#3995 Gen-AI and Agentic-AI Innovation Services
#3995 Gen-AI and Agentic-AI Innovation Services
#3995 Describe a premium service that I could offer small businesses as well as industry to apply Generative and Agentic AI to pursue innovative new and improved systems, products and services
Offering a premium service in the rapidly evolving space of Generative and Agentic AI requires moving beyond "tool implementation" and toward AI-led business transformation.
The most valuable service you can offer is an Agentic Innovation Foundry. This is a high-touch, outcome-based consultancy that doesn't just give a company a chatbot; it builds an autonomous digital workforce designed to improve their specific products, services, and internal efficiency.
The "Agentic Innovation Foundry" Service Model
This premium service is structured as a three-tier engagement designed to minimize risk for small businesses while maximizing the innovative output for industrial clients.
1. The "Agentification" Audit (Strategic Layer)
Instead of a general tech audit, you perform a deep-dive analysis of their current value chain to identify "High-Agency" opportunities.
Workflow Mapping: Identifying multi-step processes where human-in-the-loop bottlenecks occur (e.g., procurement, R&D testing, or customer onboarding).
Gap Analysis: Determining where Generative AI (content creation) needs to be upgraded to Agentic AI (task execution).
Innovation Roadmap: A 12-month plan for transitioning from manual operations to an AI-augmented "Autonomous Enterprise."
2. Custom Agent Orchestration (Technical Layer)
You build a bespoke "Squad of Agents" tailored to their industry. Unlike off-the-shelf software, these are trained on the company’s proprietary data and integrated directly into their tools.
The R&D Agent: For industrial clients, this agent might scan patent databases, analyze sensor data, and suggest material improvements or new product iterations.
The Market Intelligence Agent: For small businesses, an agent that monitors competitors, tracks social sentiment, and autonomously updates pricing or marketing copy.
Tool Integration: Connecting agents to their CRM, ERP, or specialized industrial software via APIs (e.g., through frameworks like LangGraph or CrewAI).
3. Continuous Innovation Governance (Managed Layer)
Premium service clients often fear the "black box" of AI. This layer provides the safety and evolution required for long-term trust.
Alignment & Safety: Implementing "guardrail agents" that monitor other agents for compliance and brand consistency.
Performance Optimization: Monthly "AI Tuning" sessions where you refine the agents' prompts and memory based on real-world performance.
The Innovation Fund Model: A unique pricing strategy where you take a percentage of the measurable savings or revenue growth generated by the AI systems, aligning your success with theirs.
High-Value Industry Use Cases
| Sector | Service Application | Outcome |
| Manufacturing | Predictive Maintenance Agents that don't just alert but also autonomously order parts and schedule technicians. | 30% reduction in downtime. |
| Professional Services | Legal/Compliance Agents that review contracts against ever-changing local regulations in real-time. | 90% faster document processing. |
| Retail/E-commerce | Hyper-Personalization Agents that create unique product descriptions and ad visuals for every single visitor. | Increased conversion rates and LTV. |
Why this is "Premium"
It’s specialized: You aren't selling generic AI; you are selling industrial-grade autonomy.
It focuses on ROI: You quantify the hours saved and the new revenue streams created.
It’s future-proof: As LLMs evolve, your "Foundry" updates the underlying models, ensuring the business never falls behind.
Would you like me to draft a high-level pitch deck outline or a sample "Innovation Roadmap" for a specific industry you have in mind?
How can I best take advantage of the 30+ innovative thinking methods like First Principles, Systems Thinking, TRIZ 40 Principles, TRIZ Contradiction Matrix, SCAMPER, etc. ?
To master a toolkit of 30+ innovation methods like First Principles, TRIZ, and SCAMPER, you shouldn't try to use them all at once. Instead, think of them as specialized gears in an "Innovation Engine."
The most effective way to apply these is through a Layered Workflow where each method serves a specific phase of the problem-solving lifecycle.
1. The Deconstruction Layer (First Principles & Systems Thinking)
Before you innovate, you must strip the problem to its "atomic" truths.
First Principles: Break a system down to its fundamental physics or logic.
2 If you are building an AI agent, don't ask "How do I make a better chatbot?" Ask, "What is the fundamental unit of information exchange?"Systems Thinking: Map the dependencies. Use this to see how a change in one "agent" affects the entire "ecosystem." This prevents you from fixing a symptom while creating a new problem elsewhere.
