Services
Intelligent systems start with the right questions.
Support at every stage of your AI journey
Whether you’re exploring your first use case or trying to improve an existing application, Twizu’s service approach brings together business priorities, user needs, technical choices and the practical realities of operating AI systems.
01. AI strategy and advisory
A strong AI project begins with knowing which problem is worth solving. We can help clarify goals, review existing workflows, consider data readiness, compare approaches and define what success should look like.
- Opportunity discovery and practical use-case prioritisation
- Feasibility, data-access, governance and cost considerations
- Model and vendor options mapped against requirements
- Phased roadmaps with sensible milestones and decision points
02. Custom AI applications and product development
For problems that need more than an off-the-shelf chatbot, we can explore purpose-designed interfaces and workflows that fit the people using them. Typical patterns may include internal knowledge assistants, structured content tools, AI-enhanced customer journeys and new software products.
- Early concept validation and interactive prototypes
- Usable application interfaces, APIs and workflow logic
- Knowledge retrieval, search and document-grounded responses where suitable
- Testing of output quality, usability and failure handling
03. Agents, automation and workflow integration
AI-powered agents can coordinate tasks and decisions, but autonomy needs appropriate boundaries. We take an incremental approach, identifying where automation saves time and where a human should review an action.
- Mapping repetitive processes and decision points
- Connecting AI to appropriate business software and data sources
- Approval steps, auditability and permissions by design
- Monitoring, fallback behaviour and hand-off to people
04. Model integration and optimisation
Model choice is not just about the latest benchmark. Different applications need different balances of latency, price, privacy, quality and control. We can examine these trade-offs and explore how the chosen technologies fit an application’s architecture.
- Architecture and provider-selection discussions
- Prompt and evaluation strategies
- Usage tracking, cost analysis and practical efficiency improvements
- Careful assessment of model changes as products evolve
05. Adoption, iteration and support
Launch is the beginning of the learning process. Teams need clear ownership, feedback loops and a way to improve the system based on real usage. Support arrangements, service levels and availability would be defined in a separate engagement agreement.
Understand the opportunity
Define users, workflows, desired outcomes and what good looks like.
Plan the right solution
Decide how AI, software and human review should fit together.
Validate with working software
Prototype, test, gather feedback and refine the highest-value features.
Learn from real usage
Review quality, operational costs, adoption and future requirements.
Common questions
Do we need to know which AI model we want?
No. The model should follow the use case. A useful starting point is to describe the problem, available data, constraints and intended users.
Do we have to automate everything?
Not at all. Partial automation, decision support or better information retrieval may be more appropriate. Human approval and review may be essential.
Can Twizu help with the cost of AI?
Cost awareness is part of sensible design. Twizu is also exploring this problem through its developing Modelspend product, although Modelspend capabilities are not yet generally available.
How are price and timelines determined?
Scope, milestones, responsibilities, timelines and commercial terms depend on the specific work and would be agreed separately, rather than promised by this website.
Tell us what you’re considering
Share an early idea, an existing challenge or a product you want to improve. We’ll start with the business need and work forwards.