AI agents, document recognition and automatically assembled reports take over repetitive work; after launch we measure quality and maintain the models.
See the serviceAutomation and artificial intelligence
Artificial intelligence implementations
An AI agent takes over the stage in your system that consumes the most manual work and prepares a decision with the documents it relied on; one of your employees approves it.
Scope
What clients come to us with
Cases wait for a single assessment
Applications, complaints or orders pass through the assessment of one person or a small team. Each one requires gathering data from the system, reviewing attachments and deciding according to rules. A new hire spends months learning rules that nobody has written down.
The AI pilot runs alongside the system
Someone in the company has tried an AI tool: they paste the case into a chat window, get a suggestion and retype it into the system. The suggestions are accurate, but the work has grown, because copying now goes both ways. Nobody knows what customer data has left the company this way.
A delivery or contract waits for one expert
A delivery that deviates from the order, or a contract on the counterparty's terms, is assessed by one experienced employee, from memory and from sources only they consult. When they are on holiday, cases wait; when they leave, the company starts from scratch. Nobody can check why one delivery went through and another was stopped.
Scope
What we do
An AI agent is a program that carries out one stage of a process in full: it pulls the case data from your system, reads the attached scans, applies the rules and prepares a proposed decision. The model is the part of the agent trained on examples: for a new case it returns an assessment, an amount, a category or the text of a proposal. We build the agent around the stage where measurement showed the largest share of manual work.
The agent's proposal appears on the case screen in the system where the employee handles the case today: in the ERP, the system that manages the company, in the CRM used for customer service, or in an industry-specific system. The employee sees the proposal, the documents the agent relied on next to it, and three buttons: approve, amend, reject. Every case has a record: input data, proposal, documents cited, the employee's decision and their amendment. When the agent is not sure, it hands the case back without a proposal and notes what was missing.
The model runs in your server room or in the cloud in the European Union. Personal data and company secrets stay on your side: the agent sends the case to the model without them, and the company providing the model confirms in writing what it stores from the queries.
Reading correspondence, answering questions from internal rules, board reports and assessing a counterparty from its payment history are separate scopes.
When AI does not make sense here
We advise against an agent when the stage can be described in a few rules: ordinary automation in the system is enough. We advise against it when there are too few cases for the cost of building it, or when the case data is not in the system. We also advise against it when the cost of one wrong decision exceeds the annual work the agent replaces. In those cases the analysis ends with a recommendation without an agent.
Scope
What you get
- The choice of stage, with the reasoning: share of manual work, number of cases, cost of an error and a decision on whether an agent makes sense there.
- The agent running in your system: the proposal on the case screen and approval by an employee.
- A record of every case, from the input data to the employee's decision and amendment.
- Test results on your history: the agent's proposals set against employees' decisions.
- A description of where the data sits: where the model runs and which data reaches it, confirmed by the model provider.
Scope
How the work runs
- We measure the stages of the process. We count cases, manual working time and the cost of an error at each stage, and check whether the data is in the system. Result: a stage to take over, or the reasoning for why an agent does not make sense here.
- We test the agent on your history. The agent receives cases whose outcome you know, and we compare its proposals with employees' decisions. Result: an agreement table and a decision on implementation.
- We embed the agent in the system. The proposal appears on the case screen, the employee approves, amends or rejects it, and the record goes to the log. Result: the agent in daily work, with a person approving every decision.
- We hand over the switch and the measurements. A designated person receives a way to turn the agent off, after which the process returns to manual mode, and a view of agreement and cost. Result: the agent run by your team; further support is a separate scope.
Scope
Team experience
For a client in the financial sector, members of the team built an ERP and a CRM, introduced document recognition and automated the debt collection process. For another client in the same sector, they built a market analysis platform in which an AI assistant works. In healthcare, members of the team built an AI platform for in vitro fertilisation, run as a medical device under FDA, CE and MDR rules, for the EU and US markets.
Scope
Questions the board should ask the contractor
- Which stage of the process will the agent take over, and what measurement of manual work is behind that choice?
- Will employees handle the agent's proposal on the screen they work on today, or get a separate window?
- What does the agent do with a case it is unsure about: hold it, flag it or pass it on like any other?
- On how many cases from our history has the agent been tested, and whose decisions were its proposals compared with?
- How much does model usage cost per case, and how will that cost change if the number of cases doubles?
- Are employees' amendments recorded, and who uses them for the next version of the agent?
The answers depend on the number of cases and the share of manual work at each stage, so we start with a process analysis that ends with a choice of stage or a recommendation without an agent.
Parent service
Automation and artificial intelligence
Book a meeting
Book a conversation about the problem
You describe the process or system that no longer keeps up and what has to change. The conversation costs nothing.
Phone: +48 455 406 404
Automation and artificial intelligence
Other scopes of this service
Natural language processing
A program that reads your company's scans, e-mails and internal rules, turns them into data and answers that cite their source, and hands uncertain cases to a person.
See the scopeManagement analytics and reporting
Board reports calculated from the company's systems according to definitions agreed with you, with an alert when something deviates and a named owner for every figure.
See the scope
Data clean-up and data flow
One agreed dataset on customers, products and sales, fed from your systems by a data flow that shows whether the data has arrived, ready for reports and AI models.
See the scope
Looking after AI after launch
Your agent or model looked after once it is live: answer quality checked on current samples, usage costs under control, and a decision log ready for an audit or a complaint.
See the scopeHow we start
A conversation about the problem
You describe the process or system that no longer keeps up and what has to change. The conversation costs nothing.
Analysis under an NDA
We examine the process and the systems and hand over a recommendation with a scope of work. You know the scope, deadline and price of the analysis before it starts.
Offer
You receive the scope, team, schedule and price of the implementation. You decide whether we go ahead.
The cost of the analysis is credited towards the implementation if you order it.
Phone: +48 455 406 404 E-mail: contact@aliaxit.com