Shimira ITAI document processing
Turn invoices, contracts, and forms into data you can use.
Somewhere in your business, people are reading documents and typing what they find into another system. Invoices into accounting. Contract terms into a spreadsheet. Application forms into a database. It's slow, it's expensive, and it's the kind of work people make mistakes on precisely because it's dull.
This is the most reliable use of AI in business software today, because the task is narrow and the output is checkable. We build extraction systems that handle the documents they can handle confidently, flag the rest for a human, and prove their accuracy on your real documents before you commit to a build.
What matters here
What a document automation system does
Reads what you actually receive
PDFs, scans, photos taken on a phone, and email attachments, including the badly aligned ones, not just clean digital documents.
Extracts the fields you care about
Line items, dates, totals, parties, terms, reference numbers, structured exactly the way your downstream system expects them.
Knows when it isn't sure
Confidence scoring routes uncertain documents to a person instead of guessing. This is the difference between useful automation and an expensive mess.
Gives reviewers a fast screen
Document on one side, extracted fields on the other, corrections in a couple of clicks. Those corrections also become training data.
Pushes data where it belongs
Into your accounting system, ERP, CRM, or database, so nobody re-types anything.
- Python
- Node.js
- TypeScript
- PostgreSQL
- OpenAI
- AWS
We've shipped in this space
SnuggleRead.com
An AI pipeline running in production: generation inside strict guardrails and a fully automated document pipeline producing print-ready files without a person in the loop.
What goes wrong
Where document AI projects go wrong
Chasing 100% accuracy
No system reads every document perfectly, and pursuing it wastes budget. The right design handles the confident majority automatically and routes the rest to a person, which captures most of the saving at a fraction of the cost.
No test set, so nobody knows if it works
Without a set of real documents with known correct answers, accuracy is an opinion. Building that set is the first thing we do, and it's the most valuable hour your team will spend on the project.
Only tested on tidy documents
Demos use clean PDFs. Reality includes phone photos, scans at an angle, and a supplier whose layout changed last month. We test on the messy ones deliberately.
No plan for being wrong
What happens when the AI misreads a total? If the answer is “it flows into accounting unnoticed”, the system shouldn't ship. Review steps go where errors are expensive.
Ignoring what the documents contain
Invoices and forms hold personal and commercial data. Where it's processed and how long it's kept are design decisions, not afterthoughts.
How it runs
How we build it
- 01
Feasibility on your documents
1–2 weeksWe build a test set from your real documents, measure what accuracy is achievable per field, and estimate the running cost. You get a go or no-go with numbers, before the build budget is committed.
- 02
Pilot on one document type
3–6 weeksA working extractor for your highest-volume document, with the review screen, measured against the test set you approved.
- 03
Production build
varies by scopeIntegration with your systems, confidence routing, audit trail, monitoring, and the remaining document types.
- 04
Tune on real volume
ongoingReviewer corrections feed back in, so the share handled automatically rises over time.
Timelines & pricing
Honest ranges, before you commit.
| Tier | What's included | Timeline | Typical cost |
|---|---|---|---|
| Feasibility assessment | Test set on your documents, achievable accuracy per field, running-cost estimate | 1–2 weeks | $3,000 – $8,000 |
| Single document type | One extractor, review screen, one integration | 4–8 weeks | $15,000 – $45,000 |
| Full automation pipeline | Multiple document types, confidence routing, audit trail, several integrations | 2–4 months | $30,000 – $90,000 |
Running costs are billed per document processed and depend on document length and model choice. Our AI app development cost guide shows how to estimate them, and works through the payback maths for a document-processing project.
Before you choose
Is your document process a good fit?
The strongest candidates look like this:
- People spend hours a week reading documents and typing what they find elsewhere.
- The documents are repetitive in purpose, even if the layouts vary.
- You can supply 50–200 real examples with the correct answers.
- Someone in the business can judge whether an extraction is right.
- A wrong value would be caught by a person before it does damage, or you're willing to build that check.
- The volume is high enough that saved hours outweigh the running cost.
If most of these are true, feasibility will tell you the rest in two weeks.
How you work with us
Three ways to engage.
Fixed price
Prototypes from $7,500
Production MVPs from $25,000
You know what you need built. We scope it, quote one price, and deliver against it, no meter running.
- One agreed price, agreed before we start
- Milestone-based payments tied to what you can see
- Change requests priced openly, never assumed
Time & materials
from $28 / hour
Blended ~$32; senior specialists to ~$45
For work that changes as you learn. You pay for the hours spent, see exactly where they go, and can steer week to week.
- Billed on real, logged hours, reviewed with you
- Reprioritise or change direction any sprint
- Start small, scale the team as it proves out
Dedicated team
from $2,800 / month
Per developer, full-time, see the rate card
Developers who work only on your product, full-time, as an extension of your team, the offshore model most of our long-term clients settle into.
- The same people, month after month
- Your tools, your standups, your roadmap
- Roughly half the loaded cost of a local hire
Full rate card and what's included on the pricing page. Or our wider AI development service.
Questions
Before you get in touch.
That depends on your documents, which is exactly why we measure it on your real examples before quoting a build. What we can say generally: accuracy varies per field, clean recurring layouts do better than handwriting or poor scans, and the useful design routes low-confidence documents to a person rather than guessing.
A feasibility assessment is $3,000–$8,000. A single document type in production runs $15,000–$45,000, and a full pipeline with several document types and integrations runs $30,000–$90,000, plus a per-document running cost. See the AI cost guide for how the running cost is calculated.
In our experience it changes what they do rather than removing them: less typing, more reviewing exceptions and handling the cases that need judgment. The systems that work best are built around that review step, not around removing it.
We build on enterprise API tiers where providers don't train on your data, and where documents can't leave your environment at all we use open-weight models inside your own infrastructure. Data handling is agreed in writing before any build.
Yes. Extracted data is pushed into your accounting system, ERP, CRM, or database. Each integration typically adds $3,000–$10,000 depending on the quality of that system's API.
Layout changes are normal, and systems built around fixed templates break when they happen. We build for variation, and monitoring flags an accuracy drop so you find out from a dashboard rather than from a customer.
Cost guides
Budgeting for this?
Real 2026 numbers, worked examples, and the hidden costs most quotes leave out.
How much does AI app development cost in 2026?
Build ranges by use case, a worked monthly model-cost example, RAG vs. fine-tuning, ROI maths, and the hidden costs most AI quotes skip.
Read the guide →How much does custom software development cost in 2026?
Real 2026 numbers: cost by project type, hourly rates across every major region, the hidden costs quotes leave out, and a free calculator to model your own estimate.
Read the guide →Not sure what you need yet? That's the usual starting point.
Tell us the problem in your own words. We'll scope it with you and put the plan in writing, free, and yours to keep either way.