《中小企極低成本 AI 全家桶》專案: 1 基本涵蓋範圍

 

《中小企極低成本 AI 全家桶》專案: 
1 基本涵蓋範圍

Below is a practical approach for replicating the capabilities of large‑vendor AI platforms in a GPT Store. I’ve grouped the platforms you listed into five categories and describe how collections of small, specialized GPTs could emulate the functions of each category. Where possible, I’ve anchored the suggested GPTs in features noted in the cited sources.

1. Data integration, governance & compliance

Platforms like Palantir AIP, Microsoft Fabric + Purview, Databricks Mosaic Agents and AWS Q Business / Bedrock all provide unified data ingestion, lineage tracking, policy enforcement and guardrails. A small enterprise could approximate these with several GPTs that each focus on a specific step in the pipeline:

GPT Service (10 examples)	Value & grounding in vendor features
Data‑Ingestion Agent	Emulates Palantir’s ontology‑driven integration by connecting to ERPs, IoT devices and databases to unify data and metadata. The Palantir ontology unifies data, logic and actions into a decision‑centric model.
Semantic Search & Retrieval	Performs cross‑system search using the unified schema; replicates Palantir’s semantic search and virtual tables for retrieving data without duplication.
Rule‑Based Enforcer	Embeds business rules in data pipelines (e.g., revenue recognition rules, period‑end close) similar to Palantir’s built‑in business logic.
Classification & Tagging	Labels data with sensitivity and compliance tags using GPT’s NER and classification abilities; mirrors Purview’s classification and sensitivity labeling.
Data Lineage Tracker	Records how data moves and changes (sources, transformations, outputs) to support auditability; Purview tracks lineage across Microsoft 365, multicloud and SaaS sources.
Access‑Policy Checker	Checks requests against role‑based and attribute‑based access rules; Purview enforces information protection and data‑loss prevention.
Audit & Compliance Reporter	Generates audit logs and compliance reports similar to Palantir’s action constraints and audit logging.
Reconciliation Ledger Agent	Cross‑checks multiple reports for conflicting facts and logs discrepancies; this pairs with later factuality/consistency GPTs.
Quality & Freshness Validator	Flags stale or incomplete data and triggers refreshes. AWS Q Business emphasises indexing fresh content and scalable connectors.
Data Catalog Manager	Creates a unified catalog of sources and schemas and exposes them via an API, mirroring the Purview/Fabric unified catalog.

2. Enterprise search & knowledge assistants

Tools such as Atlassian Rovo, Glean and AWS Q Business focus on personalised search, cross‑app knowledge retrieval and question‑answering. An equivalent GPT suite could include:

GPT Service (10 examples)	Value & grounding
Universal Search Assistant	Provides a single interface to search across docs, emails, Slack, CRM, etc., similar to Glean’s unified search across Google Workspace, Microsoft 365, Salesforce and Slack.
Personalised Insight Recommender	Uses an internal “teamwork graph” to suggest relevant docs and updates based on a user’s role, mirroring Atlassian Rovo’s Teamwork Graph.
Enterprise Q&A Bot	Answers natural‑language queries using retrieval‑augmented generation and cites sources, akin to AWS Q Business’s ability to search corporate knowledge bases and return answers with citations.
Document Summariser	Summarises long documents and threads for quick consumption; can output key takeaways and action items.
Meeting & Chat Summariser	Generates minutes and decisions from meetings or chat logs, similar to Rovo Chat’s ability to connect to various apps.
AI Agent Builder	Lets non‑technical users assemble new knowledge agents via natural language, like Rovo Studio’s no‑code agent builder.
ChatOps Integrator	Embeds search and summarisation into Slack or Teams channels so answers appear in the flow of work.
Source Connector Manager	Provides templates and prompts to set up connectors (e.g., Gmail, Salesforce, SharePoint, Jira) – AWS Q Business offers >40 connectors to index different data.
Security & DLP Guardian	Ensures answers respect DLP policies and sensitivity labels, following Purview’s data‑loss prevention integration.
Knowledge Base Updater	Periodically ingests new docs/emails/messages and updates semantic search indexes to keep the system current.

3. Agentic workflows & task execution

Frameworks like Salesforce Agentforce/Einstein Copilot, Databricks Mosaic AI Agent Framework, Palantir AIP’s agentic capabilities and AWS Bedrock emphasise autonomous agents that plan and execute tasks within guardrails. A portfolio of GPTs could simulate these functions:

GPT Service (10 examples)	Value & grounding
Autonomy Planner	Decomposes high‑level goals into ordered tasks and chooses appropriate tools or APIs. Salesforce Agentforce agents retrieve data, create plans and execute tasks with minimal human intervention.
RAG Workflow Builder	Assembles retrieval‑augmented pipelines and orchestrates calls to vector stores and documents; Databricks Mosaic provides a framework to build high‑quality RAG applications and evaluate them.
Agent Evaluator	Scores agent outputs on factuality, safety and cost using human‑ or model‑judged metrics; Mosaic’s agent framework includes evaluation tooling and metrics.
Guardrail Enforcer	Applies safety and compliance constraints before actions are taken; Palantir AIP emphasises action constraints and audit logs.
Action Executor	Executes API calls (e.g., send email, file support ticket) once tasks are approved, reflecting Agentforce’s ability to act on behalf of employees.
Real‑Time Data Integrator	Pulls real‑time signals (e.g., CRM updates, IoT sensor values) into the plan, similar to Palantir’s real‑time data integration.
Domain‑Specific Agents	Templates for sales, marketing, service or finance tasks; Salesforce provides out‑of‑the‑box agents for these functions.
Multi‑Agent Orchestrator	Coordinates multiple GPT agents working on sub‑tasks and handles dependencies; beneficial for complex workflows.
Feedback Incorporator	Captures user or supervisor feedback and adjusts prompts or task plans – analogous to Mosaic’s collection of human feedback for evaluation.
Agent Template Library	Stores reusable agent workflows and prompt templates that teams can clone and customise.

