$ technomers --version 2.0

Engineering Excellence,
Global Impact

Cutting-edge product engineering and consulting from Mangaluru to the world. Specializing in advanced technologies across multiple domains.

Our Services Get in Touch

Trusted by Industry Leaders

We work with clients across various industries and verticals

Serving clients across India, USA, and Singapore

In verticals including Cloud Services, BFSI, Robotics, Clean Energy, Healthcare, VoIP, WebRTC, and Retail

$ technomers --services

Our Services

Comprehensive technology solutions built by developers, for developers

Product Engineering

End-to-end product development with cutting-edge technologies and best practices.

Scalable architecture

Performance optimized

Security-first approach

Comprehensive testing

function buildProduct(requirements) {
  const architecture = designArchitecture(requirements);
  const product = implementSolution(architecture);
  return deployAndScale(product);
}

Software Development

Custom software solutions using multiple programming languages and frameworks.

Scalable architecture

Performance optimized

Security-first approach

Comprehensive testing

const developSoftware = async (specs) => {
  const stack = selectOptimalStack(specs);
  await implementFeatures(stack, specs.features);
  return testAndDeploy(specs.environment);
};

.NET Development

Enterprise-grade applications using the .NET ecosystem and C# language.

Scalable architecture

Performance optimized

Security-first approach

Comprehensive testing

public class EnterpriseApp {
  private readonly ILogger _logger;

  public EnterpriseApp(ILogger logger) {
    _logger = logger;
  }

  public async Task<Result> ProcessData() {
    // Implementation
  }
}

Data Science

Extract insights and build predictive models from complex datasets.

Scalable architecture

Performance optimized

Security-first approach

Comprehensive testing

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

def build_model(data):
  X_train, X_test, y_train, y_test = train_test_split(X, y)
  model.fit(X_train, y_train)
  return model

AI & Machine Learning

Advanced AI solutions including LLMs and agentic frameworks.

Scalable architecture

Performance optimized

Security-first approach

Comprehensive testing

def build_ml_solution(data, problem_type):
  model = train_model(data, select_algorithm(problem_type))
  evaluate_performance(model, test_data)
  return deploy_model(model)

$ technomers --list-technologies

Our Technology Stack

Expertise across a wide range of cutting-edge technologies

// Our approach to technology

function technologyPhilosophy() {
return {
rightTool: "We choose the right technology for each specific challenge",
fullStack: "Expertise across the entire technology stack",
cuttingEdge: "Always learning and adopting the latest innovations",
pragmatic: "Balancing innovation with proven reliability"
};
}

languages.ts

Programming Languages

const languages = [

TypeScript,Dart/Flutter,Python,C#,Java,C++,Go,Rust,Swift,Kotlin

];

frameworks.jsx

Frameworks & Libraries

import { frameworks } from './tech-stack';

const expertise = [

React,Flutter,Angular,Next.js,Jetpack,.NET Core,ASP.NET,fastapi,Fastify,GRPC,Spring Boot,...

];

data-science.py

Data Science & AI

import numpy as np

def data_science_stack():

return [

TensorFlow,PyTorch,Pandas,NumPy,scikit-learn,R,Jupyter,Power BI,Tableau

]

databases.sql

Databases

SELECT expertise

FROM databases

WHERE name IN (

'PostgreSQL','MySQL','MongoDB','Redis','Cassandra','Tinybird','Clickhouse','DynamoDB','Elasticsearch','Neo4j'

);

cloud.yaml

Cloud Platforms

providers:

name: AWSname: Azurename: Google Cloudname: IBM Cloudname: Oracle Cloudname: DigitalOcean

deployment: automated

scaling: dynamic

devops.sh

DevOps & Tools

# Our DevOps toolchain

for tool in [

"Docker","Kubernetes","Jenkins","ArgoCD","GitHub Actions","Terraform","Ansible","Prometheus","Grafana"

]; do integrate $tool; done

ai-assistant.ts

AI Code Generation

technomers$|

$ technomers --stats

Our Technical Portfolio

Distribution across languages and verticals

const projectsByLanguage

200+ projects by language

Language

Color

JavaScript/TypeScript

30%

.NET/C#

20%

Dart/Flutter

10%

Python

10%

Java

10%

C++

Rust

/* Also: Swift, Kotlin, PHP, Ruby & more */

$ technomers --value-prop

Our Value Addition

What sets us apart as hardcore developers and engineering partners

// Our engineering philosophy

const engineeringPhilosophy = "An engineer must know everything about something and something about everything.";

// How we approach problems

function solveProblem(problem) {
understand(problem);
analyzeOptions();
implementBestSolution();
measureResults();
continuouslyImprove();
return optimalSolution;
}

technical-excellence.sh

Technical Excellence

$ We build solutions with:

- Clean, maintainable code

- Optimal performance

- Scalable architecture

- Comprehensive testing

✓ Result: Robust systems that stand the test of time

deep-expertise.js

Deep Technical Expertise

const expertise = {;

languages: [JS, TS, Dart, Python, Java, C#, C++, Go, Rust],

frameworks: [React, Node, .NET, Fastify, Rocket, gin, Spring...],

specialties: [Data Science, AI/ML, Cloud, DevOps],

databases: [SQL, NoSQL, Graph, Time-series],

};

devops-pipeline.yml

DevOps Excellence

# Our CI/CD pipeline ensures:

stages:

- build: Automated builds

- test: Comprehensive testing

- deploy: Zero-downtime deployment

- monitor: Proactive monitoring

# Result: Reliable, consistent delivery

performance.cpp

Performance Optimization

void optimizePerformance() {

// We optimize at every level

algorithmicComplexity();

databaseQueryOptimization();

frontendRendering();

networkLatency();

return blazingFastSystems;

}

security.go

Security-First Approach

func SecureByDesign() {

// Security at every stage

threatModeling()

secureCodeReviews()

penetrationTesting()

complianceChecks()

return trustedSystems

}

data-science.py

Data-Driven Excellence

import pandas as pd

from sklearn.model_selection import train_test_split

def data_driven_solutions():

# Extract insights from data

data = collect_and_clean()

insights = exploratory_analysis(data)

model = train_predictive_model(data)

business_value = implement_insights(insights)

return data_powered_decisions