Workflow model
The job, people, systems, evidence, exceptions, and outcome the system needs to support.
Assistants that help without pretending to know everything
Grounded help for customers and teams, designed with clear handoffs, source context, and limits.
An assistant earns trust by being useful in important moments and honest when it cannot help. We design conversational assistance around actual questions, approved sources, escalation paths, and the service experience people need.
Where this can help
Often a good fit
- Support and service teams
- Products helping users complete complex tasks
- Internal teams needing approved guidance
Probably not the right fit
- An unattended public bot
- Answers that hide evidence
- A substitute for fixing the customer experience
What the engagement can produce
Integrated capability
A useful interface connected to approved data, tools, permissions, and operating context.
Review and recovery
Visible handoffs, exception handling, evidence, and human decisions where needed.
Learning record
Examples, feedback, and measures that show whether the capability is genuinely helping.
How we work together
Choose the job
Start with a valuable, bounded piece of work instead of a vague AI category.
Connect context
Use the systems, information, and policies people already rely on.
Build with evidence
Keep source material, calculations, and actions inspectable as the capability takes shape.
Learn in use
Review real cases and improve the system with the people responsible for the outcome.
What may be involved technically
- Knowledge grounding
- Conversation design
- Identity-aware context
- Escalation
- Feedback
- Quality review
Questions teams often ask first
Can an assistant act for a user?
It can prepare or complete bounded actions when permissions, validation, and approval fit the risk.
How is quality measured?
With real questions, evidence, handoff quality, resolution outcomes, and operator review.