Case studies of automation, AI and IT systems for companies
See sample SmartCodeIT implementation scenarios: business problem, scope of MVP, architecture, system modules, risks, KPIs and possible next steps. It is a practical library of decisions for companies that want to organize processes without randomly selecting tools.
Select an area similar to your process, then check the range MVP.
Each example is described so that the client can see not only the end result, but also the implementation logic: input data, modules, integrations, error control, human responsibility and development after the first release.
Examples described by the problem, solution and effect to be measured.
The materials are anonymous or model. They do not show guaranteed results, only a professional way of thinking about the process, architecture, risks, KPIs and the first stage of implementation.
Construction / engineering / design companies
Model implementation / anonymous example
Automation of KSeF and Comarch Optima invoices for construction company
Cost invoice system: import from KSeF, assignment to the project, acceptance of the manager, sharing of costs and preparation of data to Comarch Optima.
Issue
The process was based on e-mails, sheets, phones and manual assignment of invoices to projects. The biggest challenge was to control the status, cost, responsible person and the readiness of the document to handle at Comarch Optima.
automatic or semi-automatic collection of invoices from KSeFlinking the invoice to the design, construction or orderpossibility of sharing costs between several projects
30-60%potentially less manual transmission of invoices after matching the process
AI OCR and classification of documents for administration
AI OCR organizes documents with KSeF, e-mails, PDFs, scans and photos: reads fields, classifies document type, suggests design or cost category and transmits exceptions to man.
Issue
The employees manually recognized the type of document, rewrited the data, selected the cost category, indicated the project or MPK and decided who the document should be sent to. With a larger volume, the risk of delays, mistakes, duplicates and lack of information which documents need to be corrected increased.
reading key fields with invoices, PDF, scans and photosclassification of the document type and detection of deficienciesSuggestion of a project, cost category, MPK or responsible person
6 min → 45 sexample shortening the preliminary description of the document with good quality scans
Dashboard combines data with CRM, ERP, invoices, payments, sheets and design systems, so that management and managers can see sales, margin, pipeline, charges and deviations in one place.
Issue
The management does not have one current source of truth. Sales, financial and design data are scattered, reports require manual connection, and delays make decisions based on incomplete or outdated information. There are also no clear definitions of KPI, quality control of data and alerts at deviations.
definition of a common KPI model for sales, finance and projectsautomatic collection of CRM data, invoices, payments and sheetsone dashboard with current indicators
8 h → 30 minexample limitation of the time of preparation of the cyclical report
Lead from the form goes to CRM, gets status, and the trader sees the task and context of the conversation.
Issue
There was a lack of control of response time, lead status, contact history and automatic follow-up, so some of the queries didn't have the owner or the bright next step.
Automation of repetitive office tasks: forms, e-mails, CRM, documents, notifications, reports and integration of APIs in one controlled workflow.
Issue
The work of the office was dependent on manual copying of data and employee memory. The data was sent to the organization from forms, e-mails, attachments, sheets and reports, but did not flow automatically between systems, resulting in delays, status deficiencies and risk of mistakes.
automatic transfer of data from forms, emails and webhooks to target systemsvalidation of data, deduction and control of required fields before recording in CRMcreating records, tasks, folders, documents and notifications without manual copying
5-15 hless manual work per week in the model scenario for verification
Services B2B / software house / technical companies / e-commerce B2B / education / production / consulting
Anonymous, model case study
Chatbot AI for customer service and lead qualification
Chatbot AI, which answers customers' questions, qualifies lead, collects contact details, creates records in CRM and transfers cases to the right team.
Issue
Before the implementation of the chatbot, many queries were sent to one email box or contact form. The person handling the message had to read the question, recognize the intention, ask for details, hand the case over to the trader or answer the frequently repeated questions manually.
answers to repeated customer questions based on an accepted knowledge basethe qualification of leades by questions about industry, problem, systems, scale, deadline and prioritycollecting contact details and context of the conversation before transferring the case to the team
30-60%less repetitive questions operated manually in the model scenario for verification
AI automation / e-mail workflow / medium company B2B
Anonymous, model case study
AI Agent to support email mailbox and priority message
Model AI-agent who analyzes new messages, recognizes intention, gives priority, assigns owner, suggests response, creates tasks, saves context to CRM and monitors SLA.
Issue
Each email required manual reading, evaluation of validity, transfer to the right person, preparation of responses and saving information in CRM, ERP, Excel or helpdesk. There was a lack of owner, priority, SLA and full history of the decision.
automatic classification of messages by intention, department, risk and priorityassigning the case owner and creating a task in CRM, helpdesk or workflow systempreparation of the suggested response based on an approved knowledge base
to 80%less manual message segregation in the model scenario to be confirmed in the analysis
AI / Knowledge Management / MAG / service, production and technology companies
Anonymous, model case study
Internal knowledge base AI (RAG) for company
Model implementation of the internal knowledge base AI based on MAG, which indexes documents, procedures and instructions, and then responds to employees on the basis of company sources.
Issue
The employees did not know which version of the document is current, where the procedure is, what the process of complaint, shopping, onboarding or system handling looks like. Experts were constantly distracted from work, and the classic search engine did not understand questions in the natural language and did not show an answer with sources.
building a single access point to documents, procedures, instructions and corporate knowledgeAI responses based solely on approved documents and source fragmentsSemantic search engine that understands employee questions without knowing filenames
up to 70%shorter search time in model scenario to confirm in analysis
Automatic generation of offers and sales documents from CRM
Model implementation of a system that generates offers, valuations, contracts, attachments, PDF/DOCX, e-mail and sales history updates based on CRM, ERP data.
