From Collector to Founder: How My Passion for Historical Documents Led to an AI Startup

I’ve always been fascinated by historical documents. The thrill of holding an old manuscript, patent, or handwritten letter and uncovering the story it carries is like traveling through time. Over the years, I became an avid collector of these artifacts, from 19th-century French patents to century-old family letters, driven by a passion to research and preserve them for the future. Each document in my collection felt like a critical piece of history that I was responsible for safeguarding and understanding. However, as my archive grew, so did a realization: preserving and understanding these documents are two very different challenges.

The Problem

The first challenge I faced was simply reading and translating the content of these historical papers. Many were handwritten in old-fashioned cursive or in foreign languages (like French, or German), making them hard to decipher. Before the age of AI, the process of transcription and translation was tedious and slow. For years, I spent a significant amount of money hiring native speakers to translate my old French patents and letters. These documents were often so technical and nuanced that only specialized translators with historical expertise could do them justice. Even with the best professionals, the turnaround was slow, expensive, and sometimes inaccurate due to the archaic terminology and challenging handwriting. It wasn’t just me. Any researcher working with primary sources knows the pain. Scholars might spend weeks painstakingly transcribing a single letter or journal entry by hand, time that could have been spent analyzing the content. And if the document was in a language you didn’t know, you either had to find a translator or risk missing out on valuable information.

My own workflow for dealing with these papers was clunky at best. I would scan or photograph the pages, then try a patchwork of tools and services: maybe an OCR program (which often choked on elaborate 1800s handwriting), followed by Google Translate for printed text, or sending images via email to a translator for the really tough scripts. Often, I ended up with pieces of the puzzle in different places – a text file here, a translation there – which I’d then have to compile and proofread. It could take an entire afternoon to get through a single page of a letter, and a full patent dossier could occupy me for days or weeks. All this drudgery was the price to pay for pursuing the hobby I loved.

The tedium didn’t just cost me time; it risked the very insights I was after. I remember one French patent from the 1860s – ten pages of ornate cursive – that I finally got translated after many weeks. When I read the translated text, I discovered it contained an early concept for a device that predated a later invention by decades. It was a eureka moment, but one that I almost never reached because of the transcription barrier. I couldn’t help but wonder how many other discoveries were sitting in my files, just out of reach due to language or legibility. And beyond my personal collection, I knew others were struggling with the same problem. One collector had painstakingly transcribed hundreds of Civil War soldiers’ letters by hand, only to save them in random Word files and notes. Another researcher lost decades of work when his computer burned in the Palisades wildfire, a devastating loss that underscored how fragile digital storage can be. The need for a better way to transcribe, translate, and organize historical documents—while ensuring they are securely stored—was painfully clear.

The Breakthrough

In late 2023, everything changed. Artificial intelligence finally caught up to our needs, and the breakthrough came in the form of a familiar tool: ChatGPT. When ChatGPT introduced vision (image) support, it was a revolutionary moment for me. Suddenly, I could feed a photograph of an old manuscript into the AI and get highly accurate transcription et translation back almost instantly. Tasks that used to take me weeks were done in seconds. The AI didn’t just spit out words; it actually understood them in context. It recognized that an 1850 French patent might use antiquated technical terms and translated accordingly. In fact, it often grasped the historical and technical context so well that it outperformed human translators in precision. Documents that had once been practically unreadable to me were now not only legible, but clear in meaning. I was able to research more, translate more, and uncover stories that would have taken months or years to unravel before. It felt like I had a superpower – an AI research assistant who never got tired and could read old cursive better than I could.

