AI implementation & training
We have spent over 10 years implementing AI in banks, where security and quality standards are the highest. We find where AI genuinely saves time, build the solutions, and train the people who will work with them.
What we do
Classes of systems we build
An agentic system is software that performs tasks inside a client’s systems and infrastructure. We build it for a specific process: it reaches into systems such as the CRM, databases or the document flow, performs operations there and leaves an audit trail. The range of operations it performs on its own is strictly defined, and it does not exceed the permissions it has been granted.
01
The same cases come back every day, and each one waits for a free consultant.
A request arrives by chat, by email or as a call transcript. The agent reads what it is about, pulls the data it needs from your systems and performs an operation it has been cleared for, such as sending a copy of an invoice or changing a correspondence address. Cases outside that list go to a consultant together with the data already pulled, so nobody has to read the whole thread first.
agent call
INV/2026/0317
ticket-handling agent
“I never got my invoice for March”
14:02:11
intent invoice_copy
14:02:11 · confidence 0.94
billing.fetch_invoice
14:02:12 · 1 result
mail.send
14:02:13 · copy sent, closed
02
The employee knows how to settle the case, but first clicks through several systems to gather the data.
The agent reads the case and pulls what belongs to it from your systems: customer history, balances, earlier correspondence. It lays that out in one view and proposes a resolution together with the clause it follows from. The employee approves the decision, and what they approved stays in the case record.
assembling context
CC/2026/0412
CRM
customer since 2019
Billing
overpayment 142.80
Docs
credit note 12 Mar
read-only access
proposal for the operator
uphold the complaint, refund PLN 142.80
basis: clause 3.2 · overpayment above PLN 100
03
An employee retypes data from documents and checks it against the procedure, line by line.
The agent reads the document and pulls out the items that matter: amounts, dates, parties, contract numbers. It checks them against your policy, the same one written down in the procedure today, and lets through whatever matches. If a condition is not met, the agent holds the document and routes it to a person who can approve it, naming which condition.
extraction and control
INV/2026/0412
invoice · PDF
33,200.00
IT / implementation
checked against purchasing policy 4.2
escalation · CTO approval
04
The answer is in the company documents, only nobody knows which one.
The question is asked in a plain sentence, with no query syntax and no knowledge of where the data sits. The system searches the documents and data you point it at, picks the ones closest to the question and discards the rest. The answer names its source, the document and the place in it, so it can be checked instead of taken on trust.
source selection
“What’s our limit for IT services?”
relevant documents
PLN 10,000; above that the CTO approves.
source: cost policy, paragraph 4.2
Additional services in AI and data processing
The data an agent needs sits in several systems, in different formats and under different permissions.
Obligations depend on the risk category a given system falls into. On legal and regulatory matters we work with Osborne Clarke.
Not every task needs a language model. Scoring, forecasting and classification run more cheaply and more repeatably on models trained on your own data.
Technologies we work with
Choosing the process
Not every process improvement needs an agentic system. If a deterministic path in code or a model trained on data does it more cheaply, we say so before anything gets built.
Implementation and wiring into your systems
The agent reaches into your documents, databases and APIs. We agree together the list of operations it performs on its own, and anything outside it goes to a person.
Measurement and production rollout
A set of cases with expected answers, a release threshold and oversight after deployment.
Project deliverables
Agentic systems
We co-created the core of this architecture: the solution design and the split into microservices, the skeleton that further agentic use cases plug into. The architecture lets them run against shared model throughput limits, counted in tokens per minute, and manages traffic and priorities so that no single use case consumes the whole limit at the expense of the others.
The system takes an instruction, finds the documents needed to assess it in the repository, the framework agreement together with its annexes, and recommends acceptance, rejection or a request for missing information. The hard part is reconstructing the current state of the terms: successive annexes overwrite parts of the framework agreement, sometimes several times over.
The system makes sure the procedure runs from start to finish, with no steps skipped and none taken out of order. The hard part is reconciling that rigour with a natural conversation: users ask side questions and a language model is inclined to improvise, so the system tracks the state of the procedure and returns to the right step after every digression.
