AI for finance
AI that answers from your policies, contracts and procedures — with the source.
In an industry under tight regulatory oversight, a guessing model is not enough. We build knowledge-based systems — on your documents, with source citations and a full audit trail. On AWS. Your data stays with you.
The problem
Generic AI in a regulated industry is a risk, not a saving.
A public chatbot answers from what it "remembered" during training. It does not know your credit policy, your current fee schedule or last quarter's regulator guidance. A wrong answer about a product, a contract or a compliance procedure costs customer trust — and can cost you the regulator's attention.
In finance an AI answer must have a source. RAG reverses the order: it first retrieves the right passages from your controlled knowledge base, then the model answers — strictly on that basis, with a link to the document. Your staff can see where the information comes from. So can the auditor.
Use cases
Knowledge from your documents — where it is missing today.
Every one of these rests on the same pattern: controlled sources, citation, data with you. No model speculation.
Knowledge base on products and procedures
Staff ask, the system answers from current policies and internal procedures — citing the document and the paragraph. Instead of digging through network folders.
Customer support grounded in sources
A consultant gets a prompt based on the current offer and fee schedule — not a hunch. The answer comes from a document you can show the customer.
Support for analysts and compliance
Searching regulations, policies and contracts in seconds — citing the provision. The analyst gets the passage and the source, not a model output with no justification.
Internal search across policies and contracts
Legal, risk and operations ask about clauses, limits, terms — the system returns the exact passage from the contract or policy. Faster than searching hundreds of files by hand.
Risks
The AI Act and data: what you must keep under control in finance from day one.
The AI Act — some AI systems in finance are high-risk
AI systems used to assess the creditworthiness or credit score of individuals (other than for fraud detection) are classed as high-risk under the AI Act — with obligations around documentation, risk management and human oversight. Most AI Act provisions apply from 2 August 2026. We verify the classification during the audit, before the system reaches production.
GDPR, banking and professional secrecy
Customer data and internal documents cannot travel to public models. We build in your AWS account — you invoke the model, the data stays under your control and in your jurisdiction. Not a statement — architecture.
Auditability and human oversight
A regulator expects the basis of a decision to be explainable. A RAG source citation is not interface decoration, it is part of the audit trail. We design it in from the start, not bolt it on at the end.
Proof
We built an end-to-end product in production — we bring the same stack to finance.
100%
AI extraction accuracy on packaging data (validation set, n=200)
$0.0006
cost per AI scan in production
302,516
drug-interaction records in the production knowledge base
The numbers come from mojApteczka — Semitora's own product in the healthcare domain. They show the architecture and build quality we carry across industries. Case studies from the financial sector — coming soon.
See the full case studyQuestions
Before you ask
No. We build the knowledge base in your own AWS account (Amazon Bedrock Knowledge Bases). Documents, contracts and customer data stay under your control — they do not leave your infrastructure. That matters for GDPR as well as banking and professional secrecy.
Have policies, procedures and contracts your AI should answer from?
Let's start with an audit: we'll check whether your sources are RAG-ready, where your systems stand on the AI Act and how fast we can ship a working system — without experimenting on your budget.