AI Agent based SaaS Platform (AIBSP)
Bibliographic record
Abstract
This work presents a web-based AI-powered platform named AI SAP Tools, designed to deliver intelligent SaaS-based utilities such as research paper summarization, subtitle generation, PDF-based question answering, and data analysis. The system integrates multiple AI models, including large language models (LLMs) and speech-to-text engines, to power and improve user output in academic, professional, and enterprise contexts. Each tool acts as an independent AI agent, interacting via a combined interface that allows users to select and use tools as needed. The platform works on coin-based subscription model using Coins, allowing micro-payments for tool usage instead of traditional fixed plans. System performance is evaluated in terms of response accuracy, processing time, and user efficiency. Results indicate improved task automation and accessibility when compared to conventional manual processes. This approach aims to democratize AI access for a wider user base and establish a scalable framework for deploying AI utilities in SaaS environments. Future improvements includes adding performance analyzer and increasing multilingual support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".