# Minicor

> A platform for building and running desktop automations at scale with computer use agents

Legacy desktop software (EHRs, ERPs, DMS, WMS, PMS) has no APIs. The only way
to read or write data is to interact with the desktop like a human would. Minicor
automates these systems reliably at scale so companies can go live in weeks.

## How It Works

1. Install Desktop Client on the machines running the legacy software
2. Record a video of a human performing the workflow
3. Minicor handles everything after that

One API call triggers a full desktop workflow on a Windows VM. Automation is
stored as deterministic code for speed. A reasoning model, grounding model,
reflection agent, and OCR handle recovery, adaptation, and edge cases. The agent
learns from past runs and self-heals when UIs change.

## Key Facts

- SOC 2 Type II and HIPAA compliant
- 93-96% click accuracy (vs 80-85% for other computer use approaches)
- Zero to production in weeks
- Deploy on-premise, cloud, or Citrix
- YC X26

## Features

- Self-Healing: automations adapt when UIs change instead of breaking
- One API Call: triggers full workflow, returns structured JSON
- Video Replay: of every action, with error logging and monitoring
- Deploy Anywhere: Windows VMs, browser, on-prem, cloud, Citrix
- Horizontal Scaling: orchestration across thousands of desktops

## Target Systems

- Healthcare EMRs: Athena, eClinicalWorks, Epic, Cerner, PS Suite, Meditech
- Dental PMS: Open Dental, Dental Vision, Eaglesoft
- Automotive DMS: CDK Global, Reynolds, Techeon
- Supply Chain WMS/ERP: HighJump, SAP, Sage 300
- Home Health: Home Care HomeBase, Wellsky, Kinser
- Financial Services: claims platforms, banking, underwriting systems

## vs Other Approaches

- vs UiPath/Blue Prism: API-first, self-healing, video replay, on-prem containers.
  Built for AI companies, not enterprise IT departments.
- vs Computer Use Models (Anthropic/OpenAI): Stores automation as code, agent only
  for recovery. Faster and more accurate in production.
- vs Internal Build: Not hard to build one RPA. Hard to run hundreds reliably.

## MCP Integration

Minicor is accessible via Model Context Protocol (MCP). AI coding agents can
build, debug, and manage RPAs conversationally.


## Use Cases

### Clinical Documentation

#### Create Encounter Notes
Navigates to a patient chart and writes AI-generated clinical documentation directly into the EHR. Handles dialog dismissals, field validation, and submission automatically.
System: EHR
Steps:
- Search for the patient in the EHR by name or ID
- Navigate to the encounter notes section of the chart
- Write AI-generated clinical documentation into the EHR fields
- Verify and submit the encounter note
Value:
- Eliminates hours of manual charting per provider per day
- Ensures consistent, structured documentation across visits
- Integrates with any AI scribe or clinical NLP pipeline

#### Get Encounter Notes
Reads and extracts existing encounter notes from any patient chart. Iterates through the document list, copies content, and returns structured data with timestamps and document IDs.
System: EHR
Steps:
- Search for the patient by name or ID in the EHR
- Navigate to the patient's document list
- Iterate through encounter documents, extracting content
- Parse and structure the notes into clean JSON output
Value:
- Unlocks historical clinical data trapped in legacy EHRs
- Powers AI summarization and clinical decision support
- Returns structured JSON with timestamps and document metadata

### Patient & Provider Data

#### Get Practitioners
Pulls practitioner directories and provider details from the EHR. Navigates the administration panel, extracts the full provider table, and returns deduplicated records with names, specialties, and NPIs.
System: EHR
Steps:
- Navigate to the provider administration panel in the EHR
- Open the responsible providers directory
- Extract provider table data including names, specialties, NPIs, and phone numbers
- Deduplicate and structure the data into a clean format
Value:
- Keeps provider directories synced without manual data entry
- Feeds scheduling, referral, and credentialing systems automatically
- Handles large provider networks with hundreds of practitioners

