-risk obligations.
Yes, including scans, photos of physical documents, and handwritten forms. Multimodal models handle these with lower accuracy than clean digital PDFs, which is why we test against your real samples first.
No. Your documents and any fine-tuned models are used exclusively for your system and are never shared across client projects.
