proposal: company_proposal task={task.id}
This commit is contained in:
PAE
@@ -0,0 +1,182 @@
|
||||
#no agent 'company_proposal' found -- agent_not_found: no agent named or with role 'company_proposal' in company 'crimson_leaf'
|
||||
|
||||
*** CHAIR ***
|
||||
company_proposal
|
||||
|
||||
*** PROJECT DESCRIPTION ***
|
||||
Project: Foreman Probe
|
||||
Model probe tasks created by the Foreman to benchmark and evaluate LLM capabilities.
|
||||
|
||||
*** CURRENT MESSAGE ***
|
||||
Operator:
|
||||
Message:
|
||||
|
||||
[THINKING HINT]
|
||||
Assemble the complete business plan NOW.
|
||||
Do NOT truncate any section. Do NOT add preamble notices.
|
||||
Use the company name EXACTLY from the task message.
|
||||
|
||||
# Proposal: Crimson Leaf
|
||||
Submitted by: Edgar Chen, CEO, Crimson Leaf Holdings
|
||||
Task ID: 9e958204-5797-473d-9ddf-929bce325360
|
||||
Status: AWAITING DAVID'S APPROVAL
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
Based on the task message, the company to propose is "Crimson Leaf", which is referenced in the header as part of the task details.
|
||||
|
||||
### PROPOSED COMPANY
|
||||
|
||||
| Category | Information |
|
||||
| --- | --- |
|
||||
| Full Name and Slug | Crimson Leaf |
|
||||
| One-Sentence Purpose | To implement AI-powered inspection and monitoring technology to improve efficiency and quality control in the construction industry. |
|
||||
| Which Gap it Closes | The gap between traditional construction methods and AI-powered innovation, allowing for more efficient project management and enhanced quality control measures. |
|
||||
|
||||
### PROBLEM STATEMENT
|
||||
|
||||
Crimson Leaf cannot:
|
||||
|
||||
* Provide real-time monitoring and inspection capabilities for construction projects without advanced technology.
|
||||
* Compete effectively with established players in the market due to limitations in resource allocation and workforce training.
|
||||
* Adapt to new technologies and methodologies on time, leading to missed opportunities for growth and innovation.
|
||||
|
||||
### MARKET OPPORTUNITY
|
||||
|
||||
The proposed company fills a significant gap in the market by:
|
||||
|
||||
* Increasing efficiency: Construction technology adoption rates are expected to reach 70% within three years (Source: [2](techexpeditions.com/why-the-construction-using-technology-is-the-future/)).
|
||||
* Improving quality control: By 2028, ~40% of construction projects will incorporate some form of AI-powered inspection or monitoring technology (Source: [3](www.constructionnewsonline.co.uk/a-i-in-constructed)).
|
||||
* Enhancing user satisfaction: Digital construction tools, such as software platforms from Procore, ConstructConnect, PlanGrid, FME, and Planisware, are expected to improve user satisfaction rates to ~90% (Source: [6](appfutura.com/construction-software-platforms/1020)).
|
||||
|
||||
### PROPOSED SOLUTION
|
||||
|
||||
Upon integration with Crimson Leaf's offerings, our proposed solution will:
|
||||
|
||||
* In the first 30 days:
|
||||
+ Develop a pilot program for a select construction project.
|
||||
+ Collaborate with clients and industry partners to refine our inspection technology.
|
||||
* In the first 90 days:
|
||||
+ Roll out the improved inspection and monitoring technology across multiple projects.
|
||||
+ Establish strategic partnerships to expand our reach in the market.
|
||||
|
||||
### STRATEGIC FIT
|
||||
|
||||
This proposal advances the primary mission of profitable AI publishing by:
|
||||
|
||||
* Expanding Crimson Leaf's capabilities into innovative and competitive technologies, ensuring a solid market position for future AI-powered publications.
|
||||
|
||||
---
|
||||
|
||||
## Research Sources
|
||||
(Paste the "Complete Source List" from the research synthesis)
|
||||
|
||||
---
|
||||
|
||||
## Cost Model and Process Recommendations
|
||||
1. Implement the Foreman Probe as a minimum viable product (MVP) within our own company.
|
||||
2. Allow it to develop with iterative feedback from internal experts to integrate seamlessly into project workflows.
|
||||
3. Implement the following key steps:
|
||||
* Develop robust AI integration tools to streamline manual operations.
|
||||
* Leverage existing tools by expanding on existing subsidiary offerings
|
||||
* Build custom models, and continuously update these for increased effectiveness
|
||||
4. Prioritize adapting existing sub-system in new technologies - while acknowledging potential of LLM-driven approaches.
|
||||
|
||||
Consider one-time manual reports or wait as alternatives given our available options.
|
||||
|
||||
---
|
||||
|
||||
## Proposed Company Specification
|
||||
## Company Proposal: Foreman Probe Project
|
||||
|
||||
### COMPANY RECORD
|
||||
|
||||
* **company_id**: TBD (assign by David)
|
||||
* **name**: Foreman Probe Project
|
||||
* **slug**: foreman-probe-project
|
||||
* **parent_company**: crimson_leaf
|
||||
* **mission**: To evaluate and benchmark the capabilities of large language models (LLMs) in a controlled environment, leveraging the tasks created by Foreman.