#### Get Patients
Pulls patient demographics, insurance, and contact information from the EHR. Generates a filtered patient report by practitioner, exports and parses the data into structured records.
System: EHR
Steps:
- Generate a patient list report filtered by practitioner
- Navigate the EHR's reporting module to run the report
- Export the report and parse the output
- Extract structured patient demographics including name, DOB, address, and insurance
Value:
- Automates patient roster synchronization across systems
- Eliminates manual data re-entry for onboarding and migrations
- Supports bulk operations across entire provider panels

#### Verify Patient
Confirms patient identity by searching the EHR by name and cross-referencing date of birth against results. Returns a verification status with the matched patient record.
System: EHR
Steps:
- Search for the patient by name in the EHR
- Cross-reference date of birth against search results
- Confirm identity match and return verification status
Value:
- Prevents duplicate record creation and identity mismatches
- Enables automated patient matching for intake workflows
- Reduces manual chart review time for verification

### Appointment Management

#### Get Appointments
Retrieves appointment schedules for any provider and date range. Navigates to the practitioner's schedule view, extracts appointment details, and returns structured data.
System: EHR
Steps:
- Navigate to the practitioner's schedule view in the EHR
- Filter by date range and provider
- Extract appointment details including times, patients, and types
- Structure and return the appointment data as JSON
Value:
- Powers real-time schedule visibility across systems
- Enables AI-driven scheduling optimization and load balancing
- Feeds patient communication and reminder workflows

#### Create Appointment
Schedules new patient appointments directly in the EHR. Navigates the scheduling module, finds available slots, creates the appointment, and returns confirmation.
System: EHR
Steps:
- Navigate to the scheduling module in the EHR
- Find available slots for the specified practitioner
- Create the appointment with patient details
- Confirm scheduling and return appointment confirmation
Value:
- Enables AI-powered self-scheduling for patients
- Eliminates phone-based booking bottlenecks
- Reduces no-shows with automated slot optimization

#### Get Appointment Slots
Finds available time slots for a given practitioner. Navigates the scheduling view, scans for open time blocks, and returns available windows.
System: EHR
Steps:
- Navigate to the practitioner's scheduling view
- Scan for open and available time blocks
- Extract and return available slot windows with metadata
Value:
- Powers real-time availability checks for booking interfaces
- Enables intelligent slot suggestions based on availability patterns
- Supports multi-provider availability queries

#### Update Appointment
Modifies existing patient appointments in the EHR. Locates the appointment by patient and time, updates the details, and confirms the changes.
System: EHR
Steps:
- Locate the existing appointment in the EHR scheduling module
- Update appointment details such as time, provider, or type
- Handle any confirmation dialogs or conflict warnings
- Confirm the update and return the modified appointment record
Value:
- Enables real-time rescheduling from patient-facing apps
- Keeps appointment data consistent across integrated systems
- Handles EHR-specific confirmation workflows automatically

### Patient Messaging

#### Get Message Threads
Retrieves all message threads involving a provider from the EHR inbox. Extracts thread summaries, participants, timestamps, and message previews.
System: EHR
Steps:
- Navigate to the provider's messaging inbox in the EHR
- Enumerate all message threads with summaries
- Extract participants, timestamps, and preview text for each thread
- Structure and return the thread data as JSON
Value:
- Enables AI triage of incoming patient messages
- Powers automated response drafting and prioritization
- Unlocks messaging data for care coordination workflows

#### Reply to Message
Sends a reply to an existing message thread in the EHR. Navigates to the thread, composes the response, and submits it on behalf of the provider.
System: EHR
Steps:
- Navigate to the specified message thread in the EHR
- Compose the reply content in the message editor
- Submit the message on behalf of the provider
- Confirm delivery and return the sent message status
Value:
- Automates routine patient message responses
- Enables AI-assisted clinical communication at scale
- Reduces provider inbox burden by handling standard replies

