WE BUILD
PRODUCTS
THAT MATTER TO US
Apps we actually use and love
We're not just another tech company. We build the apps and tools we wish existed—ones that solve real problems we face every day. From family organization to development workflows, if we don't believe in it enough to use it ourselves, we don't build it.
We Build What We Actually Use.
Fureworks started from a simple philosophy: if we wouldn't use it ourselves, why would we expect you to? Founded by Aldo, a seasoned software engineer who got tired of building products for other people that he'd never touch, we're creating apps and tools that solve problems we face in our own lives.
Built by Users, For Users
Every product we create starts with a problem in our own workflow, family life, or daily routine. We dogfood everything—from the first prototype to the final release. If it doesn't make our lives better, it doesn't ship.
Quality Over Quantity
We'd rather build fewer products that we're genuinely excited about than chase every trend or market opportunity. Each app gets the attention it deserves because we care about the outcome—we're going to be using it every day.
Authentic Development
No corporate buzzwords or artificial roadmaps. We build based on real needs and genuine passion. Our approach is straightforward:
- Identify a problem we personally face
- Build a solution we'd actually want to use
- Test it in our own lives until it's genuinely helpful
- Share it with others who have the same problems
Our Values:
- Honesty-First: We only build what we believe in
- User-Driven: Real problems from real users (us)
- Quality-Focused: Better to do fewer things well
- Passion-Powered: Excitement drives everything we create
Current Focus:
Building developer tools and productivity apps that solve real problems we face every day. Our latest project, Accelerator, streamlines modern app development workflows.
Think We'd Build Something You Need?
We love hearing from people with similar problems. Maybe we're already working on a solution, or maybe you'll inspire our next project.
Apps That Solve Our Real Problems
Every app we build started as something we needed in our own lives. We're our own first customers.
Accelerator
Developer Tool Kit for Modern Apps
We built Accelerator because setting up new projects with OpenRouter LLM APIs, Supabase backend, and Cloudflare Pages deployment was taking too long. It's the SvelteKit template we use for rapid prototyping.
Everything You Need to Start
Other Problems We're Solving
DevOps AI Platform
Our deployment pipeline was a mess, so we're building the DevOps tools we wish existed—intelligent automation that actually works.
Document AI
Tired of manually processing documents and extracting data, we're exploring AI tools that can handle the tedious stuff for us.
Enterprise AI Suite
Working in large organizations taught us what enterprise tools get wrong. We're imagining better solutions.
Want to Try What We've Built?
Start with Accelerator—the developer toolkit we built because setting up new projects with modern tools was taking too long. It's open source and ready to use.
Try AcceleratorLab Notes
A collection of my thoughts, explorations, and insights into the world of AI, software engineering, and DevOps.
Automated DevOps: Build Faster, Smarter, and Save Time
Discover how embracing automated DevOps practices like CI/CD and IaC can dramatically accelerate product development, enhance quality, and free up valuable engineering time.
In today's fast-paced digital landscape, the speed and reliability of software delivery are paramount. Traditional development and operations often struggle to keep up, leading to bottlenecks, errors, and frustrated teams. This is where a robust, automated DevOps culture comes into play, transforming how we build, test, and deploy software.
Core Pillars of DevOps Automation:
- Continuous Integration/Continuous Delivery (CI/CD):
Automate the building, testing, and deployment of your code. Tools like GitHub Actions, GitLab CI, or Jenkins can orchestrate these pipelines, ensuring that every code change is automatically validated and pushed towards production if all checks pass. This drastically reduces manual intervention and speeds up release cycles.
// Example: A simple GitHub Actions workflow step - name: Deploy to Cloud Provider uses: google-github-actions/deploy-cloudrun@v0 with: service: my-awesome-app image: gcr.io/my-project/my-awesome-app:{{ github.sha }} - Infrastructure as Code (IaC):
Define and manage your infrastructure using code (e.g., Terraform, Pulumi, AWS CloudFormation). This ensures consistency, repeatability, and version control for your environments. No more manual server configurations that lead to drift and snowflake servers; IaC makes your infrastructure predictable and scalable.
