Turn piles of documents into data you can trust.
We build systems that read invoices, forms, contracts, scans and emails, pull out the fields you need and show exactly where each value came from. Anything unclear goes to a person. The work is built in Lahore for teams that drown in paperwork.
What is document intelligence?
Document intelligence is software that reads documents such as invoices, forms, contracts and scans, then returns structured data instead of raw text. It combines OCR for turning images into characters with a language or vision model that understands layout and meaning. Good systems define the fields up front, keep the page and region each value came from, and send low-confidence results to a reviewer, so the output is traceable and safe to feed into your accounts or CRM.
Plain OCR only gives you text. Document intelligence gives you checked fields with evidence.
Documents that waste the most staff time.
If a person re-types it, we can usually teach a system to read it. These are the document families we are asked about most. If your files are mostly clean digital PDFs, the work is simpler and cheaper. If they are photographed receipts or old scans, we plan for more cleaning and review.
Invoices and bills
Supplier names, dates, line items, tax and totals are pulled out and matched against purchase orders before they reach your accounts.
Forms and applications
Printed and filled-in forms become records in your database, with checkboxes, dates and addresses normalised.
Contracts and agreements
Parties, dates, renewal terms and key clauses are located and quoted so a reviewer can jump straight to them.
Identity and compliance papers
Required fields are checked for presence and consistency, with sensitive data handled under strict access rules.
Scans and photos
Skewed, low-light or multi-page scans are cleaned up first, because good extraction starts with readable images.
Emails with attachments
Messages and their files are classified, split into separate documents and sent to the right process.
The path from a scan to a database row.
Reading a document well is a series of small, checkable steps rather than one clever prompt. Splitting the work this way also means one weak stage can be improved without rebuilding the others.
Stages in the pipeline
- Prepare the image. Rotation, de-skewing, contrast and page splitting are handled first, since bad input is the main cause of bad output.
- Recognise the text. OCR converts pixels to characters and records where each word sits on the page, which later makes evidence possible.
- Understand the layout. A model identifies tables, headings and key-value pairs, and decides what type of document it is looking at.
- Extract and validate. Values are placed into your schema, then checked: totals add up, dates are valid and required fields exist.
Extracted data can flow on into AI workflow automation for routing and approvals. We keep every intermediate result, such as the cleaned image and the raw OCR text, so a puzzling output can be traced back through each stage.
OCR, vision model or both: which reads better?
We choose the method per document type after testing it on your real files, not on a polished demo. The right answer is often a mix, with cheap rules for predictable files and a model for the rest.
| Criteria | Classic OCR with rules | Vision or language model | Combined approach |
|---|---|---|---|
| Fixed, repeated templates | Fast and cheap | Works, costs more | Rarely needed |
| Many layouts, one document type | Breaks on changes | Handles variety | Often best |
| Handwriting and poor scans | Weak results | Better, still uncertain | Review queue needed |
| Audit and traceability | Position of each word | Needs added evidence | Evidence kept |
| Running cost at high volume | Lowest | Highest | Medium |
Agree the fields before any code is written.
Most extraction projects fail because nobody wrote down what a correct answer looks like. We start with that.
The field contract
- Names and types. Every field has a name, a type such as date, amount or text, and a rule for when it is required.
- Normal form. Dates, currencies and phone numbers are converted to one format so your systems can use them directly.
- Cross-field rules. Subtotals must match totals and end dates must follow start dates. A failed rule sends the document for review.
- Examples and thresholds. We collect real samples, including bad ones, and agree what accuracy and review rate are acceptable.
This contract becomes the test suite we run after every change to the pipeline. Without it, nobody can say whether a change made the system better or worse.
A review screen that makes checking quick.
No extraction is perfect. A good review screen turns the rare doubtful case into a ten second task instead of a lost afternoon. The share of documents that need review falls as rules improve, and we report it so you can see the trend.
What the reviewer sees
- The source beside the value. The extracted field sits next to a highlighted view of the exact region on the page it came from.
- Only the uncertain fields. Confident values pass automatically. The reviewer looks at what the system flagged and nothing else.
- One-click corrections. A fix is saved to the record and logged, and corrections are used to improve rules and test cases.
- Clear ownership. Each queue has a named owner and a time target, so uncertain documents do not sit unnoticed.
Reviewed corrections are also the best training data you will ever have for the next version.
From sample files to a running extractor.
The order matters. Samples come first, automation last. At every stage you can see real results on your own files, not slides.
- 01
Collect samples
You give us a spread of real documents, good and bad, with the correct answers filled in for a subset.
- 02
Define the schema
We write the field contract, validation rules and the review conditions, and you approve it.
- 03
Build and score
The extractor is built and scored against your samples, field by field, so accuracy is a measurement.
- 04
Add review and delivery
A review queue is added, and results are sent to your accounting tool, CRM, database or spreadsheet.
- 05
Run in parallel
The system runs beside your manual process for a period, and we compare the two until differences are understood.
- 06
Go live and monitor
You receive logs, alerts and a regression suite. New document layouts are added through a defined process.
Handling sensitive files without losing control.
Documents often hold names, bank details and signatures. We design for that from the first day, and we say plainly what each choice costs. If the data is especially sensitive, we may recommend a smaller scope first.
What shapes cost and risk
- Document volume and variety. More layouts mean more testing. Volume sets the monthly model and OCR bill, so we pick the cheapest method that passes your tests.
- Where processing happens. Hosted providers are quicker to start. A self-hosted model keeps files inside your environment at the price of more setup.
- Access control. Permissions are checked before text reaches any model, never by asking a model to refuse.
- Retention. Files and extracted data are kept only as long as you need them, with deletion built into the process.
For private deployments, read about self-hosted AI models. Contracts and personal records may also carry legal duties in your country, so we document where data goes and who can see it.
Document intelligence, your questions.
Straight answers to what clients ask before they start.
How accurate is extraction from scanned documents?
It depends on scan quality, layout variety and the field. We measure accuracy per field on your own samples and set review rules so uncertain values never pass silently.
Can it read handwriting?
Sometimes. Neat block handwriting can be read, but cursive and poor scans stay uncertain. We route those fields to review and tell you the expected review rate before you commit.
Which languages are supported?
Most common languages work with current OCR and models, including Urdu and Arabic scripts in many cases. We test your actual files, since quality varies by script and print condition.
Do I have to use a cloud AI service?
No. Cloud services are convenient, but a self-hosted pipeline is possible when documents must not leave your servers. The trade-off is setup effort and hardware.
How do I know a value is correct?
Each field keeps its source page and region, passes validation rules and, if confidence is low, is checked by a person. You can always trace a number back to the original.
Can it connect to my accounting or CRM tool?
Yes. Results can be pushed through an API, a webhook, a scheduled export or a workflow in n8n or Make, with duplicate protection so the same invoice is never entered twice.
What happens when a new document layout appears?
The system flags it as unfamiliar, sends it for review and logs it. We then add that layout to the test suite and adjust the extraction rules.
Is this the same as a chatbot over my documents?
No. A chatbot answers questions from documents and is covered by our retrieval work. This service produces structured records from each document as it arrives.
Send us ten typical documents.
Share a small, anonymised sample of the files that cost you the most time. We will tell you what can be read reliably, what needs review and how the project would be run.