### Document Management

#### Upload Patient Document
Uploads and labels a document to a patient's chart in the EHR. Navigates to the patient record, attaches the file, applies the correct document label, and confirms the upload.
System: EHR
Steps:
- Navigate to the patient's chart in the EHR
- Open the document upload interface
- Attach the file and apply the appropriate document label
- Confirm the upload and verify the document appears in the chart
Value:
- Automates intake document processing and filing
- Eliminates manual scanning and labeling workflows
- Ensures documents are correctly categorized in the patient chart

#### Get Fax Documents
Retrieves incoming fax documents from the EHR fax queue. Extracts fax metadata, content previews, and file references for downstream processing.
System: EHR
Steps:
- Navigate to the EHR fax inbox or document queue
- Enumerate all incoming fax documents with metadata
- Extract sender information, timestamps, and content previews
- Return structured fax data for routing and processing
Value:
- Automates fax-to-digital conversion workflows
- Enables AI-powered fax triage and routing
- Eliminates manual fax review and filing bottlenecks

### Deployment Environments

#### On-Premise Windows Desktop Automation
Deploy computer use agents on on-premise Windows desktop applications. Automate EHRs, ERPs, claims platforms, and any other Windows application through the visual interface.
System: Windows Desktop
Steps:
- Provision Windows VMs with the target application installed
- Agent connects to the VM and navigates the desktop visually
- Executes workflows through the application's native interface
- Platform handles VM pool management, health checks, and failover
Value:
- Works with any Windows desktop application: EHRs, ERPs, claims systems, and more
- Full VM lifecycle management: provisioning, monitoring, and horizontal scaling
- On-premise deployment option for sensitive data and compliance requirements

#### Citrix and Remote Desktop Automation
Automate EHRs, ERPs, and enterprise applications running in Citrix, RDP, and virtual desktop environments. No special connectors needed.
System: Citrix / RDP
Steps:
- Connect to the Citrix or RDP session hosting the target application
- Agent processes the visual stream and navigates the interface
- Executes workflows identically to local desktop automation
- Handles session disconnects, reconnects, and latency gracefully
Value:
- No Citrix-specific connectors needed for EHRs, ERPs, or claims systems
- Same agent works on local desktop and remote desktop identically
- Handles Citrix session management, timeouts, and server migration automatically

#### Browser-Based Application Automation
Automate web-based EHRs, ERPs, CRMs, and enterprise applications through the browser. Handles SPAs, dynamic content, and complex web forms.
System: Browser
Steps:
- Launch the target web application in a managed browser session
- Agent navigates the browser interface by visual recognition
- Performs data entry, form submission, and multi-page workflows
- Handles dynamic content loading, popups, and authentication flows
Value:
- Automates any web application: cloud EHRs, ERPs, CRMs, portals, and more
- Handles SPAs, dynamic rendering, and complex JavaScript-driven interfaces
- Works alongside desktop automation for hybrid web and desktop workflows

### Automotive DMS

#### Invoice Matching Automation
Navigate the dealer management system, match AI-generated invoices to repair orders, and update billing records. Handles multi-step workflows across parts, labor, and sublet billing screens.
System: DMS
Steps:
- Open the repair order in the DMS billing module
- Match AI-generated invoice line items to repair order entries
- Update billing records with matched amounts and codes
- Confirm submission and return reconciliation status
Value:
- Eliminates manual invoice-to-RO matching across dealership systems
- Reduces billing errors and speeds up payment cycles
- Works with any desktop DMS through the visual interface

#### Vehicle Inventory Sync
Extract vehicle inventory data from the DMS, including VINs, stock numbers, pricing, and status. Returns structured data for synchronization across listing platforms and internal systems.
System: DMS
Steps:
- Navigate to the inventory module in the DMS
- Extract vehicle records with VINs, pricing, and status
- Parse and structure the inventory data into clean JSON
- Return structured records for cross-platform synchronization
Value:
- Keeps inventory synchronized across DMS, website, and third-party listings
- Eliminates manual data re-entry for inventory updates
- Supports bulk extraction across entire dealer inventory

