AI & CHATBOTS
FOR FINANCIAL SERVICES
We build an assistant for specific workflows: guidance on products and their published terms; answers from the approved knowledge base. Complex enquiries go to a staff member.
A SPECIFIC TASK.
A VERIFIABLE RESULT.
We start with the process and quality criteria: what takes time, where errors occur and what outcome staff need. We select a solution to suit the task, available data and company constraints. The project serves financial service users and corporate clients.
AI IN YOUR TEAM'S
EVERYDAY WORK
Understanding the request
Guidance on products and their published terms.
Answers from trusted sources
Answers from the approved knowledge base.
Taking useful action
Document checklists and enquiry status.
Human expertise
Handing personalised consultations over to a staff member.
PUTTING AI INTO
REAL WORKFLOWS
Your industry’s use cases
Guidance on products and their published terms. Answers from the approved knowledge base. We agree the scope of the first phase before work begins.
Data and access
We agree sources, the permitted processing environment and user roles.
Quality control
We prepare test examples, evaluation criteria and human-handoff scenarios.
Handover and development
Document checklists and enquiry status. Handing personalised consultations over to a staff member. We agree responsibilities and operating rules with your team.
AN ASSISTANT THAT
WORKS WITH YOUR KNOWLEDGE
Design concept for financial services companies: guidance on products and their published terms; answers from the approved knowledge base. These screens illustrate the workflow; the final interface is designed for your company.
FROM HYPOTHESIS
TO A WORKING AI SOLUTION
At every stage, we agree on the outcome and the next step.
Brief and industry objectives
We review the current process, roles and data. Priority workflow: guidance on products and their published terms.
Outcome: objective and scope of the first stage
Data and criteria
We check sources, access and ways to measure the result.
Outcome: quality evaluation plan
Pilot
We build a limited working version using agreed data.
Outcome: solution prototype
Quality assessment
We test answers and errors, assess costs and the employee's role.
Outcome: implementation decision
Integrations
We connect the required systems, permissions and working scenarios.
Outcome: integrated workflow
Launch and observation
We set up monitoring, instructions and a post-launch improvement plan.
Outcome: controlled operationsCHOOSE THE RIGHT FORMAT
FOR YOUR GOALS
AI projects start at $11,780. The final estimate depends on the task, data and integrations. Recurring model and infrastructure costs are calculated separately.
AI audit
To choose a task and assess feasibility.
Custom quotefor the agreed scope- Process and data review
- Implementation priorities
- Pilot criteria and plan
Pilot solution
To test one practical hypothesis.
from $11,780for the agreed scope- Prototype based on your workflows
- Test set and evaluation
- Operating cost estimate
Implementation and development
To bring the solution into the team's workflow.
Custom quotefor the agreed scope- Integrations and roles
- Monitoring and quality control
- Training and ongoing development
ANSWERS TO
YOUR QUESTIONS
How do you handle financial product terms?
We use approved materials and agree on their update process. Interfaces distinguish general information from personal advice; staff permissions are defined before launch.
How much does an AI project cost?
AI projects start at $11,780. After the audit, we agree the scope and final estimate. Model usage, infrastructure and support are priced separately.
Where should AI implementation start?
With one specific task that can be measured and tested. During the audit, we examine the process, data and constraints, then define the pilot scope.
Do we need our own large model?
Not always. Existing models and document search suit some tasks. We choose the architecture after assessing quality, cost and data requirements.
Where will the data be sent?
We agree the processing environment and services before the pilot. We define permitted sources, roles and the data that may be transmitted.
Can AI make mistakes?
Yes. We therefore set verification criteria, limits and cases that require employee confirmation in advance. Document-based answers include source references.
How do you measure value?
We compare the agreed task before and after implementation: time, quality and cost. The pilot results inform the decision to scale.
What is paid for after launch?
Model usage, infrastructure and support depend on the architecture and request volume. We estimate these costs separately before implementation.
A PROJECT HAS
A FACE AND A CHARACTER.
“I personally contribute to strategy and key decisions. A website should do more than impress — it should clearly and honestly explain why a customer should choose you.”
LET'S FIND A TASK
WHERE AI HELPS
Describe a process that takes up your team's time. We will suggest a pilot scenario and a way to evaluate the result.