Business Process Automation

Less manual work Lower business process costs

We rethink existing processes, remove unnecessary steps, and automate everything the system can perform reliably. Human involvement remains only where it genuinely adds value.

Find automation opportunities How we work
AI, software, data, and integrations are tools. Process economics is the outcome.
Process optimization

01 / Document processing

Scenario 01 of 03
Current process
Incoming email
Employee opens files
Reads documents
Copies data
Updates CRM
Notifies a colleague
BPFlow AI
Automated process
Email / API input
Extraction and classification
Automated validation
Automatic CRM update
Automatic next action
Exception → employee
Manual steps 5+ → 0–1
System actions 1 → 5+
Human involvement Exceptions
Business Process Audit

What should have been automated in your business long ago

Manual operations often remain not because they are necessary, but simply because the process has always been done that way.

The question is no longer whether it can be automated. The real question is why it is still being done manually.

01 / Data Manual transfer

Data is transferred between systems manually

An employee copies information from emails, spreadsheets, or one service into another.

Source → Employee → CRM
02 / Documents Manual review

Documents are read and checked manually

Data is extracted from documents manually, document types are identified, terms are checked, and routine decisions are made by people.

PDF → Reading → Action
03 / Reporting & BI Manual preparation

Recurring reports and metrics are prepared manually

Data is exported from CRM, ERP, databases, and spreadsheets, then metrics are consolidated and reports rebuilt manually each time.

CRM / ERP / DB → Data consolidation → BI / Report
04 / Operations Repetitive actions

CRM and ERP require constant repetitive actions

Create a record, change a status, check a field, send a notification, move data.

Event → Actions → ERP
05 / Search & Research Manual search

Online information is searched and analyzed manually

An employee reviews websites and documents, finds the required information, verifies sources, matches data, and prepares the result.

Internet → Search and analysis → File / Research
06 / Internal Knowledge Manual search

Employees search company knowledge manually

Information is scattered across files, emails, chats, and glossaries. Employees spend time searching and often do not even know where the right document or answer is.

Email / Files / Glossaries → Manual search → Answer?
Operating costs

If a process can be performed automatically while the company keeps paying for manual execution — those are already unnecessary recurring operating costs.

Automation Opportunities

Examples of business process automation

Practical examples where manual work, fragmented data, and routine actions can be replaced with automated processes.

01

Business processes and operations

We rethink the entire process: remove unnecessary steps, automate repetitive actions, and leave people only the tasks where their involvement is genuinely necessary.

  • automation of manual and repetitive operations;
  • routing of tasks, checks, and approvals;
  • automated actions across systems and employees;
  • human involvement only in exceptional cases.
Remove the unnecessary → Automate → Keep what is necessary
02

Documents, glossaries, and unstructured data

We automate document workflows and turn fragmented corporate knowledge into a simple, unified, accessible system.

  • processing PDFs, contracts, emails, and other documents;
  • data recognition, extraction, and classification;
  • building a corporate Knowledge Base;
  • intelligent search and an AI assistant for company data.
Documents → Knowledge Base → Answer
03

Speech, communications, and AI coaching

We turn conversations, calls, and meetings into structured data, insights, and concrete actions.

  • transcription of calls, meetings, audio, and video;
  • analysis of conversations and communication quality;
  • summaries, recommendations, and tracking of agreements;
  • AI coaching and personalized recommendations for employees.
Conversation → Analysis → Recommendation
04

Data, analytics, and forecasting

We combine data from different sources and turn it into metrics, forecasts, and a foundation for management decisions.

  • automated reports and analytical dashboards;
  • data collection, integration, and quality control;
  • forecasting, segmentation, anomaly detection, and metrics;
  • experiments, A/B testing, and specialized analytics.
Data → Analysis → Decision
05

CRM, ERP, and custom business systems

We automate existing systems or build custom ones around the company’s requirements.

  • CRM and ERP automation and integration;
  • custom CRM, ERP, and internal systems;
  • Odoo Community and specialized modules;
  • customer portals, internal services, and APIs.
Systems → Integrations → Automation
06

Internet, Telegram, and external resources

We automate information search and the actions employees currently perform manually on websites and external services.