#### Service Appointment Scheduling
Create and update service appointments directly in the DMS scheduling module. Navigates the calendar, finds available bays, and books the appointment with customer and vehicle details.
System: DMS
Steps:
- Navigate to the service scheduling module in the DMS
- Find available service bays and time slots
- Create the appointment with customer and vehicle details
- Confirm booking and return appointment confirmation
Value:
- Enables online booking that writes directly into the DMS
- Eliminates phone-based scheduling bottlenecks at dealerships
- Handles DMS-specific scheduling rules and bay assignments automatically

### Supply Chain / WMS / ERP

#### Warehouse Order Processing
Navigate the WMS to create, update, and process warehouse orders. Handles pick tickets, packing slips, and shipping labels through the desktop interface.
System: WMS
Steps:
- Open the order management module in the WMS
- Create or update order records with incoming data
- Process pick tickets and validate inventory availability
- Confirm order completion and return processing status
Value:
- Automates order processing in legacy WMS systems with no API
- Reduces manual data entry errors in warehouse operations
- Works with FAT client WMS applications like HighJump, Manhattan, and Blue Yonder

#### ERP Data Entry Automation
Automate form fills and data entry in desktop ERP systems. Navigates complex multi-tab interfaces, enters data across fields, and handles validation dialogs.
System: ERP
Steps:
- Launch the ERP desktop application and navigate to the target module
- Fill form fields with structured input data across multiple tabs
- Handle validation dialogs, confirmation prompts, and error messages
- Submit the form and return confirmation with record IDs
Value:
- Works with SAP GUI, Oracle Forms, Sage, and custom ERP interfaces
- Handles complex multi-step forms that span multiple screens
- Deploys on-premise for sensitive operational data

#### Inventory Reconciliation
Extract inventory counts and product data from the WMS or ERP, then reconcile against external systems. Returns discrepancies and structured comparison data.
System: WMS / ERP
Steps:
- Navigate to the inventory reports module in the WMS or ERP
- Extract current inventory counts and product data
- Compare against external system data provided via API
- Return structured reconciliation report with discrepancies
Value:
- Eliminates manual inventory counting and comparison workflows
- Enables daily reconciliation instead of monthly or quarterly cycles
- Works across legacy WMS and ERP systems without API access

### Dental Practice Management

#### Insurance Verification
Pull patient insurance eligibility and coverage details from the dental PMS. Navigates to the patient record, extracts insurance information, and returns structured verification data.
System: PMS
Steps:
- Search for the patient in the dental PMS
- Navigate to the insurance and benefits section
- Extract coverage details, plan information, and eligibility status
- Return structured insurance verification data
Value:
- Automates insurance checks that front desk staff do manually
- Reduces claim denials by verifying coverage before appointments
- Works with desktop PMS systems that have no API

#### Patient Scheduling
Create and update patient appointments directly in the dental PMS scheduling module. Handles provider availability, operatory assignment, and appointment type selection.
System: PMS
Steps:
- Navigate to the scheduling module in the dental PMS
- Find available slots for the specified provider and operatory
- Create the appointment with patient and procedure details
- Confirm booking and return appointment confirmation
Value:
- Enables online booking that writes directly into desktop PMS systems
- Eliminates phone-based scheduling bottlenecks at dental practices
- Supports multi-location scheduling across practice groups

#### Claims Submission
Automate dental claims submission through the PMS interface. Navigates to the billing module, populates claim forms with procedure and insurance data, and submits for processing.
System: PMS
Steps:
- Navigate to the billing and claims module in the PMS
- Populate claim forms with procedure codes, fees, and insurance data
- Validate claim details and handle any warnings or errors
- Submit the claim and return confirmation with tracking details
Value:
- Speeds up claims submission across desktop-only dental PMS systems
- Reduces manual coding errors that lead to claim denials
- Handles batch submissions across multiple patients and procedures


## Competitive Comparison

### vs UiPath / Blue Prism / Automation Anywhere
Traditional RPA vendors use brittle scripts that break when UIs change. They are
drag-and-drop tools built for enterprise IT departments. Minicor is API-first,
self-healing, includes video replay of every run, deploys as on-prem containers,
and is built for AI companies integrating with legacy systems.

