}

Engineering Benefit

Human Meaning

Algorithmic Clarity

( ) :

When numbers work for you

def

{

return

community of engineering craftsmen and tech enthusiasts

//

We believe that the value of numbers is not in their volume, but in their ability to transform reality for the better

//

#

}

‘When a forecast prevents losses rather than just plotting a graph'

# [ 0 ]

‘When optimization saves real resources — time, money, energy’

# [ 1 ]

‘When AI becomes an invisible assistant, not a complex toy'

# [ 2 ]

‘When a data-driven decision is clear and applicable here and now’

# [ 3 ]

digital_benefits = {

#

}

const services = {

‘AI Consulting’

> AI implementation strategy, potential assessment, ROI analysis

‘Development & Implementation’

> Creation and integration of custom models, RAG systems, agents, industrial ML solutions

‘Process Analysis’

> Diagnostics of business processes for automation using CV/ML/agents

‘Audit & Optimization’

> Examination of data, existing ML models, and AI infrastructure

‘Solution Design’

> Strategy for AI deployment, potential assessment, ROI analysis

‘Support & Training’

> Maintenance, fine-tuning models, training your teams

#

More info

#about the platform's advantages

>>>

manuspect (

)

Manuspect RPA () #automation of repetitive tasks

АProcess Analysis () #business identification and optimization

Platform for business process analysis and optimization

//

>>

Manuspect AI

#About the key levels and modules of PROM AI

>>>

)

>>

Industrial and engineering artificial intelligence

Predictive analytics & maintenance ()

Natural Language Processing (NLP) and RPA ()

Computer vision ()

Integration with IoT, AIIoT ()

Generative AI ()

Intelligent personnel management ()

Logistics & warehouse optimization ()

prom_AI ( #Process Optimization & Management AI

A Proven AI Implementation Methodology in 4 Key Steps

//

#

>>

Analysis ()

phase 1 {

}

> Identify bottlenecks (logistics, support, etc.)

> Map the “AS IS” state

> Clearly define goals for improvements

> Filling out the project brief

> Business process analysis

> Prepare technical specification

> Sign NDA

>>

Preparation ()

phase 2 {

}

> Define KPIs and success metrics

> Approve implementation schedule

> Budget preparation and ROI calculation

> Choose co-financing instruments

> Approve project contract

> Sign contract

>>

Development ()

phase 3 {

}

> Select and launch solutions for the task

> Pilot testing

> Evaluate solution effectiveness

> Complete key development stage

> Deployment and commissioning

> Training of personnel

Launch ()

phase 4 {

}

> Implementation analysis

> Technical support

The result is not just deployment of technology, but sustainable digital development of your business

> Updates and model retraining

> Optimization based on new data

Your R&D AI department for technological partnership

//

#

]

AI technologies in automation and control systems

Distributed and hybrid collective intelligence in robotics

Predictive analytics and intelligent decision support systems

Software-hardware platforms for industrial AI systems

Industrial cybersecurity

research = [

#

Research ()

Trend Analysis =

Monitoring SOTA (CV, RL, Multi-agent systems)

Applied Experiments =

Hypothesis testing on real industrial data (failure forecasting, swarm robotics optimization)

Proof-of-Concept (PoC) =

Building minimal working prototypes (e.g. an AI anomaly detector on Edge device)

>>

phase 1 {

}

Development ()

Industrialization of prototypes =

Creating AI Infrastructure =

>>

phase 2 {

}

Implementation ()

Pilot in production environment =

Cybersecurity =

System Optimization =

phase 3 {

}

Partnering with us means access to exclusive R&D resources, accelerated development of competitive solutions, and reduced technological risks. Together, we're creating the future of industrial AI.

“

“

Our mission is to make numbers useful. To turn them into a tool that serves people, business, and society. Into a tool that makes work easier, decisions smarter, resources more efficient, and life a little more predictable and fair.

