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AI Process Automation — from pilot to production

BestCoding designs automations that connect AI models and business rules with eCommerce, ERP, CRM and helpdesk systems, and with email, documents and other data sources. We start from one process and a measurable goal. A pilot on a real data sample shows quality and cost before the solution goes to production with access control, monitoring and emergency procedures.

Tell us about your process See example scenarios
  1. Email / documents / eCommerce / CRM
  2. Rules + AI model
  3. Validation and human approval
  4. ERP / helpdesk / store / report
  5. Monitoring and action history
Controlled access
only the necessary data and permissions
Systems integration
eCommerce, ERP, CRM and helpdesk
Measurable effect
quality, cost and time
Cost control
limits, alerts and monitoring

Not every process needs AI

Business process automation does not always need artificial intelligence. If a decision can be described with clear rules, plain automation is cheaper, faster and more predictable. We use AI where the process involves unstructured text, documents, ambiguous classification or context interpretation.

  • Type of problem

    Clear, fixed rules

    Recommended solution

    classic automation and API

  • Type of problem

    Emails, text and documents

    Recommended solution

    an AI-assisted process

  • Type of problem

    Questions to company knowledge

    Recommended solution

    a document-based company knowledge base (RAG), with source citations

  • Type of problem

    Multi-step actions across systems

    Recommended solution

    an AI agent with a limited set of tools

  • Type of problem

    High-risk process

    Recommended solution

    AI as a recommendation + human approval

An example of our work

Automating support tickets and software fixes

Handling a new ticket meant reviewing the documentation, the earlier history and the source code, verifying the reported problem and assessing whether a software change was needed. Gathering this context by hand lengthened the time to prepare a reply and a fix.

  1. 1 A new ticket, together with its communication history, enters the system. The input is scanned and checked for security before any further processing.
  2. 2 The system analyzes the company knowledge base, the documentation and the source code, and verifies the reported problem.
  3. 3 It produces an analysis of the likely cause and a draft reply for the customer. If the problem requires a software change, the system prepares a code fix and runs automated tests.
  4. 4 The person handling the ticket sees the problem description, the sources used, the analysis, the draft reply, the prepared code changes and the test results in one place. They can review the materials and accept, modify or reject them.

The reply to the customer and any software change always require a human decision. Passing tests do not replace code review — the system prepares the analysis and working materials, but it does not send replies or deploy a fix on its own.

Result: The person handling the ticket no longer has to gather context from several places by hand. They get an analysis of the problem, the relevant fragments of documentation and code, a draft reply and a tested change to review — so they decide and prepare the solution faster.

Safeguards: scanning and validation of the input, limited access to data and the repository, automated tests, action logging, review of the prepared changes, and human approval before a reply is sent and a fix is deployed.

Have a similar process? Tell us

What we automate and how it works

What we automate

  • Operations across systems: data synchronization, moving records, catalog consistency
  • Messages and tickets: classification, priorities, suggested replies
  • AI document processing: extracting data from PDFs, spreadsheets and emails into structured records
  • Company knowledge base (RAG): answers based on documents, with references to sources
  • Product data and content: attribute normalization, descriptions and translations with quality control

How it works

  • AI models chosen for the task, and business rules where they are enough
  • Integrations via API and webhooks with PrestaShop, ERP, CRM and helpdesk
  • Queues, retries and idempotency for stable processing
  • Logging of actions and results from day one

Example scenarios: before and after

Supplier data

Before

A team member receives a PDF, spreadsheet or email, manually retypes parameters, unifies units and assigns categories.

After

The system reads the document, extracts the data, normalizes values, flags gaps and prepares a record for approval in PrestaShop or a PIM.

We measure
  • time to handle one document
  • share of data accepted without corrections
  • cost of processing one product

Ticket handling

Before

A team member reads the message, checks the order, terms and customer history, then writes a reply.

After

The system classifies the case, fetches data via API and drafts a reply aligned with company policy for approval. Unusual cases go to a human.

We measure
  • time to first response
  • share of cases handled semi-automatically
  • reply quality on samples

Company knowledge base (RAG)

Before

An answer requires searching procedures, guides and contracts scattered across many places.

After

A team member asks and gets an answer with references to the source documents, within their own permissions.

We measure
  • time to find an answer
  • share of answers with a correct source

Ways to work together

Three stages you can order separately. We usually start with analysis and a pilot before a production deployment.

