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Pull the names, dates, and amounts out of your documents — into one searchable place.
Invoices, claims, statements, contracts, regulatory filings, support tickets. We build the pipelines that turn loose documents into clean, structured records your systems can search, check against each other, and act on — at the volumes that make it worth replacing or supporting manual review queues.
What this means for you
If your team spends its days keying details off invoices, claims, or contracts by hand, this is about pulling those details out automatically and dropping them straight into your systems — so people review only the tricky cases instead of typing every one.
Most 'unstructured data' is really useful detail nobody has pulled out yet.
The names, amounts, dates, and obligations are all in there. The work is pulling them out into something your systems can actually search and use.
We build pipelines that take a high volume of incoming documents — invoices, claims, vendor statements, contracts, identity-check packets, support tickets — and turn them into clean, structured records in your data platform (for example, a flexible table in Snowflake or a Delta table in Databricks). From there, the records can be checked against each other, scored, routed, paid, or sent to a person to review with all the right context already loaded.
What separates a demo from a production extraction pipeline.
Document AI demos are easy. Document AI in production is hard. These are the requirements that bridge the gap.
A representative document sample
Not the three pretty examples — the messy real-world ones. Bad scans, foreign-language pages, hand-written notes, multi-document PDFs, unusual layouts. The pipeline has to handle all of it (or hand off cleanly to a person when it can't).
Output shaped to fit the system that receives it
Pulling data out is only useful if it lands in the exact shape your accounts-payable, claims, contract-management, or CRM system expects. We design that shape first, and the AI instructions second.
A confidence rating on every detail
Every value the system pulls out comes with a confidence rating and a link back to the exact source page. Anything it's unsure about goes to a person to review. The system fails loudly and visibly, never silently.
Checking the data against your rules and records
Vendor IDs checked against your official vendor list. Amounts checked against purchase-order totals. Dates checked against contract start dates. Catches mistakes before they cost money.
A person in the loop, whose corrections make it smarter
When the system isn't sure, a reviewer fixes it in a simple screen we build — and those corrections feed back in to improve the next round. The system gets better over time.
A test set you trust
A hand-checked set of documents with known-correct answers, covering the tricky cases, re-run automatically on every change, with accuracy reported detail by detail. You can tell exactly which detail has gotten less accurate — not just that 'something' has.
Where document automation pays for itself fast.
High-volume, structured-enough, and expensive to do by hand.
Accounts payable: invoice and purchase-order matching
The totals, line items, tax, and vendor — pulled out, matched against the purchase order, checked against your official vendor list, and either routed for approval or paid straight through. Mid-sized accounts-payable teams typically handle 60–80% of invoices without a human touching them within a few months.
Claims intake and triage
First reports of a claim, supporting statements, photos. Read, checked, and routed to the right adjuster's queue with the context already loaded. Cuts turnaround time without removing the human decision.
Contract summarizing
The other party, the term, renewal dates, governing law, liability terms, key obligations — pulled into a structured form where legal can search across the whole portfolio at once instead of opening files one by one.
Identity checks, onboarding, and regulatory paperwork
Identity documents, ownership forms, financial statements. Read, cross-checked against outside sources, and flagged for an analyst when something doesn't add up. ('KYC' is the 'know your customer' checks regulators require.)
Signals document automation isn't the right next step.
Candid red flags.
The receiving system can't accept the data automatically
If your accounts-payable, claims, or contract system still needs a person to re-type the data anyway, automating the reading saves nothing. Fix that connection first — or pick a different starting point.
The volume doesn't justify the running cost
The AI calls, storage, monitoring, and review tools all cost real money every month. Below a few thousand documents a month, a well-designed manual process often wins. We'll say so.
No willingness to keep a person in the loop
Even the best pipelines miss sometimes. If the business won't accept any human review on the tricky cases, the project ends in either silent errors or constant escalations. Both are worse than a simple review queue.
No known-correct examples, and no plan to create any
Without a set of documents with known-correct answers, you can't measure accuracy and you can't improve. The first month is mostly building that test set; if that's not in scope, neither is the rest.
Three reversible phases.
Old process stays live until the new one matches accuracy and cost on the holdout.
See the full engagement modelDiscovery & sample analysis
Quoted by scopeA look at the documents coming in, samples of the tricky cases, a review of the system that will receive the data, and a recommendation on which kinds of documents are ready to automate and which aren't.
Pipeline build
Quoted by scopeThe AI instructions (or further training on your data), the output shape, the checking rules, the review screen, monitoring, and the test set. Built on Snowflake Cortex Document AI, Databricks, or platform-independent tools depending on your setup.
Rollout & enablement
Quoted by scopeA staged rollout by document type or business unit, comparing results against the manual process the whole way, and working side by side with your operations team on the written guides. Post-launch support is quoted based on your needs.
The questions we get most often.
A ready-made product or a custom pipeline?
Both have a place. A ready-made product (off-the-shelf document processing) wins when the document type is common — tax forms, US driver's licenses, standard invoice formats — and your accuracy bar is moderate. Custom wins when the documents are specific to your industry, the layouts vary, or you need a tight connection to your other systems and records. We'll recommend candidly — sometimes the readiness check ends with 'license this product and save the consulting fee.'
A leading general model, or one trained on your data?
We start with the leading general-purpose models (Claude, GPT-4-class, Gemini) almost every time — they're accurate enough that further-training a model on your own data rarely pays off at mid-market volume. That extra training makes sense mainly at very high volumes, with very specific document types, or under tight cost or speed limits the general models can't meet.
How do you handle sensitive and confidential documents?
The same way we handle the rest of our work — keeping sensitive personal data protected. For sensitive workloads we'll often run the AI entirely inside your own environment (Snowflake Cortex stays in-platform; Databricks Model Serving runs in your account; open models can run on Bedrock or Azure AI in your own tenant) so documents never leave your walls. Sensitive details are hidden before anything is logged, the review screen is locked down by role, and every extraction is recorded for audit.
What accuracy can we expect?
Varies by document type and by the specific detail. Simple, fixed details (totals, dates) routinely hit 98%+ on clean inputs. More complex details (line-item detail, contract obligations) usually land 85–95% on the first pass and improve as people correct them. We report accuracy detail by detail, not document by document — '95% accurate' across a document can hide one critical field that's only 70% right.
How this shows up in the work we ship.
Case studies and writing from engagements where this thinking actually meets production.
Adjacent topics in AI enablement.
Want to know if this is the right next move for your team?
Take the free AI & Data Readiness Assessment, or schedule 30 minutes to talk through your situation. Either way, you'll leave with a clearer picture of where you are and what the next steps should look like.