### vs Computer Use Models (Anthropic / OpenAI)
Most computer use approaches put one model in front of a screen to figure out
everything from scratch every time. This works for demos but falls apart in
production: 80-85% accuracy and slow because the agent recalculates the trajectory
each run. Minicor stores automation as deterministic code and uses the agent only
for recovery, adaptation, and edge cases. Faster and more accurate in production.

### vs Building RPA In-House
Not hard to build one RPA. Hard to run hundreds reliably. UIs change, edge cases
pile up, errors cascade. Engineering teams drown in maintenance instead of building
their core product.

### vs Waiting for APIs
Most legacy desktop systems have no writable API and never will. Some vendors are
actively restricting third-party API access. RPA through the UI cannot be blocked.

## Frequently Asked Questions

### What is Minicor?
Minicor is a platform for building and running desktop automations at scale with
computer use agents. AI companies selling into healthcare, automotive, logistics,
and financial services need to read and write to their customers' systems of
record. These are old desktop applications with no APIs. Minicor automates them
reliably so companies can go live in weeks.

### Why can't I just use the system's API?
Most legacy desktop systems like EHRs, ERPs, DMS, and PMS have no writable API
and never will. Some vendors are actively restricting third-party API access. The
only way to read or write data is to interact with the desktop like a human would.

### Why not build RPA in-house?
Building one RPA is not hard. Running hundreds reliably is. UIs change, edge cases
pile up, errors cascade. At scale, even small error rates become catastrophic.
Engineering teams end up spending all their time maintaining automations instead
of building their core product.

### How is Minicor different from UiPath or Anthropic's computer use?
Traditional RPA vendors like UiPath use brittle scripts that break when UIs
change. Computer use models from Anthropic and OpenAI work for demos but fall
apart in production at 80-85% accuracy because they figure everything out from
scratch every time. Minicor stores automation as deterministic code and uses the
agent only for recovery and edge cases.

### What happens when the UI changes?
Minicor self-heals. A reflection agent verifies every action against what is on
screen and self-corrects before mistakes cascade. When a vendor ships a UI update,
the automation adapts instead of crashing. 93-96% click accuracy vs 80-85% for
other approaches.

### How long does it take to go live?
Zero to production in weeks. Install the Desktop Client, record a video of a
human performing the workflow, and Minicor handles everything after that.
Traditional RPA approaches typically take 4 or more months.

### Is Minicor HIPAA compliant?
Yes. SOC 2 Type II certified and HIPAA compliant. For on-premise deployments,
the entire platform is containerized and runs inside your network. No data leaves
your perimeter.

### What systems does Minicor work with?
Any legacy desktop or web application. Healthcare EMRs like Athena, Epic, Cerner,
and PS Suite. Dental PMS like Open Dental and Dental Vision. Automotive DMS like
CDK Global. Supply chain systems like SAP and HighJump. Home health systems like
Wellsky and Home Care HomeBase. Financial services platforms for claims, banking,
and underwriting.