Issue
The sales documents were created manually: the employee copied CRM data, checked current prices, improved discounts, filed a template in the Word, exported PDF, saved a file, sent an email and updated the status in CRM. As a result, there were different versions of documents, old prices, typos, lack of standardisation, difficult control of discounts and limited analytics of the effectiveness of offers.
shortening the preparation time of repeated tenders, valuations, contracts and annexesone standard of sales documents throughout the organisationautomatic data collection from CRM, ERP, price lists and product catalog
to 80%shorter time to prepare a repeatable offer in a model scenario to confirm in the analysis
Automation of employee onboarding and the digital circulation of HR documents
The dedicated HR Workflow system coordinates onboarding from acceptance of the offer to the end of the trial period: documents, checklists, equipment, accounts, trainings, statuses, reminders and reports for HR, IT and managers.
Issue
The onboarding process was dispersed between emails, sheets, Teams, PDF checklists, phones and paper documents. HR manually reminded about tasks, IT received incomplete information, managers had their own checklists, and new employees sometimes started without equipment, access to systems, signed documents or training set.
one workflow from acceptance of the offer to the end of the trial periodHR, IT, administration and managers tasks with the owner, date and statusdigital circulation of HR documents, statements, GDPR, regulations and signatures
up to 70%less manual coordination in a model scenario requiring confirmation in the analysis
Task and deadline control system for the project team
A dedicated operating system for design teams that arranges tasks, deadlines, owners, acceptance, delays, alerts and reports in one controlled process.
Issue
The biggest problem is not the number of tasks itself, but the lack of one place where you can see the owner, the date, status, lockdown, decision history and the impact of delay on the whole project. Managers manually check what is on time, and employees lose time to ask questions about priorities, decisions and overdue acceptances.
central register of tasks, stages and deadlines for projectsclear roles: the owner of the task, the acceptor, observer and managerdesign statuses tailored to the company's work, not imposed by the finished tool
E-commerce Automation / ERP / WMS / API integrations
Anonymised e-commerce model scenario
Integration of the online store with the warehouse, invoices and notifications
Integration platform for e-commerce that combines shop, warehouse, ERP, invoices, payments, couriers, statuses, notifications and reports in one controlled order handling process.
Issue
The team manually checks payments, rewrites data for invoices, broadcasts shipments, updates statuses and improves stocks. With a larger order volume, the risk of delays, lack of consistency of states, incorrect notifications and lack of one operational report increases.
downloading orders from the store and marketplacevalidation of payments and status of implementationsynchronization of warehouse, bookings and states
The automatic reporting system that downloads data from Excel, API, CRM, ERP and databases, arranges them and presents them in the Power BI dashboards and management reports.
Issue
The biggest problem was not the lack of data, but the lack of a consistent process of processing them. The data was found in reports from different locations, in different formats and with different frequencies, so reporting depended on people who knew the files, formulas and order of operations.
automatic data download from Excel, API, CRM, ERP, databases and sheetsvalidation of the quality of the data before showing them in the management reportone definition of KPI for sales, finance, operations and projects
70-90%less manual preparation of reports in the model scenario for verification
Application system, workflow and service for service company
The application goes into one system, receives status, owner, date and communication history.
Issue
There was one missing case status, an SLA, a responsible person and a delay report, so the manager didn't see which cases require response or escalation.
Case studies help recognize the right first step: audit, automation of one process, MVP, integration API, dashboard or a more complete company operating system.
When to start with an audit?
When the process is distributed, it is not known where to start, and the company needs a map of data, risks, costs and the first stage MVP.
unclear data sources
several departments in the process
no priority
When to build MVP?
When a process is repeatable, it has an owner and the statuses, users, exceptions and the effect to be measured can be clearly described.
statuses and roles are known
data are available
the result can be measured
When is human control needed?
When the system touches documents, payments, complaints, business decisions, sensitive data or high cost of error.
financial decisions
customer data
high risk processes
How we work
From diagnosis to implementation, without random selection of tools.
We start each implementation with process and data. Only then do we choose technology, automation, AI, dashboards or a dedicated application.
01
Audit
We recognize process, data, manual steps, exceptions and risks.
02
Process design
We establish roles, statuses, rules, integrations and the scope of the first stage.
03
Prototype
We show the screen layout and workflow before development.
04
MVP
We are building the first working scope that can be checked against customer data.
05
Integrations
We connect API, swap files, CRM, BI, KSeF, OCR, email or local systems.
06
Implementation
We launch production, train users and monitor errors.
07
Development
We add AI, reports, automations, alerts and further modules after MVP validation.
Technical scope
Each case study shows modules, data, integrations, architecture and possible extensions.
Prudent KPIs
The numbers are examples and require confirmation on customer data, volume and process.
Path to MVP
The materials help you more quickly determine the first safe stage of implementation.
Consultation
Do you want a similar solution in your company?
Describe the current process, tools, data, and goal. We will prepare a recommendation: audit, MVP, automation, integration API, AI, dashboard or dedicated application.