Less blank-page review
Reviewers start with a summary, checklist flags, and source sections instead of the raw document only.
Document review automation
Document review automation that gets files review-ready
Rebotify builds an AI employee that turns recurring documents into review-ready packets: missing information, source references, issue summaries, and the next decision prepared.
Mia is our AI employee. Email her — she’ll book your 15-minute call. That’s the demo.
48h
To first document checks from recent examples.
1
Document type or checklist before broad rollout.
3
Outputs: summary, flags, decision packet.
Get documents review-ready before the expert opens them.
The outcome is faster first-pass review with source references, missing information, and escalation already prepared.
Earlier missing-info requests
Gaps, unclear fields, and inconsistent evidence are caught before the reviewer meeting.
More consistent checks
The same review criteria are applied across recurring document types and routed for human sign-off.
THE ROLE
Rebotify takes the first pass on repeated review criteria: required fields, missing clauses, policy checks, consistency checks, and summary preparation. Human reviewers handle judgment and exceptions.
48-HOUR BUILD
What Rebotify ships first.
01
Review checklist
We turn your repeated criteria into a playbook the AI employee can apply consistently.
02
First-pass review
The employee reads the document, highlights relevant sections, and flags missing or conflicting information.
03
Reviewer summary
The human reviewer gets a concise packet with the source file, findings, and suggested next action.
04
Exception library
Repeated edge cases become new examples and rules in the playbook.
BUYING SIGNALS
Use it when the work is already costing time.
Documents arrive faster than reviewers can read
The work is not deeply strategic, but every file still needs a careful first pass before someone can decide.
Checklists live in people heads
Reviewers know what good looks like, but the rules are scattered across habits, examples, and old comments.
Missing information causes delay
The reviewer has to ask for the same details repeatedly before the real review can even start.
HUMAN CONTROL
The AI employee prepares the work. People keep judgment.
No final judgment by default
The employee prepares the review; a human still makes sensitive, legal, or commercial decisions.
Source references included
Flags link back to the document section or field that caused the issue.
Checklist versioning
As review standards change, the employee playbook changes with them.
FIRST WEEK PLAN
What a clean first week actually looks like.
Choose another workflow if
- One-off expert judgment where the criteria change every time.
- Legal review that needs a lawyer final decision but no preparatory checklist.
- Document stores with no consistent file access, naming, or review owner.
A good first week looks like
- Repeated criteria are turned into a checklist the AI employee can apply.
- Reviewers receive summaries, flags, and source references instead of raw files only.
- Missing information is caught before the review meeting or approval step.
EXAMPLE WORKFLOWS
The kind of work it takes on first.
Application review
Check required documents, missing fields, eligibility notes, and obvious inconsistencies before human review.
Compliance packet review
Summarize evidence, compare against a checklist, and flag missing controls or unclear claims.
Client document intake
Read incoming files, label them, identify gaps, and prepare the next request.
Runs inside
The employee works in the systems your team already uses — no new dashboard to log into.
- Google Drive
- SharePoint
- Dropbox
- Gmail
- Outlook
- Slack
- Notion
Controls that make this safe to run.
Document review automation needs source-grounded outputs, scope boundaries, and expert sign-off for high-risk decisions.
Safeguards we design around
- Show exact section or clause references behind each flag or summary.
- Keep expert review, escalation, and override points visible.
- Define supported document types, playbook rules, retention, and access controls.
Claim boundary
We do not claim guaranteed compliance, 100% accuracy, no missed issues, or replacement of lawyers or compliance officers.
Reference point
ISO 37301 compliance management systems
ISO 37301 covers establishing, evaluating, maintaining, and improving compliance management systems.
Reference point
NIST AI Risk Management Framework
NIST AI RMF describes trustworthiness factors such as reliability, transparency, privacy, and security.
Reference point
ABA 2024 AI TechReport
The ABA reports accuracy and data privacy/security are major AI concerns among legal-technology respondents.
FAQ
Final buying questions.
What will Rebotify take off the team first?
Rebotify takes the first pass on repeated review criteria: required fields, missing clauses, policy checks, consistency checks, and summary preparation. Human reviewers handle judgment and exceptions.
Who is Document review automation best for?
Document review automation is best for operations, compliance, legal ops, and service teams with a repeated workflow, a clear human owner, and enough examples to teach the AI employee what good work looks like.
What does Rebotify deliver in the first 48 hours?
Rebotify maps the workflow, writes the first operating playbook, connects the minimum tools, and puts useful drafts, checks, or summaries into a human approval queue.
Do humans still approve the work?
Yes. Rebotify normally starts with human approval for customer-facing, financial, legal, or policy-sensitive actions. The AI employee prepares the work and escalates uncertainty.
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48-HOUR START
Bring us one workflow. Leave with a 48-hour plan.
Email Mia
Mia is our AI employee. Email her — she’ll book your 15-minute call. That’s the demo.