### How does pricing work?
Development is included. You pay per successful task execution. Usage-based pricing.


## Blog

- [Why Computer Use Agents Are the Future of Enterprise Desktop Automation](https://minicor.com/blog/future-enterprise-desktop-automation-computer-use-agents): Enterprise desktop automation is shifting from brittle scripts to intelligent agents. Computer use agents will replace traditional RPA within five years.
- [How to Automate Athena EHR Without API Access](https://minicor.com/blog/automate-athena-ehr-without-api): Athena's on-premise desktop EHR has no writable API. Here is how computer use agents automate it reliably in production.
- [How to Automate CDK Global and Dealer Management Systems](https://minicor.com/blog/automate-cdk-global-dealer-management-system): CDK Global and Reynolds will not give you API access. Computer use agents navigate these DMS desktop applications through the visual interface.
- [How to Automate Open Dental and Desktop Practice Management Systems](https://minicor.com/blog/automate-open-dental-desktop-practice-management): Dental practice management systems are desktop applications with no API. Here is how to automate insurance verification, scheduling, and claims submission.
- [How to Automate SAP GUI and Legacy ERP Desktop Applications](https://minicor.com/blog/automate-sap-gui-legacy-erp-desktop): SAP GUI, Oracle Forms, and Sage 300 run critical operations on desktop. Traditional RPA breaks on their complex interfaces. Computer use agents handle it.
- [Automating Insurance Claims Processing on Legacy Desktop Systems](https://minicor.com/blog/automating-insurance-claims-processing-legacy-systems): Insurance claims run through legacy Windows applications with no API. Computer use agents automate these workflows without replacing the underlying system.
- [The ROI of Switching from Traditional RPA to Computer Use Agents](https://minicor.com/blog/roi-switching-rpa-to-computer-use-agents): Companies switching from traditional RPA to computer use agents see faster deployment and lower maintenance. Here is how the ROI breaks down.
- [One Engineer, Fifty Automations: Why Computer Use Agents Scale Better](https://minicor.com/blog/one-engineer-fifty-automations-computer-use-agents): Traditional RPA needs one engineer per three to five bots. With computer use agents that self-heal and learn, one engineer manages fifty or more. Here is why.
- [Production RPA Observability: Beyond Dashboards and Log Files](https://minicor.com/blog/production-rpa-observability-beyond-dashboards): When a desktop automation fails, you need to see what happened on screen. Log files are not enough. Visual replay changes how fast you resolve issues.
- [Citrix and Remote Desktop Automation with Computer Use Agents](https://minicor.com/blog/citrix-remote-desktop-automation-ai-agents): Citrix and RDP environments are common in enterprise. Computer use agents work through the visual interface, making them ideal for remote desktop automation.
- [The Bottleneck Is Not AI Capability. It Is Legacy System Deployment.](https://minicor.com/blog/ai-bottleneck-legacy-system-deployment): AI models work. The problem is getting their output into legacy systems never designed for programmatic access. Deployment is the real bottleneck.
- [How AI Agents Handle UI Changes That Break Traditional RPA Scripts](https://minicor.com/blog/ai-agents-handle-ui-changes-break-rpa): A vendor pushes a UI update and every RPA script breaks. Computer use agents that see the screen instead of reading selectors handle changes automatically.
- [Computer Use Agents for Healthcare: Automating What APIs Cannot Reach](https://minicor.com/blog/computer-use-agents-healthcare-ehr-automation): Healthcare runs on EHRs with no APIs. Computer use agents navigate these desktop applications visually, processing thousands of patient interactions daily.
- [Scaling Desktop Automation from Five Bots to Five Hundred](https://minicor.com/blog/scaling-desktop-automation-five-to-five-hundred): Most RPA deployments stall at a handful of bots. Scaling to hundreds requires orchestration and session management that traditional tools were not built for.
- [Desktop Automation Without Selectors: How Vision-Based Agents Work](https://minicor.com/blog/desktop-automation-without-selectors-vision-agents): Traditional RPA relies on element selectors that break constantly. Vision-based computer use agents find elements by looking at the screen, like a human does.
