Case preparation workspaces
Internal case preparation workspaces.
AI Application Development for
Fintech
Build AI features into financial software your team can use.
Zimozi develops AI applications and product features for fintech businesses. We bring together the interface, backend, data connections and review workflow so AI output fits the wider application.
Document review tools
Document analysis and review tools.
Support assistants
Product and customer support assistants.
WHAT WE BUILD
AI applications we build.
Case preparation workspaces
Internal case preparation workspaces.
Document review tools
Document analysis and review tools.
Support assistants
Product and customer support assistants.
Report drafting
Report drafting from approved information.
Platform features
Text and document features inside existing fintech platforms.
A PRACTICAL FIRST STEP
Build beyond the model response.
The starting point
A usable AI feature needs permissions, saved records, error handling and a way to review results. We define those application requirements alongside the model task.
How we scope it
The project can start as a focused pilot or extend an existing product.
Case preparation workspaces
Internal case preparation workspaces.
Choose the approach around the task
We assess model APIs, retrieval and hosting against the data, quality requirements and operating costs. Fine-tuning is considered only when suitable training data and a clear benefit justify it.
Test quality and application behaviour
Acceptance criteria cover usefulness, unsupported claims, latency, cost and failure handling. Application tests also cover authentication, source access and saved records. Support can include model changes and evaluation of updated behaviour.
From idea to everyday use.
Discover
Understand the users, current process and intended result.
Assess
Review the data, systems, access and review needs.
Develop
Build a focused pilot around the agreed use case.
Deploy
Connect the solution to the workflow and test exceptions.
Evolve
Monitor quality, usage and costs, then improve.
BUILT FOR REAL WORKFLOWS
Choose the approach around the task.
We assess model APIs, retrieval and hosting against the data, quality requirements and operating costs. Fine-tuning is considered only when suitable training data and a clear benefit justify it.
IN PRACTICE
A practical use case.
Test quality and application behaviour
The situation
A fintech operations team wants a workspace that prepares case summaries from approved documents.
The workflow
The application retrieves relevant material, creates a draft and lets staff check the source references. It records revisions and the approved result before export.
In context
This example illustrates a potential workflow, not a claimed customer result.
Project scope
Acceptance criteria cover usefulness, unsupported claims, latency, cost and failure handling.
Delivery details
Application tests also cover authentication, source access and saved records. Support can include model changes and evaluation of updated behaviour.
Frequently asked questions
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Yes, after reviewing the architecture, data flows and available integration points.
Not necessarily. Existing models and retrieval can support many tasks. Customisation follows the requirements.
Ownership, handover and third-party licence terms are defined in the project agreement.
YOUR NEXT STEP
Build the AI feature your product needs
Tell us who will use it, what they need to accomplish and how it should fit into your software.
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Tell us about
your project.
Share a short brief, or book a time to talk with our team.