- Automated Testing:
Integrate various levels of automated tests (unit, integration, end-to-end) into your CI/CD pipeline to catch bugs early and ensure code quality. Comprehensive test automation builds confidence in your releases and prevents regressions.
By embracing these core pillars, development teams can focus more on innovation and less on manual, error-prone tasks.
The result? Faster time-to-market, improved system reliability, and a more collaborative and efficient engineering culture. While setting up can require an initial investment, the long-term savings in time and resources, coupled with the boost in product quality, are undeniable.
AI Agents for Automated Data Extraction & Entry
A look into how AI agents can revolutionize tedious data extraction and entry tasks from various documents, boosting accuracy and efficiency. (Tech demo available!)
In countless industries, the manual extraction of data from documents (invoices, receipts, application forms, legal contracts, and more) is a time-consuming, error-prone, and often tedious task. This is where AI agents, specifically those powered by advanced Optical Character Recognition (OCR) and Natural Language Processing (NLP), are stepping in to revolutionize workflows.
How AI Agents Streamline Data Extraction:
- Automated Recognition: AI models can identify and read text from scanned documents or images, even in various formats and layouts.
- Intelligent Understanding: Beyond just reading text, these agents understand context. They can identify specific fields like "Invoice Number," "Total Amount," "Date," or customer names, regardless of their exact position on the document.
- Structured Output: The extracted information is then converted into a structured format (like JSON or CSV), ready for input into databases, ERP systems, or other business applications.
- Reduced Errors & Increased Speed: Automation significantly reduces human error and dramatically speeds up the entire data entry process, freeing up valuable human resources for more strategic tasks.
This demo below provides a glimpse into this capability. You can upload an image of a document (like a utility bill or an invoice) and instruct the AI to pull out specific pieces of information. While this is a simplified example, it showcases the core power of AI in understanding and processing visual documents for data extraction.
Try uploading an image of an invoice, receipt, or a paper bill. Then, ask the AI to extract specific information like "What is the total amount?", "Extract all line items and their prices", or "What is the invoice number and date?".
Quick Guide: Interfacing with Popular LLMs
A practical introduction to connecting with leading Large Language Models using their APIs, covering basic setup and common use cases for your projects.
The field of Large Language Models (LLMs) is evolving at an incredible pace, with new models and capabilities emerging constantly. Each LLM possesses unique strengths, weaknesses, and specializations. For developers and researchers, this rapid advancement underscores the importance of being able to quickly test and compare a wide range of models. Efficiently managing access to these diverse LLMs and experimenting with them is crucial for selecting the best fit for any given task, be it creative text generation, complex problem-solving, or nuanced data analysis.
Providers like OpenRouter.ai simplify this process significantly. They aggregate numerous open-source and proprietary LLMs under a unified API, allowing you to switch between models with ease without juggling multiple API keys or SDKs.
This interactive demo utilizes the OpenRouter API to send your prompts to various leading LLMs. Simply choose a model from the dropdown, type your question or instruction, and click "Send Prompt" to see the AI's response. You can optionally provide your own OpenRouter API key. If you don't have one, you can create one at OpenRouter Settings. If no key is provided here, the demo will use a pre-configured key with limited access for demonstration purposes.
Join the Future of AI Development.
Be among the first to experience our revolutionary AI development platform. Get early access, provide feedback, and help shape the tools that will transform how intelligent products are built.
Early Adopters Get:
- • Priority access to beta features
- • Direct feedback channel to our team
- • Special launch pricing
- • Exclusive community access
Perfect for:
- • AI/ML Engineers
- • Product Teams
- • Startup Founders
- • Enterprise Dev Teams
Have questions about our platform or want to discuss partnerships?
Reach out to our founder Aldo directly at info@fureworks.com