1

Process analysis and implementation plan

Before building anything, we check whether and where AI makes sense.

  • a map of the current process and its bottlenecks
  • an assessment of whether AI is needed
  • required data and integrations
  • risks and proposed KPIs
  • an initial architecture and work plan
2

Pilot on real data

One scenario run on your data, with measured quality.

  • a working scenario
  • a test set and measured quality
  • an estimated cost per operation
  • a list of cases for manual handling
  • a recommendation: expand, change the approach, or stop
3

Production deployment and maintenance

A solution connected to your systems, with control and observability.

  • integrations and access control
  • monitoring, alerts and an action log
  • cost limits and emergency procedures
  • documentation and further optimization

AI under control

  • The model receives only the data needed to complete the task
  • An agent is granted only the necessary tools and permissions
  • High-risk actions require human approval
  • Inputs and results are logged with a defined retention
  • We check quality on representative cases
  • Cost, latency and errors are monitored
  • We design an emergency mode so that a model outage does not stop the whole process; it includes automatic retries and a kill switch
  1. Limited data access
  2. Result validation
  3. Human approval (high-risk actions)
  4. Action and result log
  5. Monitoring of quality, cost and errors
  6. Emergency mode
A model solution flow. The detailed architecture depends on the data, risk and integrated systems.

How the process works

  1. 1

    Process analysis

    Goal, input data, risks and metrics — set at the start.

  2. 2

    Pilot on real data

    Quality and cost measured on a sample of your data.

  3. 3

    Production integration

    Connection to your systems, queues and full observability.

  4. 4

    Quality control

    Validation, acceptance samples and an action log.

  5. 5

    Maintenance and growth

    Monitoring, cost optimization and further automations.

Related services

Frequently Asked Questions

What is AI business process automation, and where do you start?

AI business process automation combines rules, AI models and integrations with your company systems. We start from a repetitive process, the available data and a measurable result. If clear rules are enough, we recommend classic automation without an AI model.

Can AI be integrated with PrestaShop, another online store, an ERP or a CRM?

Yes, if the system exposes an API, webhooks, data import or another secure integration method. We can also connect the automation with a helpdesk, email, a PIM and other operational tools. We check the options and the required permissions before the deployment starts.

Can AI support order, return and complaint handling in eCommerce?

Yes. The system can classify a message, fetch order and shipment data, check the applicable rules and prepare a reply or a suggested action. Unusual cases, complaints and financial operations can always require human approval.

How does automatic document and email processing with AI work?

The system reads the content of messages, attachments, PDFs, spreadsheets or scans, then extracts and normalizes the data it needs. Depending on the format, we use parsing, OCR, validation rules or an AI model. Correct records can go to an ERP, CRM, PIM or the store, while ambiguous cases go for review.

What is a company knowledge base based on RAG?

RAG answers questions using your company documentation, procedures and other approved sources. The system retrieves the relevant passages before preparing an answer, so it can point to the sources used and respect the user’s permissions. It does not require training a new general model on all your documentation.

Does our data go to external models?

It depends on the chosen architecture and provider. The model receives only the data needed to complete the task, and we agree the data scope, retention, processing region and required permissions before the deployment. If confidentiality requirements are higher, we look at options that limit sending data outside.

How do you measure the quality of AI results?

Before the deployment we set a representative set of cases and acceptance criteria. Depending on the process, we measure things like result correctness, the quality of the cited sources, the number of corrections, the share of exceptions, and processing cost and time. The pilot results decide on further development of the solution.

What happens if the model returns a wrong result or an API goes down?

We use validation, timeouts, automatic retries, error logging and emergency procedures matched to the process. An incorrect result can be stopped or handed to a person instead of automatically entering the system. High-risk actions require additional control.

How much does an AI automation deployment cost, and can we start with a single process?

Yes — it is usually best to start with one process and a pilot on a representative data sample. The cost depends on the number of integrations, data quality, the required accuracy, safeguards and how results are verified. After analysis we define the scope, architecture, deployment cost and the expected running costs.

Who maintains the solution after deployment?

BestCoding can handle monitoring, alerts, updates, error handling and further optimization. We can also hand the solution, its documentation and an agreed support scope to the client’s team. We agree the maintenance model before the production deployment.

Have a process worth automating?

Tell us about the process and the data you have. We'll reply whether AI is needed here, the approach we see and how to measure the effect.

Tell us about your process Get in touch: contact@bestcoding.net