Workflows Without Walls

AI Support for High-Frequency Tasks

Most organizations face the same core challenge: a heavy load of repetitive administrative tasks that are critical to operations, yet time-consuming and resource-intensive. These processes, ranging from gathering data, assembling reports, and filling out forms to generating complex data transformations and communications, exist across industries and departments.

Our software takes a domain-agnostic, AI-in-the-loop approach to help users complete these tasks more efficiently, without removing human control. Instead of replacing human work, our platform enhances it - allowing users to delegate portions of the workflow to AI while retaining ownership of decisions, verification, and final outputs.

A Typical Workflow: Human-Led, AI-Augmented

Our platform follows a modular workflow model where you remain fully in control. The AI acts as a supportive tool, responding to your instructions throughout each step. Here’s how a common task might progress:

1. Data Collection and Autofill

  • H: The user selects a report template. To gather necessary data, they instruct the AI to collect information and fill out the forms, possibly adding real-time prompts or instructions.
  • AI: The AI gathers data from various sources, including uploaded documents or connected systems, and populates the form fields automatically.
  • H: The user reviews the autofilled content, making edits if needed and approving the results.

2. Setting Up AI Instructions

  • H: The user requests an output—such as a data transformation, summary, table, paragraph, or full report—based on the verified data generated in Step 1. They may use a saved prompt from a personal or team prompt library, or craft one from scratch.
  • H: A sample output can be selected from the personal or team library to guide the tone, structure, or format of the AI-generated content.
  • H: Additional supporting documents can be added for the AI to consider during the generation process.
  • AI: The AI processes this information, producing a structured output that meets the user’s requirements.

3. Running the AI Chain

  • H: The user runs the AI-Chain with the gathered information and settings. Multiple chains can be run in sequence, with the output of one chain informing the next.
  • AI: Executes the AI-chain, combining all inputs to produce structured, contextually relevant content—reliably and without hallucinations.
  • H: The user reviews the results and makes any necessary adjustments before finalizing the work. Prompts and samples may be fine-tuned for future runs.

4. Final Document Assembly

  • H: After everything is complete, the user clicks a “magic wand” icon to generate the final document. Rich-text templates are populated with the AI-generated content, formatted consistently with branding, headers, dynamic tables, and images.
  • Output: The user receives a polished document in their desired format, such as DOCX, Google Docs, or a fillable PDF.

5. Chain Forward: Using Outputs in New Tasks

  • H: Any completed data transformation, report or document can be repurposed as an input for a new workflow.
  • AI: Extracts relevant information from the prior output, maps it to new data fields or templates, and uses it to seed the next AI-chain.
  • H: This supports continuous workflows where one task’s result is used in another, reducing duplication and enhancing efficiency.

Tag supports everything from simple forms to complex processes, adapting continually to your needs.

Practical Example: Education Workflow

In education, collaboration often spans across roles, departments, and timelines. Our system streamlines this process. Here’s an example of how a collaborative workflow could happen using AI to make each step easier:

1. School Psychologist: Completing the Assessment Report

A school psychologist conducts a psychoeducational assessment for a student suspected of having a learning disability. After testing is complete, she receives a standardized score report in PDF format from her scoring software. Rather than manually transcribing scores into her documentation, she uses AI to autofill fields in Tag forms. When the forms are ready, more AI chains are used to generate report content based on her custom prompt and writing sample.

2. Teacher: Creating the Individualized Program Plan (IPP)

The student’s classroom teacher accesses the completed assessment report's data. Using the AI, he extracts a subset of the assessment data (diagnosis, relevant test results, and narrative summary) into a form for the IPP. Then, he runs his own AI chain which specifies preferred interventions and includes a sample IPP from the school board's sample libary. He then generates the IPP, reviews it, and submits the finished document.

3. Administrator: Preparing Grant Documentation

Once both the Psych Assessment Report and IPP are complete, the school administrator initiates a new task: preparing a Grant Monitoring Form for government funding. Rather than re-entering information, the administrator uses Tag autofill to pull content from both documents. Within minutes, the government’s standardized fillable PDF is completed and submitted to the appropriate education authority.

4. Government Analyst: Compiling Data

A government employee later accesses a batch of submitted Grant Monitoring Forms. Instead of compiling summaries manually, she uses the AI to extract structured data from each PDF and generate a collated summary table. Her AI-chain transforms multiple inputs into a clean dataset, ready for reporting or funding analysis.

This example highlights how our platform supports collaboration across different roles while keeping everyone in control. The result is faster completion times and accurate outputs that meet everyone’s needs, all while ensuring professionals are in the driver’s seat.

Let Tag take care of the time-consuming tasks so you can focus on what truly matters.