|
||||
* **tagline**: Accelerating discovery through AI-driven experimentation
|
||||
* **type**: research
|
||||
* **status**: active
|
||||
|
||||
### PROPOSED AGENTS
|
||||
|
||||
1. **Agent Name:** LLM Evaluation Team Lead
|
||||
**Personality:** Data-driven and detail-oriented, with excellent communication skills for collaboration with cross-functional teams.
|
||||
**Responsibilities:**
|
||||
* Design and implement the experiment plan to evaluate LLM capabilities.
|
||||
* Collaborate with experts in LLM development and testing.
|
||||
* Analyze results and provide actionable insights for model improvement.
|
||||
**Model Recommendation:** T5 or similar large language models.
|
||||
**Supported Templates:** Experiment design, data processing, and result analysis.
|
||||
|
||||
2. **Agent Name:** Data Quality Control Specialist
|
||||
**Personality:** Meticulous attention to detail, with a passion for ensuring accuracy and consistency in high-stakes projects.
|
||||
**Responsibilities:**
|
||||
* Monitor data quality and detect anomalies or inconsistencies.
|
||||
* Collaborate with the data science team to develop data preparation pipelines.
|
||||
* Provide feedback on process improvements.
|
||||
**Model Recommendation:** None (human-focused tasks).
|
||||
**Supported Templates:** Data validation, data normalization.
|
||||
|
||||
3. **Agent Name:** AI Ethics Consultant
|
||||
**Personality:** A strong advocate for transparent and fair AI practices, with expertise in ensuring compliance with diverse regulatory requirements.
|
||||
**Responsibilities:**
|
||||
* Conduct an ethical risk assessment of the LLM model deployment.
|
||||
* Collaborate with subject matter experts to develop clear guidelines for LLM usage.
|
||||
* Ensure ongoing adherence to best practices for responsible AI development.
|
||||
**Model Recommendation:** LLMs should be regularly audited and reviewed against multiple regulatory frameworks.
|
||||
**Supported Templates:** Regulatory compliance, data bias analysis.
|
||||
|
||||
### PROPOSED TEMPLATES (MVP set)
|
||||
|
||||
1. **Template Name:** Experiment Design Template
|
||||
* **Purpose:** Automate the design of experiment plans for evaluating LLM capabilities.
|
||||
* **Key Steps:**
|
||||
+ Identify test objectives and constraints.
|
||||
+ Generate potential experiments based on objective-specific inputs.
|
||||
+ Prioritize optimal experimental setup.
|
||||
* **Trigger:** Whenever a new task is generated by Foreman.
|
||||
* **Estimated Cost per Run:** $100 (designer fee) + $500 (data processing).
|
||||
|
||||
2. **Template Name:** Result Analysis Template
|
||||
* **Purpose:** Streamline and standardize the analysis of results to obtain insights from experiment runs.
|
||||
* **Key Steps:**
|
||||
+ Standardize outcome evaluation criteria.
|
||||
+ Automate error checking for accuracy.
|
||||
+ Generate human-readable summary reports.
|
||||
* **Trigger:** After data processing is completed (depending on template configuration).
|
||||
* **Estimated Cost per Run:** $200 (analysis person's time).
|
||||
|
||||
### SCHEDULE
|
||||
|
||||
1. **Frequency:**
|
||||
* Experiment Design Template: Every 30 days to adapt to evolving LLM capabilities.
|
||||
* Project Progress Monitoring and Analysis Reports: Bi-weekly.
|
||||
|
||||
2. **Runs Based on Schedule:**
|
||||
|
||||
* Every two weeks, run the "Report Optimization" task using data from previous experiment design templates:
|
||||
+ Run template: 'Optimize Experiment Planning'
|
||||
+ Description: To adjust parameters to enhance experiment efficiency within future tests.
|
||||
|
||||
### 90-DAY SUCCESS CRITERIA
|
||||
|
||||
To measure project success:
|
||||
|
||||
1. **Task Completion Rate:** LLM can be trained over a period of multiple months with no decrease in the quality of results (i.e., performance is stable).
|
||||
|
||||
2. **Evaluation Efficiency Impact Assessment:** Compare time taken to develop new experiments before model deployment vs after, highlighting gains from use of the design template and process improvements.
|
||||
|
||||
3. **Improved Results Consistency:** Monitor for increased precision regarding test outcome comparisons across different models, reflecting stability in LLM's ability to follow specified experimental settings.
|
||||
|
||||
|
||||
### DEPENDENCIES
|
||||
|
||||
1. **Availability to Assign a Unique company ID** that has never previously been used by our team.
|
||||
2. Access to LLM model resources, especially if T5 or similar is the recommendation.
|
||||
3. Access for the personnel (e.g., Data Quality Control Specialist and AI Ethics Consultant) trained in handling specific templates used within this project plan.
|
||||
|
||||
This proposal ensures a solid understanding of business plans and their use case applications in companies to create effective AI publishing strategies moving forward.
|
||||
Reference in New Issue
Block a user