- [Why Your RPA Total Cost of Ownership Is Higher Than You Think](https://minicor.com/blog/rpa-total-cost-of-ownership-analysis): License fees are the beginning. Factor in maintenance, failed runs, and downtime. The true cost of traditional RPA is three to five times the sticker price.
- [Automating Legacy ERP Systems Without Touching the Source Code](https://minicor.com/blog/automating-legacy-erp-systems-without-code): Legacy ERP systems like SAP GUI and Oracle Forms were never built for automation. Computer use agents interact through the GUI, no source code access required.
- [The RPA Engineer Shortage Is a Scaling Problem, Not a Hiring Problem](https://minicor.com/blog/rpa-engineer-shortage-scaling-problem): One RPA engineer maintains three to five bots. That ratio makes scaling impossible. Computer use agents change the math to one engineer for fifty automations.
- [HIPAA Compliant Desktop Automation for Healthcare AI Companies](https://minicor.com/blog/hipaa-compliant-desktop-automation-healthcare): Desktop automation on patient data requires SOC 2 and HIPAA compliance from day one. Here is what healthcare AI companies need from their automation platform.
- [Self-Healing Automation: What It Means Beyond the Marketing](https://minicor.com/blog/self-healing-automation-what-it-means): Every automation vendor claims self-healing. Here is what actually happens under the hood when a production automation encounters something unexpected.
- [EHR Integration Without an API: Solving the Last Mile for Healthcare AI](https://minicor.com/blog/ehr-integration-without-api-healthcare-ai): Most EHR systems have no usable API. Healthcare AI companies need to push data into them anyway. Computer use agents solve this last mile integration problem.
- [Why AI Companies Are Replacing RPA with Computer Use Agents](https://minicor.com/blog/ai-companies-replacing-rpa-computer-use-agents): AI startups building on healthcare, logistics, and finance are switching from traditional RPA to computer use agents. The reason is not what you might expect.
- [The Hidden Cost of Keeping Legacy RPA Bots Running in Production](https://minicor.com/blog/hidden-cost-legacy-rpa-bots-production): RPA vendors sell on build time. Nobody talks about the 90% of effort keeping bots alive after deployment. Here is what that maintenance actually costs.
- [Computer Use Agents vs Traditional RPA: A Practitioner's Comparison](https://minicor.com/blog/computer-use-agents-vs-traditional-rpa): Traditional RPA breaks when UIs change. Computer use agents see the screen and adapt. A side-by-side comparison from someone who has run both in production.
- [Why We Dropped the Planner from Our Agent Architecture](https://minicor.com/blog/flat-agent-architecture): Most computer use agents use a planner-executor hierarchy. Modern LLMs are good enough that the extra layers are now overhead. Here is what we learned when we simplified.
- [Split Reasoning from Grounding: Why Two Models Beat One](https://minicor.com/blog/two-models-click-accuracy): Asking one model to both decide what to click and predict where it is on screen is asking it to do two very different jobs at once. We found that splitting them changes everything.
- [Why Every Action Needs a Verification Step](https://minicor.com/blog/agent-self-correction): One missed click without a check cascades into five more bad actions. One missed click with a check gets caught in three seconds. The math is simple.
- [Your Agent Should Get Faster the More It Runs](https://minicor.com/blog/computer-use-agent-memory): The first time an agent navigates a system, it explores. The hundredth time, it should know the path. Most agents do not work this way.
- [When Clicking Is the Wrong Answer](https://minicor.com/blog/code-agent-when-clicking-is-wrong): Some tasks are fundamentally computational. Summing a spreadsheet column, parsing a PDF, renaming files. Clicking through a GUI for these is slow and fragile. The agent should know when to write code instead.
- [Running RPA at Scale Is a Distributed Systems Problem](https://minicor.com/blog/rpa-distributed-systems): You have a pool of Windows VMs. Requests come in through an API. Something needs to route, queue, health-check, and retry. This is not an automation problem. It is an infrastructure problem.
- [The Hidden Complexity of Windows VM Session Management](https://minicor.com/blog/windows-vm-session-management): Session timeouts, memory leaks, OS updates at 3am, MFA prompts. Your automation can be perfect. The Windows environment will still break it.
