The use case is not specific enough
An interest in AI has not yet been translated into a defined user, decision, workflow, and measurable benefit.
04 · Floatger service
Artificial Intelligence (AI) solutions
We identify practical AI opportunities, validate them against representative data, and engineer responsible features that fit existing products and operating workflows.
The context
When this service becomes valuable.
Apply AI where it can improve a real decision, task, or customer experience. The right starting point is a shared understanding of the problem—not a predetermined feature list.
Data is not ready for reliable use
Important information may be incomplete, inconsistent, sensitive, or difficult to access with the right permissions.
A prototype is mistaken for a product
A promising demonstration may not yet address evaluation, failure handling, security, cost, monitoring, or human review.
What the work may include
Connected capabilities, shaped around the engagement.
The exact mix is agreed after understanding your current situation, priorities, and constraints.
01
AI opportunity discovery
Prioritise use cases by user value, feasibility, data readiness, risk, and fit with the wider product.
02
Prototype and evaluation
Test model behaviour with representative tasks, explicit quality criteria, known limitations, and reviewable evidence.
03
AI application engineering
Connect approved models and data to product interfaces, retrieval, tools, permissions, and existing business systems.
04
Operational safeguards
Design human oversight, logging, feedback, monitoring, fallback behaviour, and cost controls appropriate to the use case.
Potential outputs
Tangible progress your team can use.
Outputs depend on the scope and stage of the engagement. They are agreed before delivery begins and refined as the work becomes clearer.
- 01A prioritised AI opportunity and feasibility assessment
- 02A working prototype evaluated against agreed scenarios
- 03An implementation architecture covering models, data, permissions, and integrations
- 04A release plan with quality, oversight, monitoring, and operating considerations
Planning the engagement
Make the inputs, evidence, and boundaries clear.
Useful delivery starts with the right context and an agreed way to evaluate progress—not an assumption that every possible concern belongs in scope.
What we need to understand
- The user, workflow, decision, and desired improvement
- Representative approved data and realistic evaluation scenarios
- Privacy, security, legal, brand, and human-review requirements
- Existing applications, integrations, operating ownership, and budget constraints
How progress can be evaluated
- The capability improves an agreed task on representative scenarios
- Limitations, failure states, and human escalation paths are understood
- Data access, monitoring, cost, and operational ownership are defined before release
Important scope boundary
AI output can be incomplete or incorrect and should not be presented as guaranteed. Suitability, model choice, data permissions, human oversight, and domain-specific legal or compliance review must be defined for each use case.
Delivery path
From context to a practical next stage.
Each stage creates enough clarity for the decisions that follow, while keeping the process proportionate to the work.
Frame
Define the user, task, evidence, risk, and value hypothesis.
Prepare
Review data access, quality, permissions, and evaluation scenarios.
Prototype
Test a focused approach and document behaviour, limitations, and cost.
Operationalise
Integrate, safeguard, release, monitor, and improve the capability.
Engagement shape
Start at the point that matches the challenge.
AI opportunity workshop
Identify, compare, and prioritise feasible use cases before selecting a build direction.
Prototype and evaluation
Create a bounded proof of value and evaluate it against realistic tasks and quality criteria.
Production AI feature
Engineer the selected capability into a product with appropriate controls, integrations, and operating practices.
Service questions
Useful things to clarify.
Do we need to know which AI model to use?+
No. Model selection follows the use case, quality criteria, data requirements, integration needs, operating constraints, and acceptable risk.
Can AI use our internal documents or product data?+
Potentially, when access, permissions, data quality, retention, security, and intended use are understood. The solution should expose only information the user is authorised to access.
How do you evaluate an AI feature?+
We define representative scenarios and criteria such as relevance, completeness, groundedness, task success, latency, cost, and safe failure behaviour. The appropriate measures depend on the use case.
Can AI decisions be fully automated?+
Some low-risk tasks may support greater automation, but consequential decisions usually need explicit controls and human accountability. The right level of oversight is defined during discovery.
Can you improve an existing AI prototype?+
Yes. We can assess its use case, prompts, data flow, evaluation, architecture, security, cost, and product experience before proposing a route to production.
Start a conversation
Let's discuss ai solutions.
Share the context, current state, and what you need to move forward. We will help identify a sensible starting point.