2. The Expansion Layer (SCAMPER & TRIZ 40 Principles)
Once you have the fundamental building blocks, use these methods to rearrange them in novel ways.
SCAMPER: Use this for rapid, iterative "twisting" of an existing idea.
Example: Substitute a human-led verification step with a "Guardrail Agent."
TRIZ 40 Principles: These are "vibration patterns" of success. Use principles like Segmentation (breaking an agent into smaller, specialized sub-agents) or Dynamicity (allowing an agent’s behavior to change based on the environment).
3
3. The Resolution Layer (TRIZ Contradiction Matrix)4
This is the "Premium" step. Innovation usually stalls at a trade-off (e.g., "I want the AI to be more accurate, but that makes it too slow").
The Contradiction Matrix: Don't compromise; resolve.
5 Identify the "Improving Parameter" (Accuracy) and the "Worsening Parameter" (Speed). The Matrix will point you to specific Inventive Principles (like Preliminary Action—pre-calculating data) to solve the conflict entirely.
Mapping Methods to the Innovation Lifecycle
| Phase | Goal | Primary Methods |
| Discovery | Find the "Real" Problem | First Principles, 5 Whys, Root Cause Analysis |
| Mapping | Understand Interactions | Systems Thinking, Functional Analysis |
| Ideation | Generate Volume | SCAMPER, Lateral Thinking, Bio-mimicry |
| Optimization | Break Bottlenecks | TRIZ Contradiction Matrix, Ideality (IFR) |
| Refinement | Practical Application | Six Thinking Hats, Failure Mode & Effects (FMEA) |
Applying this to Agentic AI
Since you are pursuing Agentic systems, you can actually automate these methodologies by assigning them to specific AI Agents:
The "First Principles" Agent: Tasked with questioning every assumption in a project proposal.
The "TRIZ Master" Agent: Equipped with a RAG (Retrieval-Augmented Generation) database of the 40 Principles and the Contradiction Matrix to suggest technical breakthroughs.
The "SCAMPER" Agent: Performs 7 rapid permutations of any proposed system architecture to find "hidden" efficiencies.
By treating these 30+ methods not as "checklists" but as Agent Personalities, you transform a complex library of theory into a high-speed innovation factory.
Would you like me to create a specific prompt template for a "TRIZ Specialist Agent" that can analyze technical contradictions in your current projects?
TRIZ: The Theory of Inventive Problem Solving
This video provides a solid foundation for understanding how the systematic patterns of TRIZ can be used to solve complex engineering contradictions.
Describe the best way to apply Agentic-AI in an Ollama local LLM framework
To apply Agentic AI effectively within an Ollama local framework, you need to transition from simple "chat" interactions to a structured orchestration where the LLM can call tools and follow complex workflows.
As of late 2025, the most robust way to achieve this is through an Agentic Orchestration Layer that treats Ollama as the inference engine while the framework manages the "loop" (Think → Plan → Act → Reflect).
1. Choose the Right Orchestration Framework
Since Ollama is a local backend, you need a lightweight yet powerful framework to manage your agents. The top choices for local deployment are:
CrewAI (Collaborative): Best for multi-agent "teams" where agents have specific roles (e.g., Researcher, Technical Writer). It works seamlessly with Ollama using the
ChatOllamaclass.LangGraph (Stateful): If you need high precision and custom control over the agent's logic paths. It models agents as a graph, allowing you to define exactly when an agent should loop back or move to the next step.
Microsoft AutoGen: Excellent for "conversation-based" agents that solve tasks through peer-to-peer dialogue. It now has native support for Ollama without needing intermediary proxies like LiteLLM.
2. Optimize Ollama for Agentic Performance
Not all models are built for agency. For agents to work, the model must be capable of Function Calling and Reasoning.
Model Selection: Use models specifically fine-tuned for tool use.
Llama 3.2 (8B or 11B): The gold standard for local agency due to its strong instruction-following.
Mistral-Nemo (12B): Excellent for long-context reasoning.
Qwen 2.5 (7B/72B): Currently holds top-tier performance for local function calling accuracy.
Parameter Tuning: Set your
temperaturelow (0.1 - 0.3) for agents to ensure deterministic behavior and reliable tool calling. Use thenum_ctxparameter to increase context windows if your agents need to analyze large documents.