I can’t overstate how magical that felt. I remember holding a French report from the 1820s, written in flowing French script, which I had acquired but never fully understood. I snapped a few photos, and within moments ChatGPT had given me not just a transcription of the French, but a fluent English translation. For the first time, I understood that the document described groundbreaking experiments with a steam-powered artillery invention by an English mechanic named Mr. Perkins. That was a far cry from the frustrating nights I’d spent squinting at such letters with a dictionary in hand. I began to blitz through my backlog of documents. Handwritten notes, faded correspondences, obscure patents – each became instantly accessible and translatable. I found that the AI could even handle archaic spelling and old idioms with surprising accuracy. It was like having a team of experts on call 24/7, willing to translate any page I needed. The painstaking process I once knew had become, in large part, a push-button solution.

However, this huge leap forward created a new kind of problem. Yes, I could suddenly translate an old document in seconds, but now I was drowning in data and files. My images, transcriptions, and translations were scattered everywhere — on iCloud, in my email attachments, in a dozen SmugMug gallery links, and of course, lost deep in lengthy ChatGPT chat threads. The chat interface, while great for a quick result, wasn’t meant for managing a research project’s worth of material. I often hit the AI’s context limit, meaning I could only process so much in one conversation before I’d have to start a new one (losing all the previous thread’s memory). For a single long document, I found myself splitting it across multiple chat sessions and then manually reassembling the translations. It was messy and inefficient. There I was, with this incredible new capability at my fingertips, and yet I was still juggling folder after folder of images and text files like it was 1999. I realized that while the AI solved the transcription problem, it highlighted an organizational one: I needed a way to store, organize, and easily retrieve all these newly digitized texts.

And I wasn’t alone in this struggle. My collector and researcher friends were experiencing the same thing. We’d gone from no transcription to instant transcription, but now we had a chaos of files to make sense of. How do you keep track of which scans have been transcribed? Which ones have been translated? Where is that one quote you remember saving last month? One researcher friend confessed that he had transcripts saved across Word documents, Apple Notes, and even screenshots on his phone – a scattered digital trail that was bound to cause mistakes or losses. We had all these digital treasures and no central treasure chest to put them in. After hearing about my other friend’s wildfire incident and seeing how easily years of work could go up in smoke, I knew we needed a solution before another disaster (natural or digital) struck. It was time to create a proper home for our historical documents in this new AI-enabled world.

Building Document Transcribe

So, I decided to build that solution. I have a background in software and data engineering, which meant I had the know-how to develop a web application – at least in theory. The challenge was doing it fast and on a tight schedule. I’m not a large company or a research lab; I’m just one person with a full-time job and a family, working on this project late at night after the kids are asleep. In the past, creating a complex platform to handle image uploads, AI transcription, translation, and a database for organization would have been a monumental task for a lone developer. Honestly, it might have been impossible to achieve in my spare time. But I had a secret weapon: the new generation of AI coding tools. I leveraged AI pair-programmers like Lovable et Cursor to rapidly develop the first version of what would become DocumentTranscribe.com. Tools like Cursor and Lovable AI are part of this new wave of development platforms that make coding and app creation faster and more accessible.

To put it simply, these tools helped me write code smarter and faster. Cursor is an AI-enhanced IDE (Integrated Development Environment) built on Visual Studio Code that can autocomplete code and even generate entire functions based on a description. It felt like having an expert co-developer suggesting improvements and handling boilerplate while I focused on the logic and design that I wanted. On the other hand, Lovable is an AI-driven full-stack development platform. You just describe what you want in plain language, and it can generate whole chunks of the application, including the front end, back end, and database, to match. Using Lovable was almost like describing a dream app to a very talented (and very fast) engineer and watching them build it in front of you. I would type out something like, “I need a user upload page that accepts image files and a backend service that calls an OpenAI API to transcribe text,” and Lovable would draft out the code structure for that feature. Of course, it wasn’t perfect – but it gave me a huge head start.