Semantic search over user documents in a multi-tenant platform, where each user sees only what they are entitled to. Permissions are enforced before the search runs, not when the results are presented, so neither a semantic query nor an injected instruction can reach documents belonging to someone else.
We analysed screen recordings from operations teams along two routes, because the cost and the effectiveness of each depend on the kind of recording. A multimodal model took the recording whole, and the hard part was splitting it, carrying context between the parts and joining the result back into one piece. Splitting it into the audio track and sampled frames gave more control, but called for a considered sampling strategy and for laying the image and audio readings over one another. From the material gathered the agent reconstructed how the work actually ran, set it against the model process flow and the catalogue of inefficiencies, pointed to where the process gets blocked and picked improvement tools.
Manages customer conversations using a provided knowledge base, detects customer intentions and automatically executes operations.
An LLM-based system running as a browser extension. It reads CRM data about customer complaints, finds similar cases and suggests resolution steps. The hard part was integration: the agent uses many tools at once, each with its own access and its own data format.
Generates SQL queries fine-tuned to the specific database and its schema, from questions asked in natural language. The hard part was the complexity of the schemas: the more tables and relations there are, the less obvious it is which of them answer the question asked.
A search engine over a domain knowledge base in various formats, from PDFs to documents in SharePoint. Built on RAG architecture and language models. The hard part was scale: a very large number of users and documents, so usefulness depends on holding relevance and response time as the corpus grows.
The user says what they are in the mood for in a plain sentence and can refine it over further turns. The agent reads the intent and picks titles in a hybrid way: on metadata, on semantic similarity of the descriptions computed from embeddings, and on preferences, both those set explicitly and those inferred from viewing history.
Systems built on machine learning
5msuggestions a month
Payment Assistant
Reminds users of upcoming transfers based on transaction history, suggesting the recipient and the amount. Close to 1.5 million bank customers.
+80%sales potential
Credit campaigns
A system predicting interest in mortgage financing, at 20% precision.
−4%account closures
Customer churn prevention
A system predicting the probability of customer churn and raising an alert in the CRM.
How we work
The choice of model comes down to what suits you better: running the deployment with your own team, reinforced by our engineers, or handing it to us in full.
We take responsibility for the whole thing: requirements, data, model selection and validation, integration, deployment and monitoring in production.
We provide experts in agentic systems, RAG, evaluation, MLOps and DevOps. They work with you on building the solution.
Training
Two programmes, both live and on your premises: one for the board, one for engineers and product teams.
Which AI initiatives to fund, scale or stop — and on what basis?
How do you build products deliberately in an environment where LLMs are everywhere?
References
„For years, mBank has been synonymous with innovative banking solutions. We are one of the strongest and fastest-growing financial brands in Poland and Europe. The complexity of our operations and processes drives us to constantly seek cutting-edge solutions in data analytics, artificial intelligence, and machine learning. Our collaboration with Processifier, including the implementation of a process mining application and the integration of process data from multiple source systems, was carried out with the utmost attention to detail. We highly recommend Processifier as a trusted partner in both IT services and as a provider of tailored solutions in data analytics and process mining.”
Olga Kacprzak
IT Manager, Process Intelligence & Optimisation · mBank S.A.
„Our collaboration with Processifier involved a comprehensive analysis of the purchasing process using process mining techniques. Initially, Processifier's engineers mapped data from our SAP system into a format suitable for use with process mining analytical tools. After several iterations, we achieved results that enabled us to visualize the process across all our subsidiaries. Subsequently, in close cooperation with Processifier consultants, we conducted an in-depth analysis of the process. The insights presented in the process mining tool serve as the foundation for further efforts to optimize the P2P process. Processifier is a team of experts focused on rapidly delivering value and sharing knowledge related to the solutions they develop.”
Magdalena Kowalczyk
Continuous Improvement Manager · Veolia
Our products
Alongside client projects we build our own tools.
Thirty minutes to establish whether an agent is worth reaching for here at all, and what would have to be ready on your side.
Let’s talk