Andrey Vishnyakov, CEO, Senior

Our material =

data, artificial intelligence

artificial intelligence

Our tool =

>>

>>

>>

>>

>>

To find meaning in digital noise

To apply the craft of AI engineering

To compose a reliable, ethical solution

To deliver value clearly, without unnecessary complexity

>>

>>

To find meaning in digital noise

To apply the craft of AI engineering

To compose a reliable, ethical solution

To deliver value clearly, without unnecessary complexity

Our craft

#

We are the artisans of the digital age. Our team brings together engineers, data scientists, and implementation specialists who know how to listen to businesses, understand their processes, and identify areas where AI can deliver maximum impact.

#

Weaving neural networks

#

#

//

14 years

12 years

7 years

5 years

15 years

3 years

5 years

4 years

1 year

2 years

1 year

Senior

Senior

Middle

Senior

Middle

Junior

Middle

Junior

figma

web 2, Go/js

rust/flutter

GO

js

rust/js

Python

Python

HR

GR

Аналитик

UX/UI

CTO

Full stack

Backend

Frontend

Full stack

NLP

NLP

Kira Zhuikova

Roman Morozov

Marius

Nikita

Anton

Alexey

Mikhail

Andrey

Evgeny

Maxim

Daniil

}

telegram =

email =

CEO, cofounder

role =

{

Andrey Vishnyakov

}

telegram =

email =

CPO, cofounder

role =

{

Nikita Zaiganov

}

telegram =

email =

CTO, cofounder

role =

{

Kirill Sakhnevich

Industrial AI, CV, ML, RAG, and agents are our specialty. Reliable, scalable solutions based on the best open-source and cloud technologies.

//

#

Category

Python: model training;
Java: enterprise backend;
JS: RAG web interfaces.

Examples of use

Python is the leader for ML/CV (libraries: TensorFlow, OpenCV);
Java/C++ — for high-performance tasks;
JS/TS — web interfaces and agents

Justification of choice

Python, Java, JavaScript/TypeScript, C++

Technologies/Tools

PyTorch: custom neural networks;
OpenCV: real-time video analysis

TensorFlow/PyTorch is the standard for DL; OpenCV is for image processing; Hugging Face is SOTA for NLP/RAG

TensorFlow, PyTorch, OpenCV, Scikit-learn, Hugging Face

Category

Examples of use

Justification of choice

Technologies/Tools

Node.js: API for agents;
Django: forecasting services

Node.js — asynchronous processing for agents; Django — integration with Python models; Spring—Boot — reliability for industrial systems

Node.js (Express), Django
(Python), Spring Boot (Java)

Category

Examples of use

Justification of choice

Technologies/Tools

Redis: model inference cache; Neo4j: knowledge graph relationships.

PostgreSQL — transactional data; MongoDB — documents for ML logs; Redis — caching;
Neo4j — graph queries for RAG

PostgreSQL, MongoDB, Redis,

Category

Examples of use

Justification of choice

Technologies/Tools

Kafka: потоковая аналитика;
AWS SageMaker: развертывание моделей

Kubernetes + Docker — container orchestration; cloud — GPU/TPU scalability;
Kafka — stream data processing

Kubernetes, Docker,
AWS/GCP/Azure, Apache Kafka

Category

Examples of use

Justification of choice

Technologies/Tools

Grafana: GPU load dashboards; Elasticsearch: anomaly detection

Model and infrastructure performance monitoring; ELK – real-time log and error analysis

Prometheus/Grafana, ELK Stack (Elasticsearch, Logstash, Kibana)

Category

Examples of use

Justification of choice

Technologies/Tools

LangChain: Chatbots with RAG;
MLflow: Experiment Tracking

LangChain — building RAG chains; Ray — distributed computing; MLflow — managing lifecycle models; RabbitMQ — asynchronous tasks

LangChain, Ray, MLflow, RabbitMQ

Category

Examples of use

Justification of choice

Technologies/Tools

#

#

©️2023-2025
All rights reserved.

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email =

github =

tg bot =

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