  • online search, collection, and analysis of information;
  • automated work with websites and forms;
  • monitoring suppliers, prices, competitors, and markets;
  • Telegram bots, Mini Apps, and automated workflows.
Internet → Data → Action
07

Generative AI systems

We build automated workflows in which AI creates text, images, documents, and other materials, the output passes required automated checks, and is delivered to its destination without manual assembly.

  • automated generation of text and images;
  • preparation of documents, reports, and publications;
  • personalization of materials for a task or customer;
  • automated multi-stage AI workflows.
Search → Selection → Generation → Review → Result
Our Approach

Process first Technology second

We do not start by choosing an AI model, CRM, or software product. We start with the business process itself: which actions are truly necessary, which can be removed, and which are more efficient to automate.

01 Remove

Remove what is unnecessary

Automating an unnecessary step makes no sense. We first eliminate actions that create no value and exist only because the process evolved that way.

  • duplicate actions and data;
  • unnecessary approvals and handoffs;
  • unnecessary intermediate steps;
  • operations that do not affect the outcome.
Fewer steps
02 Automate

Give the system everything it can do reliably

Data, documents, checks, search, calculations, actions in corporate systems, and other repetitive operations should run automatically whenever human involvement is not required.

  • data and document processing;
  • search, analysis, and routine decisions;
  • actions in CRM, ERP, and other systems;
  • control, routing, and triggering of next steps.
Less manual work
03 Keep

Keep people where they are genuinely needed

People are involved not out of habit, but where their participation genuinely adds value or is required by the nature of the task.

  • accountability and consequential decisions;
  • negotiations and trust-based communication;
  • informal or difficult-to-formalize context;
  • genuinely non-standard situations.
People — where they are needed
Principle

We do not ask what can be delegated to AI. We ask what still makes sense to leave to people at all.

Solution Examples

How it works in practice

Not a standalone AI feature, but an end-to-end process — from incoming data to a completed action, document, or management decision.

01 Communications

Analysis of customer conversations

Before

Call and meeting recordings are reviewed selectively, quality is assessed manually, and a significant part of the information from conversations is lost.

Automation

The system transcribes the conversation, analyzes the content, captures agreements, evaluates defined parameters, and generates recommendations for the employee.

Conversation → Text → Analysis → Recommendations
Every conversation becomes structured data and concrete actions.
02 Internet

Information search and website automation

Before

An employee manually searches for companies, products, suppliers, or other information, opens dozens of websites, verifies data, and transfers the results into a spreadsheet.

Automation

The system performs the search, analyzes sources, collects and matches data, works with website forms, and produces the final file or report.

Task → Search → Review → Result
Hours of manual browser work become a controlled automated process.
03 Company Knowledge

Corporate Knowledge Base

Before

Documents, instructions, and accumulated knowledge are scattered across folders, emails, and systems. Employees do not always know whether the required information exists or where to find it.

Automation

Corporate materials are brought together in a Knowledge Base. The system understands document content, finds relevant information, and answers employees’ questions.

Documents → Knowledge Base → Search → Answer
Company knowledge becomes an accessible working tool rather than a collection of scattered files.
04 Generative AI

Automated content workflow

Before

A person searches for materials, selects the best ones, prepares the text and image, reviews the result, and publishes it manually in the target channel.

Automation

A single workflow collects data, evaluates materials, generates text and images, performs automated review, and passes the result on for publication.

Search → Selection → Generation → Review → Publication
One controlled automated pipeline replaces multiple manual operations.
05 Data / Semantic Matching
A familiar problem?

Semantically identical data is named differently, with no unified nomenclature

In CRM, ERP, Excel, and other sources, the same entity may be described in completely different words. There is no common text key, and ordinary string comparison cannot reliably determine that the records mean the same thing.

Before

Employees manually match rows across spreadsheets and systems, trying to determine which different descriptions refer to the same catalog item or entity.

Automation

The system compares not only how strings are written, but what they mean. Semantically similar records from CRM, Excel, ERP, and other sources are automatically linked to a single entity and assigned a unified ID.