- [Routing Desktop Workflows Across a Pool of VMs](https://minicor.com/blog/routing-workflows-across-vms): One API call triggers execution. The platform handles routing, session state, and failover. Here is what that orchestration layer actually needs to do.
- [What Makes Healthcare EHR Automation Different](https://minicor.com/blog/healthcare-ehr-automation-different): The stakes are different. The UIs are deceptive. Session management is brutal. Here is what you learn when you automate EHRs in production.
- [Why AI Companies Lose Deals (It Is Not the AI)](https://minicor.com/blog/why-ai-companies-lose-deals): The AI works. The model generates the note, processes the invoice, classifies the document. Then the output needs to go into a legacy system with no API. That is where deals die.
- [Automating Clinical Documentation Without Breaking the Chart](https://minicor.com/blog/automating-clinical-documentation): Writing notes into an EHR is not a text-entry problem. It is a navigation and verification problem with text entry in the middle.
- [The 30-Second Constraint in Clinical Workflows](https://minicor.com/blog/latency-constraint-clinical-workflows): A doctor finishes a visit. The note needs to be in the chart before the next patient walks in. Most automation architectures cannot hit this window.
- [The 10/90 Rule of RPA: Building Is Easy, Maintaining Is Everything](https://minicor.com/blog/ten-ninety-rule-rpa-maintenance): Building an automation takes a week. Keeping it alive over 12 months is where all the time goes. The ratio is roughly 10% build, 90% maintain.
- [The Compound Failure Math That Makes Click Accuracy Critical](https://minicor.com/blog/click-accuracy-math): 95% per-click accuracy sounds great. On a 20-step workflow, it means two out of three runs will have at least one misclick. Here is why recovery matters more than accuracy alone.
- [The Three Hardest Things About Production RPA](https://minicor.com/blog/three-hardest-things-production-rpa): They have nothing to do with the automation itself. Detection, investigation, and ongoing resilience are what consume your engineering hours.
- [Silent Failures: When the Automation Runs Fine But the Data Is Wrong](https://minicor.com/blog/silent-failures-rpa): The worst kind of failure is not a crash. It is when the automation completes 'successfully' and puts data in the wrong field. Nobody finds out for hours.
- [Why Hard-Coded RPA Breaks Every Time the UI Changes](https://minicor.com/blog/why-traditional-rpa-breaks): Traditional RPA memorizes element positions and IDs. Computer use agents look at the screen. The difference matters every time a vendor pushes an update.
- [Going from Zero to Production in Three Weeks](https://minicor.com/blog/zero-to-production-three-weeks): Our customers go live in three weeks, not three months. Here is what that timeline actually looks like and what makes it possible.
- [Observability for Desktop Automation: Why Logs Are Not Enough](https://minicor.com/blog/observability-desktop-automation): When a desktop automation fails, you need to see what the screen looked like, not read a log file. Visual replay changes how fast you can investigate and resolve issues.
- [The Long Game: From Integration Layer to Something Bigger](https://minicor.com/blog/integration-layer-to-system-of-record): Every workflow automated captures data about how legacy systems actually work. Over time, that understanding compounds into something more valuable than the integrations themselves.
- [How to Write Data Back to Legacy Desktop Systems](https://minicor.com/blog/write-data-back-legacy-desktop-systems): AI companies can analyze data but cannot push results back into the system of record. Here are the options and why computer use agents are the only reliable one for desktop systems.
- [Minicor vs UiPath: Which Is Better for AI Companies](https://minicor.com/blog/minicor-vs-uipath-ai-companies): UiPath is built for enterprise IT departments. Minicor is built for AI companies integrating with legacy systems. Here is when each makes sense.
- [Home Health EHR Automation: Wellsky, HCHB, and Kinser](https://minicor.com/blog/home-health-ehr-automation-wellsky-hchb-kinser): Home health agencies run on desktop EHR systems with no APIs. AI companies serving this vertical need to automate referral intake, patient data entry, and scheduling.

## Links

- Use Cases: https://minicor.com/use-cases
- Blog: https://minicor.com/blog
- Documentation: https://docs.laminar.run/
- Contact: https://minicor.com/contact
- Book a Demo: https://minicor.com/book-demo
- Security: https://app.mycroft.io/trust/laminar