3. Implementation Workflow: The "Local Loop"
The most effective setup uses a Central API Endpoint where your orchestration framework talks to Ollama’s OpenAI-compatible API.
Host Ollama: Ensure the server is running locally on port
11434.Define Local Tools: Create Python functions for your agents to use (e.g., searching a local PDF database, checking a calendar, or executing code).
The Orchestration Code:
from crewai import Agent, Task, Crew from langchain_ollama import ChatOllama # Connect to local Ollama local_llm = ChatOllama(model="llama3.2", base_url="http://localhost:11434") # Define an Agent innovator = Agent( role='System Architect', goal='Analyze systems for bottlenecks using TRIZ', backstory='Expert in industrial optimization.', llm=local_llm )
4. Advanced: Native Tool Calling
Starting in mid-2025, Ollama added native support for Tool Calling. This is superior to the old "prompt-engineering" method because the model understands the specific schema for your tools.
Why it matters: It reduces hallucinations where the model "makes up" tool outputs.
How to use: Ensure you are using a model with the
toolstag in the Ollama library. When you define your tools in a framework like LangChain, they are automatically converted into a JSON schema that Ollama understands.
Comparison of Local Agent Approaches
| Feature | Single Agent (ReAct) | Multi-Agent (CrewAI) | Graph-Based (LangGraph) |
| Complexity | Low | Medium | High |
| Reliability | Variable | High (Peer Review) | Maximum (Custom Logic) |
| Local Resource Use | Low | Medium (Sequential) | High (Parallel nodes) |
| Best Use Case | Simple Q&A with Web Search | Complex projects (e.g., Market Research) | Mission-critical workflows (e.g., S&MA) |
Would you like me to provide a full Python script to set up a local TRIZ-specialized agent using Ollama and CrewAI?
Local AI Agents with LangGraph and Ollama
This video provides a practical, step-by-step guide to building autonomous agents that run entirely on your local hardware using the LangGraph framework and Ollama.
From an online chat perspective, would Google NotebookLM be useful for this endeavor?
Google NotebookLM is an exceptional tool for this endeavor, but it serves a different purpose than the Ollama/Agentic framework we discussed.
Think of Ollama + CrewAI as the "Engine Room" (where the work gets done autonomously) and NotebookLM as the "Executive Research Desk" (where knowledge is synthesized and presented).
How NotebookLM Fits Your "Premium Service"
For a consultant or solopreneur, NotebookLM is arguably the best tool for Phase 1: The Deconstruction Layer and Phase 3: The Governance Layer of your innovation service.
1. The "Innovation Knowledge Base" (Internal Use)
You can create a notebook for each client and upload:
The TRIZ 40 Principles and Contradiction Matrix.
Industry Standards (e.g., NASA NPR 7150.2 or specific ISO standards).
Client Data (interview transcripts, system manuals, or legacy code documentation).
First Principles Whitepapers.
The Benefit: NotebookLM will "ground" its answers strictly in those documents. When you are brainstorming a TRIZ-based solution for a client, you aren't just getting "generic AI advice"—you are getting a response cited directly from the sources you provided.
2. Client-Facing "Insight Deliverables"
One of the "premium" aspects of NotebookLM is the Audio Overview (Podcast) feature.
The Play: After performing an AI audit for a client, you can upload your findings and the relevant innovation frameworks to a notebook. Then, generate an "Audio Overview."
The Value: You provide the client with a 10-minute, professional-sounding podcast where two "experts" discuss their specific business bottlenecks and how TRIZ or Systems Thinking can solve them. This is a high-impact, "magic" moment for small business owners.
3. Rapid Semantic Mapping
Unlike a standard chat, NotebookLM allows you to see citations in real-time. If you are applying "First Principles" to a complex industrial system, NotebookLM can show you exactly which part of the technical manual contradicts the proposed innovation, helping you identify the "Physical Contradiction" required for TRIZ.
NotebookLM vs. Agentic Ollama: Key Differences
| Feature | Google NotebookLM | Ollama + Agentic Framework |
| Primary Goal | Knowledge synthesis and RAG. | Task execution and autonomy. |
| "Agency" | Low (It can't "do" things, only talk). | High (Can run code, call APIs, browse). |
| Privacy | Cloud-based (Google). | 100% Local / Private. |
| Speed to Set Up | Instant (Upload & Chat). | Medium (Requires Python/Config). |
| Best For... | Research, Briefing, and Education. | Workflows, Data Analysis, and Prototypes. |
Strategic Recommendation
Use NotebookLM to "Onboard" the Project:
Upload all the "Innovation Methods" documents you want to use.
