Raphael Amponsah, PhD

AI & Data Product Architect

Banks, fintechs, institutions, and growing companies that want AI in production, not in a slide deck

AI Application Development for Banks and Institutions in Ghana

Custom AI assistants and AI features built on your own data, deployed so sensitive data stays under your control.

Book a free 30-minute call Send an enquiry Message on WhatsApp

Plenty of AI projects produce an impressive demo. Far fewer produce a system staff trust with real work. The gap is almost always the same: answers that are wrong in ways nobody notices, data that is not allowed to leave the building, and no one who owns the system after launch.

I build AI applications for institutions that need them to work in production: assistants that answer questions over your own documents and data, natural-language querying of databases, and AI features added to products you already run.

Where data cannot go to a public AI service, the system runs on local or private-cloud models. Every build includes evaluation and monitoring, so you know when answers are wrong instead of finding out from a customer.

The problem

Most AI projects stall between a promising demo and a system staff can trust with real work: wrong answers, data that cannot leave the building, and no one who owns it after launch.

What you get

Use-case selection: where AI will actually save time or money, and where it will not
AI assistants that answer questions over your documents and data (retrieval-augmented generation)
Plain-English querying of databases, and AI features added to existing products
Local or private-cloud deployment when data cannot go to a public AI service
Evaluation, guardrails, and monitoring so you know when answers are wrong

Format

Proof of concept in two to four weeks, then a scoped build to production

Why me

Built a natural-language-to-SQL engine and a local LLM assistant in Microsoft Teams for a commercial bank, and the AI platform behind OneRubric.

What makes an AI build work

Choosing a use case where AI saves measurable time or money, and saying no to the ones where it will not.
Grounding answers in your own documents and data, with sources shown, rather than relying on what a model remembers.
Testing answers against real questions before launch, then monitoring them after.
Deployment and access control your IT, security, and audit teams can sign off.

Common questions

Can AI answer questions about our internal data without it leaving our network?: Yes. Open models can run on your own servers or private cloud, so documents and data never go to a public AI provider. I built a local LLM assistant in Microsoft Teams for a commercial bank on this basis.
What is natural-language-to-SQL?: A system that lets staff ask a database questions in plain English, such as 'how many new savings accounts did Kumasi branches open last month?', and get an answer without writing SQL or waiting for the analytics team.
How long does a proof of concept take?: Usually two to four weeks, using a real use case and a sample of your real data. You then decide on a scoped build to production based on measured results.
What is retrieval-augmented generation (RAG)?: A way of building an AI assistant that looks up the relevant passages in your own documents first and answers from them, with sources, instead of answering from memory. It is the standard pattern for policy, product, and procedure assistants.

See the work

Talk about ai application development

Describe the problem in a few lines. I will tell you honestly whether I can help, and if not, who might.

Book a free 30-minute call Send an enquiry Message on WhatsApp

Other services

What you get

Use-case selection: where AI will actually save time or money, and where it will not

AI assistants that answer questions over your documents and data (retrieval-augmented generation)

Plain-English querying of databases, and AI features added to existing products

Local or private-cloud deployment when data cannot go to a public AI service

Evaluation, guardrails, and monitoring so you know when answers are wrong

Format

Proof of concept in two to four weeks, then a scoped build to production

Why me

Built a natural-language-to-SQL engine and a local LLM assistant in Microsoft Teams for a commercial bank, and the AI platform behind OneRubric.

What makes an AI build work

Choosing a use case where AI saves measurable time or money, and saying no to the ones where it will not.

Grounding answers in your own documents and data, with sources shown, rather than relying on what a model remembers.

Testing answers against real questions before launch, then monitoring them after.

Deployment and access control your IT, security, and audit teams can sign off.

Common questions

Can AI answer questions about our internal data without it leaving our network?

Yes. Open models can run on your own servers or private cloud, so documents and data never go to a public AI provider. I built a local LLM assistant in Microsoft Teams for a commercial bank on this basis.

What is natural-language-to-SQL?

A system that lets staff ask a database questions in plain English, such as 'how many new savings accounts did Kumasi branches open last month?', and get an answer without writing SQL or waiting for the analytics team.

How long does a proof of concept take?

Usually two to four weeks, using a real use case and a sample of your real data. You then decide on a scoped build to production based on measured results.

What is retrieval-augmented generation (RAG)?

A way of building an AI assistant that looks up the relevant passages in your own documents first and answers from them, with sources, instead of answering from memory. It is the standard pattern for policy, product, and procedure assistants.