We embed AI in product workflows — we do not just add a chat
We help users and teams work faster with knowledge, documents, requests, and repeating tasks — with quality control, access, and cost in view.
Where this fits
- AI search and answers over a knowledge base
- Extracting and structuring data from documents
- An assistant for a specialist or an operator
- Classifying and routing incoming requests
- Drafting from approved data
- Personalizing learning and user experience
What the work includes
Scenario selection, sources and access rights, the human role, quality and cost metrics, a bounded pilot, then embedding into the product.
What the client gets
- An AI feature embedded in the user process and measured by practical effect
How the work is organized
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Discovery
We unpack the task, the user, data, constraints, and KPIs.
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MVP / AI Pilot
We test the scenario on a working product.
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Production
We harden architecture, integrations, roles, security, and operations.
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Growth
We improve the product from data and feedback.
How we reduce risk
- We check data sources and access rights.
- We define where a human makes the decision.
- We log critical actions.
- We measure quality and cost.
- We run a bounded pilot before scaling.
We use this on our own site
Teoglobal’s AI Project Intake Assistant helps a prospective client pick a working format, gather basic context, and hand it to a person only after explicit consent. It is not a free-form chat and not an autonomous salesperson: the dialogue is limited to an approved knowledge base, and partnership decisions stay with the team.
What to plan for
AI is not added for its own sake. You need data with clear rights, a process owner, and a willingness to measure effect. We do not promise model accuracy, savings, or autonomous decisions without a human.
FAQ
Can we add a site chat and call it an AI rollout?
No. We embed a function in a work scenario with context, access, and a metric. A decorative chat is not this service.
What if we do not have a platform yet?
You may need product development or a Discovery Sprint first — AI attaches to a real process, not an empty screen.
No finished brief?
Start with a Product Discovery Sprint: we pick the scenario, reduce risk, and produce a launch plan before a large build.
Discuss the task