Document AI is how you turn years of locked-up contracts, policies, and reports into a system your team can actually ask a question — and get a verified answer with a source in seconds. PieSoft.ai has been building AI systems over structured and unstructured documents for 12 years across 367 projects. We know what works.
Most organizations are drowning in unstructured documents. Contracts live in shared drives. Policies are buried in outdated SharePoint folders. Compliance guidelines are PDFs nobody has time to read. Clinical guidelines are printed binders. Every time a team member needs a specific answer, someone spends 20 to 40 minutes digging — or just guesses.
Manual document review isn't just slow — it's inconsistent. Two people reading the same contract will extract different facts. Two calls about the same clinical policy will end with different answers. Document AI fixes that by reading every document the same way, every time, and showing its work.
Document AI isn't a single technology — it's a pipeline. We design each stage to fit your documents, your access rules, and the questions your team actually asks.
PDFs, Word files, scanned images, emails, SharePoint pages, EHR exports — we ingest them, extract clean text, and preserve structure. Scanned documents go through OCR; tables and headers are preserved, not flattened.
We chunk documents intelligently — not by arbitrary word count, but by semantic units. Each chunk is embedded and indexed so that when your team asks a question, the system finds the passage with the answer, not just the document with the keywords.
Not every user should see every document. We build access control into the retrieval layer itself — not as a UI filter that can be bypassed, but as a hard gate. A care coordinator sees their member's records. An auditor sees the audit trail. Neither sees the other's data.
The system synthesizes an answer from the retrieved passages and cites every claim back to the source paragraph. Your team member reads the answer, sees the source link, and can click to verify. No black-box outputs. No guesses passed off as facts.
Beyond Q&A, we build extraction pipelines that read a document and output a structured record: contract dates, liability caps, renewal clauses, prior-auth criteria, claim amounts. The output lands in your system of record — no copy-paste, no manual re-entry.
The system tracks when answers are useful, when they're corrected, and when users need to dig deeper. That signal feeds back into retrieval tuning. Over time, the system gets better at your specific documents and your specific questions.
These aren't theoretical. Each pattern below maps directly to an engagement we've delivered in the last 24 months.
Legal and procurement teams typically spend 3–6 hours reviewing a single vendor contract — checking liability caps, indemnification clauses, auto-renewal dates, and non-standard terms. A senior attorney costs $300–$600 per hour. That math gets painful fast when you're doing 40 contracts a quarter.
Our document AI contract review system reads each contract, extracts the fields that matter to your risk framework, flags clauses that deviate from your standard positions, and surfaces the top three risks with the source language quoted verbatim. The attorney still reviews — but they spend 45 minutes on what the AI flagged, not 4 hours reading everything.
Integrates with: SharePoint, Salesforce, Ironclad, iManage, or your existing contract repository. No migration required.
Every regulated industry has the same problem: policies change, training doesn't keep up, and staff either guess or escalate. In healthcare, that means prior-auth answers vary by coordinator. In insurance, it means adjusters interpret the same clause three different ways. In financial services, it means compliance questions go to legal even when the answer is already in the manual.
We build internal Q&A systems over your policy and procedure library. A team member types a plain-English question — "What's the prior auth requirement for this CPT code in Texas?" or "What's our SLA for a business interruption claim under a BOP policy?" — and gets back an answer sourced to the exact paragraph. If the member doesn't have permission to see a certain policy tier, those passages are excluded before the answer is formed.
The result: consistent answers, faster resolution, and an audit trail that shows which policy guided each response. See how we've applied this in healthcare.
Weekly ops reports, quarterly business reviews, claims summaries, patient discharge summaries — these take hours to assemble and often arrive late, heavily templated, or incomplete. The data exists. The AI can read it and write the narrative. The analyst reviews and edits. That's the right division of labor.
We build report generation pipelines that pull from your data sources — structured databases, raw spreadsheets, EHR exports, CRM data — read the numbers, identify trends, flag anomalies, and draft the narrative sections in your organization's voice and format. The output lands in a template-ready document that your team can review, edit, and publish. What used to take an analyst a day takes them 40 minutes.
This pairs well with our workflow automation service when the report generation is part of a larger review and approval process.
A regional managed care organization came to us with a familiar problem: their care coordinators were handling 60–80 calls per day about prior authorization requirements, member benefit eligibility, and care management protocols. Every answer required looking something up — and the "something" was spread across a 900-page clinical guidelines manual, a 400-page member contract library, and a legacy prior-auth system that required a separate login.
Coordinators were spending 12–18 minutes per call just finding the right policy passage. When they couldn't find it fast enough, they'd give a best-guess answer — introducing the exact compliance risk the organization most wanted to avoid.
We built a permission-aware document AI system over all three document sources. Coordinators now type a plain-English question into a sidebar panel within their existing CRM. The system retrieves the relevant clinical guideline or benefit clause, synthesizes a direct answer, and displays the source passage with a link to the full document — all in under 4 seconds.
Every answer is 100% sourced. If the system can't find a passage that directly supports an answer, it says so — and escalates to a clinical supervisor rather than guessing. Auditors reviewed the system logs after 90 days and found zero unsupported answers in 14,000 recorded queries. The compliance team moved from "AI is a risk" to "AI is how we reduce risk."
Industry: Managed Care · Bethlehem PA engagement · 2025
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The minimum worthwhile scenario is usually 500+ documents or a corpus that would take a person more than a week to read cover-to-cover. Below that, a well-structured wiki often serves better.
If your team asks variants of the same 50 questions 100 times a week, the economics are obvious. If every question is truly novel and requires expert judgment, the AI is a research assistant — still valuable, but different ROI.
If wrong answers have consequences — a compliance finding, a denied claim, a contractual breach — you need sourced answers, not confident-sounding AI summaries. That's exactly what we build.
Healthcare, insurance, legal, and finance all operate under access rules that can't be bolt-on features. We design permission-aware retrieval into the foundation — not the UI.
Not sure if your situation qualifies? Start with our AI Opportunity Assessment. We'll tell you honestly whether document AI is the right call — or whether something simpler would get you to the same outcome faster.
Book a 30-minute call. We'll ask what your team is searching for, show you what document AI looks like on your actual use case, and tell you honestly whether it will move the needle — or whether a simpler fix gets you there first.
Book a consultation →