4. Memory management, reflection & sleep‑time compute

Research like Letta’s sleep‑time compute, MemGPT, Reflexion and Generative Agents highlights the importance of offline processing, long‑term memory and self‑improvement. GPT‑based services could emulate these mechanisms:

GPT Service (10 examples)	Value & grounding
Memory Hierarchy Manager	Implements tiered memory (core, recall, archival) so agents can manage context windows; MemGPT adds a tiered memory system and function calls to move data between fast and slow memory.
Sleep‑Time Summariser	Runs on a schedule (e.g., nightly) to summarise the day’s interactions into compact “pinned blocks” and update long‑term memory, inspired by Letta’s sleep‑time compute which uses a sleep agent to refine memory and precompute answers.
Reflection Writer	After each task, writes a reflective note about what went well or poorly; Reflexion agents maintain reflective text in an episodic memory buffer to improve subsequent decisions.
Daily/Weekly Planner	Generates plans for the next day or week based on memories and reflections. Generative Agents use memory retrieval, reflection and planning loops to inform behaviour and feed these reflections back into the memory stream.
Memory Consolidator	Decides which memories are important enough to keep and which to archive or discard; Letta’s sleep‑time compute emphasises memory consolidation and precomputation.
Memory Query API	Allows other GPTs to query long‑term memory for facts; MemGPT can search recall storage to answer questions even when the answer is not in the current context.
Schema Induction Agent	Induces new fields and extraction rules based on recurring patterns in memory; ties into the extraction‑rule learning described later.
Precomputation & Caching Agent	Anticipates likely questions and precomputes responses while idle, reducing latency – Letta’s model offline precomputes answers and improves responsiveness.
Memory Graph Visualiser	Presents interactive graphs of memory nodes and their relationships to help humans understand the agent’s knowledge.
Behaviour Simulator	Simulates generative agents in small “towns” or scenarios for training; generative agents rely on LLMs to remember interactions, reflect and plan, enabling believable behaviour.

5. Factuality & consistency checking

Research on SelfCheckGPT, SummaC, SIFiD and similar methods offers tools for verifying AI outputs and detecting contradictions. In a GPT Store, these functions could be modular GPTs:

GPT Service (10 examples)	Value & grounding
Hallucination Detector	Implements SelfCheckGPT’s sampling‑based approach: compare multiple stochastic generations to detect statements lacking consensus.
Summary Consistency Evaluator	Uses natural language inference (NLI) to score entailment between source and summary sentences, similar to SummaC’s approach of aggregating NLI scores for summary inconsistency detection.
Multi‑Report Contradiction Detector	Compares multiple reports or analyses and flags conflicting facts; fits within the earlier reconciliation ledger.
Extractor‑Rule Learner	Learns new extraction schemas by identifying key sentences through NLI or semantic similarity – SIFiD filters documents to find key sentences and uses LLMs for inconsistency detection.
Factuality Scoring Agent	Assigns an “A‑score” to extracted facts based on relevance to key objectives, expected utility and tension; helps decide what to promote into long‑term memory.
Reconciliation Agent	Maintains the reconciliation ledger, updates winners and losers for contradictions, and notifies stakeholders.
Metric Dashboard Generator	Summarises precision, recall and false‑positive rates of the above detectors; Databricks Mosaic emphasises evaluating quality via metrics.
Data Freshness Monitor	Checks whether supporting data is up to date and triggers re‑ingestion; ties back to the Data‑Quality Validator.
Cross‑Language Consistency Checker	Ensures summaries or translations across languages remain faithful to the source.
Correction Suggestor	Suggests corrected sentences or facts when inconsistencies are detected, referencing the authoritative sources.

Putting it together

This stack built from GPTs could let a small enterprise ingest its data, enforce policies, provide unified search, spin up task‑oriented agents, consolidate memories, and continuously verify factuality—all without the cost of enterprise licences. Each GPT would be specialised but interconnectable via APIs or shared memory. 

For example, the Data Lineage Tracker could feed into the Audit Reporter, while the Hallucination Detector informs the Reconciliation Agent about which facts to trust. Scheduling “sleep‑time” jobs (e.g., daily summarisation and weekly rule induction) would ensure that the system stays lean and improves over time, echoing Letta’s sleep‑time compute and the reflection mechanisms of Generative Agents and Reflexion.

This modular approach doesn’t replicate every nuance of the enterprise platforms, but it shows how a group of focused GPTs can simulate many of the core services while remaining affordable and flexible.

 

 

 

 

 © 2025 Danny Yeung. All rights reserved. 版权所有 不得转载

 

Disclaimer

This book is the product of a collaboration between the author and OpenAI's GPT-5 language model. While every effort has been made to ensure accuracy, clarity, and insight, the content is generated with the assistance of artificial intelligence and may contain factual, interpretive, or mathematical errors. Readers are encouraged to approach the ideas with critical thinking and to consult primary scientific literature where appropriate.

This work is speculative, interdisciplinary, and exploratory in nature. It bridges metaphysics, physics, and organizational theory to propose a novel conceptual framework—not a definitive scientific theory. As such, it invites dialogue, challenge, and refinement.

I am merely a midwife of knowledge.