01
AI assistants for questions and requests
Give employees or customers a useful first point of contact that can answer from approved business information, capture what it cannot resolve, and route the next step.
What Gibson AI can help with
Start with the work that keeps piling up.
The best first AI project is usually not a grand transformation. It is a frustrating, repeatable task that drains time: a report someone rebuilds, questions that keep arriving, information scattered across documents, or customer follow-up that needs to be faster and more consistent.
02
Workflow and reporting automation
Reduce repetitive work around reporting, research, document preparation, data checks, and routine coordination—while keeping people in control of important decisions.
03
Practical analytics and machine learning
When the business case calls for it, use data, forecasting, or machine learning to improve decisions—not as a separate technology exercise.
Live applications
Explore the applications behind the work.
The videos show one working workflow. These live projects let visitors explore the applications themselves and see the different patterns Gibson AI builds with.
Digital Twin
A voice-enabled, knowledge-grounded assistant that answers questions, supports scheduling, and surfaces unanswered requests for follow-up.
Try the Digital TwinNetflix RAG Chatbot
A document-based question-answering demo that shows how retrieval can ground responses in a trusted knowledge base.
Open Netflix RAGAI Article Writer
A multi-step AI workflow that turns a prompt into an article and paired image, demonstrating orchestration and user-facing output.
Enter a topicCardioSentinel
A machine-learning project that demonstrates structured data preparation, model evaluation, and a reproducible predictive workflow.
View on GitHubCase studies
How the systems are actually built and checked.
A live app shows that something works. A case study shows the decisions behind it—what was deliberately left out, where the constraint is enforced, and what test would have caught a false claim.
Teaching an Agent to Remember (Only What It Should)
Adding durable, cross-session memory to a retrieval agent—then killing the server process outright and reading the fact back from a cold start. Three allow-listed keys, enforced at two independent layers.
- A named allow-list instead of “remember everything”
- Rejected at the storage layer, not just in the prompt
- Proven against a real process restart, not simulated
The entire schema
preferred_nameGreg
preferred_languageEnglish
last_topicculture memo
Three keys. Any other key is rejected with an HTTP 400 at write time.
See it work
A working example, not just a list of promises.
The Digital Twin demonstration shows the building blocks of an AI assistant that can be adapted for a business: its own trusted knowledge, natural conversation, and a clear handoff when a person is needed.
- Voice and typed questions in one simple interface
- Answers grounded in a curated knowledge base
- Scheduling support through connected tools
- Alerts when a question needs a human response
From useful answers to reliable follow-up
The demo is not a generic chat window. It combines a curated knowledge base, a voice interface, scheduling support, and a handoff when the assistant reaches a question that needs a person.
That same pattern can help teams respond more consistently while making sure important questions are still seen and handled.
Explore the live appA practical starting point
From a work bottleneck to a usable system.
Implementation begins with the business process—not with a generic AI tool. That keeps the project focused, easier to test, and connected to work your team already understands.
1
Find the friction
Identify the repeated task, the people involved, the information it needs, and what a better outcome would look like.
2
Build a focused pilot
Create a small, useful workflow with clear boundaries, review points, and a simple way to measure whether it saves time or improves responsiveness.
3
Refine and extend
Use what the pilot reveals to improve the workflow, train the people using it, and decide whether to expand the system into adjacent processes.
Why Gibson AI
AI implementation grounded in operations.
Before working with AI systems, Greg Gibson improved reporting and automation in factory, warehouse, logistics, and delivery environments. That background brings a practical question to every project: will this make the work easier, clearer, and more dependable for the people who have to use it?
Operations perspective
Experience turning messy inputs, manual reporting, and delayed information into clearer, more reliable business processes.
Builder mindset
Hands-on work with AI applications, retrieval-augmented generation, tool calling, automation, and user-facing prototypes.
Right-sized technology
Use the simplest approach that solves the problem well. AI, data automation, and machine learning each have a place—but not every problem needs all three.
Let’s talk
What is one computer task your team wishes would take care of itself?
Start with the process that creates the most repeated effort or unanswered requests. We can explore whether a focused AI workflow is a good fit.