Intelligent Software Development on Demand
Invector Labs combines artificial intelligence with a high-end talent network of computer scientists and engineers to reimagine software development on demand for enterprises and startups.
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Why Invector Labs
The emergence of technologies such as artificial intelligence and blockchain, as well as the proliferation of global, freelance talent has challenged the traditional agency model for software development on-demand. Invector Labs is a new platform for a new era in software development with very tangible benefits over traditional approaches.
|Facts to Consider||Traditional Consulting Firms||Freelance Marketplaces|
|Engineering Talent||Constrained to the firm’s resources||Unvetted freelancers||Highly curated network of computer scientists and engineers|
|Team Composition||Whoever is available||Whoever you can find||Specialized teams of experts assembled by AI processes|
|Focus||Constrained to the firm’s expertise||Mostly web and mobile development||Groups of labs focused on emerging enterprise software areas such as artificial intelligence, blockchain technologies, advanced cloud computing, and several others.|
|Pricing||As much as they can bill||Hourly rate per engineer||Fixed price, scientifically calculated based on the characteristics of the project and team|
|What do you pay for?||Everything||Everything||Reusable code and AI models from other projects|
|Project Management||Depends on the firm||You are responsible for managing the project||Sophisticated project management methodology|
|After Deployment||You are on your own||You are on your own||Offer devops solutions to maintain and monitor your solution|
Hire the best data scientists, apply the latest AI research and technologies to your software solution.
Build decentralized architectures using blockchain and distributed ledger technologies.
Advanced Cloud Computing
Hire engineers that have built cloud-first solutions at scale.
Google’s New Framework for Solving Ranking Problems
2018.12.12 / 4min read. Read More
Cashflow on the Blockchain Part I: Tokenized Debt and Security Tokens
I regularly get asked what areas I find more exciting about security tokens and I always answer with a single line: “the ones we are not…
2018.12.11 / 4min read. Read More
What’s New in Deep Learning Research
OpenAI Wants to Quantify Overfitting in Reinforcement Learning Agents Using This New Technology
2018.12.10 / 5min read. Read More
Last Week in AI
Every week, my team at Invector Labs publishes a newsletter to track the most recent developments in AI research and technology. You can…
2018.12.09 / 3min read. Read More
The Security Token Anthology: November Edition
December is here and its time for another edition of the security token anthology. Below you will find the compilation of my writings…
2018.12.07 / 4min read. Read More
Achieving the Machine Learning Dream: Interpretability and Performance in a Single Model
Machine learning is a discipline full of frictions and tradeoffs but none more important like the balance between accuracy and…
2018.12.06 / 4min read. Read More
Inside Conflux: How a Computer Science Legend is Planning to Solve the Biggest Challenge in the…
Yesterday, a group of notable investors to the likes of Sequoia China, Baidu Ventures, crypto exchange Huobi, Metastable and IMO Ventures…
2018.12.05 / 5min read. Read More
Platforms vs. Networks: Decentralization Vectors in Security Tokens
The subject of decentralization is at the forefront of some of the most passionate debates in the security token ecosystem. Whereas the…
2018.12.04 / 5min read. Read More
What’s New in Deep Learning Research: Microsoft Also Wants to Use Neural Networks to Design Neural…
Choosing the right network architecture is a highly subjective aspects of deep learning systems. How many layers should we use? What’s the…
2018.12.03 / 4min read. Read More