Ask it to "Extract the core contradictions in this client's current workflow based on TRIZ."
Use the output to design the Agentic Workflows you will then build in Ollama.
Use Ollama to "Execute" the Project:
Build the specialized "TRIZ Agent" or "S&MA Compliance Agent" that actually parses the data, runs the simulations, or iterates on the designs autonomously.
Pro Tip: You can actually "close the loop" by taking the logs and outputs from your Ollama Agents, uploading them into NotebookLM, and having NotebookLM summarize the "Agentic Progress" into a weekly executive briefing for your client.
Would you like me to suggest a specific set of "Source Documents" you should feed into a NotebookLM "Innovation Engine" to get started?
How could AI coding and application development using Generative-AI provide value to this endeavor?
AI coding and application development are the "connective tissue" that transforms your high-level innovation theories (TRIZ, First Principles) into functional, revenue-generating systems. For a solopreneur with 40 years of experience, GenAI coding doesn't just replace your manual programming—it acts as a Force Multiplier for your domain expertise.
Here is how you can specifically apply GenAI development to provide value in your premium service:
1. Rapid Prototyping of "Innovation DSLs"
As someone interested in Domain-Specific Languages (DSLs), you can use GenAI to rapidly build specialized mini-languages that allow non-technical business owners to interact with complex systems.
The Value: Instead of writing complex code, a client uses a simplified DSL you designed (e.g., "Optimise: Manufacturing_Line where Bottleneck == Cooling_Phase").
GenAI Role: You use LLMs to generate the parsers and compilers for these DSLs, drastically reducing development time from months to days.
2. Automated Compliance & S&MA "Guardrail" Agents
Given your support for NASA Safety and Mission Assurance (S&MA), you can offer a premium "Compliance-as-Code" service for industry.
The Value: Build agents that automatically audit a company's software or system designs against standards like NPR 7150.2 or ISO standards.
GenAI Role: Use LLMs to "read" the standard and "write" the unit tests or static analysis rules that verify a codebase is compliant. This transforms a manual, error-prone auditing process into an automated, real-time safety net.
3. Creating the "Digital Twin" of the Innovation Engine
You can develop a custom application—let's call it the Innovation Workbench—that integrates all 30+ methods we discussed.
The Value: A client inputs a problem, and your software "runs" it through the TRIZ Contradiction Matrix, SCAMPER, and First Principles simultaneously, presenting a synthesized solution.
GenAI Role: Use GenAI to handle the unstructured natural language inputs and map them to the structured logic of the TRIZ matrix or Systems Thinking graphs.
The "Agentic Developer" Workflow for Solopreneurs
As a 40-year veteran, your role shifts from Coder to System Architect.
| Activity | The Old Way (Manual) | The New Way (Agentic) |
| System Architecture | Drawing diagrams and manually mapping data flows. | Prompting an LLM to generate Mermaid.js diagrams and boilerplate Python architecture. |
| Code Implementation | Writing functions, handling boilerplate and APIs. | "Pair programming" with a local model (Ollama) that handles the 80% repetitive tasks. |
| Testing/QA | Manually writing test cases for edge conditions. | Asking an "Adversarial Agent" to find vulnerabilities or logical flaws in your code. |
| Documentation | Spending days documenting system logic for the client. | Auto-generating technical manuals and "Innovation Briefs" based on the code itself. |
Measurable Value for Your Clients
Reduced R&D Costs: You can show a small business how to build a functional prototype for $5,000 using AI-assisted coding that would have cost $50,000 in traditional development.
Safety Assurance: For industrial clients, you provide a "Safety Shield" agent that ensures their innovative new systems don't violate critical industry safety protocols.
Speed to Market: You enable businesses to pivot their product line in weeks rather than years by automating the "What If" scenarios in their software models.
Next Step
Would you like me to outline a Python-based "Compliance Agent" prototype that uses Ollama to check a simple requirement against a NASA-style safety standard?
From <https://gemini.google.com/app/bb3874910430577c> Google Gemini 3.0 Pro (Thinking)
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