Scheduled maintenance of office VRF and split systems Air conditioner service: diagnostics, cleaning, preventive maintenance Routine maintenance of climate-control equipment Quarterly HVAC maintenance and preventive service
Air conditioning system maintenance ID 000184
CRM / Excel / DB → Embeddings → Semantic matching → Unified IDs
A reference table with unified IDs is created and updated automatically. New data is matched to known entities, while exceptional ambiguous cases can be routed to a person for review when necessary.
Core principle

We automate not a single action, but the entire chain wherever it is economically and technically justified.

Technologies

Technology is selected for the task

AI, software, data, and infrastructure are parts of one system. We use the stack that enables reliable automation and sensible production operation.

01

AI and machine learning

OpenAI API LLM Multimodal AI Embeddings Semantic Search RAG Machine Learning
02

Backend and automation

Python REST API Webhooks Bash Background Jobs Event-driven pipelines
03

Data

PostgreSQL pgvector BI Vector Search Data Quality ETL / ELT
04

Business systems and integrations

CRM ERP Odoo Community Telegram Bots Telegram Mini Apps External APIs
05

Web and interfaces

HTML CSS JavaScript TypeScript Web Applications Internal Tools
06

Infrastructure

Linux Ubuntu Server Apache Git / GitHub Cloud On-premise
Principle

We do not sell a particular technology. We select the architecture and tools around the economics, requirements, and constraints of the specific process.

Data and Security

Business data stays under business control

We design the system architecture around requirements for personal data, internal documents, access control, and the use of external AI services.

Data processing architecture
Business data

Sensitive information stays within the environment defined for it

Personal data Internal documents Commercial information Financial data
Controlled environment

The system defines where and how data is processed

Access control
Local storage
De-identification
Activity logging
AI and external services

Only what is genuinely necessary is sent outside

Permitted data → API / AI
Sensitive data → local processing
01

Personal and sensitive data

We determine which data may move between systems, which must remain inside the controlled environment, and where additional protection is required.

02

Access control

We separate permissions for users and services, restrict access to data, and, where necessary, log system and employee actions.

03

Cloud / On-premise / dual environment

The architecture can use cloud services, local infrastructure, or split the system into external and protected internal environments.

04

Controlled use of AI

We send only the necessary data to external AI services. Where required, we use de-identification, local processing, or local models.

Principle

Architecture is determined by business, data, and security requirements — not the other way around.

NEWS

Major news from the world of artificial intelligence

News archive →
01

Barclays expands Claude across banking operations

According to Anthropic, Barclays is expanding its use of Claude in software development, employee information retrieval, and client-request handling. In Global Markets, models help process incoming emails by classifying them, adding information about the request, and selecting a processing route. Barclays UK also has a live assistant that retrieves information for colleagues serving customers. Wider adoption of Claude Code among developers is an expected next step, not an achieved rollout.

Read analysis →
02

Google launches Guided Vision in Gemini Live for Android

Google has launched Guided Vision for compatible Android devices. With the feature on, a person can share their camera during a Gemini Live conversation, hear descriptions, ask follow-up questions, and receive spoken prompts to adjust the camera for a clearer view. Google’s examples include reading fine print, finding objects, and describing nearby surroundings. Availability depends on more than the device: Gemini Live must also be supported in the user’s region and language.

Read analysis →
03

Google’s Project Suncatcher prototype satellite reaches orbit

Google reports that a Project Suncatcher prototype satellite, built with Planet, reached orbit on SpaceX’s Transporter-18 mission. The company says it has established contact and the satellite is operating as expected. The next step is to gather data on how Google’s tensor processing units, or TPUs, used for machine-learning workloads, withstand the stresses of spaceflight. The launch announcement does not yet contain results from those tests.

Read analysis →
Contact

Do you still have a manual process?

Show us how it works today. We will break down the steps, identify unnecessary operations, and determine what should be removed, automated, or left to people.

Where to start

Start by describing one real business process

You do not need to know in advance which technology, software, or AI model it requires. We start with the process itself and the business requirements.

Current process → Analysis → Automation → Result
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@bpflowai

Tell us briefly about the task or process you want to automate.

Discuss a project
Easy to start

No technical specification is required. Just show us what is currently being done manually.