I’d estimate that the AI tools handled about 90% of the heavy lifting for the initial codebase, which saved me an enormous amount of time. What might have taken me six months to code from scratch, I was able to get running in a matter of weeks. I’d come home from work, spend a couple of hours describing features to Lovable, and then switch over to Cursor to fine-tune and debug the output. Rinse and repeat, night after night. It was exhausting, but also exhilarating. I was literally watching an idea turn into a real, working product faster than ever before. Crucially, I could do this without neglecting my day job or family. In previous eras, a solo founder might have needed to quit their job or secure funding to hire a team in order to build something like Document Transcribe. But here I was, essentially pair-programming with AI on my couch, building a fully functional platform in my free time.

Of course, that last 10% of the project, the polishing touches, the bug fixes, and the little UX details that make a tool truly reliable still required plenty of human effort. The AI could draft code, but I often had to debug integration issues or adjust the logic to handle edge cases. However, knowing that the foundation was solid (and built so quickly) gave me the freedom to focus on those details. By the time I had a minimum viable product, Document Transcribe was able to do what I needed it to: securely store document images, transcribe handwriting to text, translate that text, and organize everything in a structured way. I initially built it just for myself, to scratch my own itch, but I quickly realized this could help many others in the community who had the same problem. I opened it up for some friends to test, and their reactions convinced me I was onto something. One historian friend uploaded scans of his great-grandfather’s diary and was thrilled to see the AI transcribe the old German Gothic script. Another, an archivist at a small museum, used it to translate a batch of 100-year-old letters in minutes. Empowered by these results, I decided to turn Document Transcribe from a personal project into a proper startup.

The Impact

Today, Document Transcribe is solving a real pain point for collectors, researchers, and small archives alike. What started as a personal workaround has evolved into a platform that centralizes transcription, translation, and organization of historical documents in one place. Essentially, it’s an all-in-one digital workspace for the kind of work I and my peers used to do across dozens of tools. The impact has been immediate and rewarding to see. Instead of spending their time on manual data entry, historians and enthusiasts can spend it on actual research and discovery. Important letters, diaries, and records that sat unread due to language barriers or hard-to-read handwriting are now easily accessible. In fact, modern AI techniques have achieved near human-level accuracy at reading 18th- and 19th-century handwriting, so even the most challenging manuscripts can be transcribed with ease. That means a collector with a stack of faded 200-year-old letters can finally read them and understand them, without having to hire a rare-books expert to interpret each swirl of ink.

For example, a researcher using Document Transcribe can upload a scan of an old journal article or a medieval parchment, and within minutes get back a full transcription et a translation, ready to be read or cited. This virtually eliminates the waiting period that used to bog down historical research. I’ve heard from academic users who said that what used to take an entire semester – transcribing sources for a thesis – now takes a weekend, freeing them to focus on analysis and writing. Collectors have told me how satisfying it is to finally search their collections. All those digitized pages become searchable text, so you can type a keyword and instantly find which document (and even where on the page) a particular name or date is mentioned. No more flipping through binders hoping to spot the right passage. You can organize documents into projects or folders (for example, “Civil War Letters” or “Edison’s Patents”), add tags or notes, and instantly search through the text of all your documents in seconds. And because everything is stored in the cloud, it’s securely backed up so a catastrophe like a fire or hardware failure won’t erase your efforts.

The platform is also making a difference for small museums and archives that have limited resources. Often, these institutions have treasures in their collections such as letters, ledgers and reports that remain inaccessible to the public due to the cost and effort of transcription. With Document Transcribe, even a tiny archive team can digitize and process large volumes of material with ease. Instead of hiring a whole team of interns to type up old records, a single archivist can let the AI handle the bulk of the transcription work, only refining things as needed. By automating the tedious parts, even small organizations can process far more documents with the same staff, ensuring that cultural heritage is accessible to global audiences instead of locked in a filing cabinet. One museum curator told me that using the tool was like “having an extra staff member who works 24/7” – they managed to create a digital catalog of handwritten World War I letters in a fraction of the time it would normally take.

Perhaps the most meaningful impact to me is hearing stories of preservation and sharing. One user, a granddaughter who inherited a box of her grandfather’s letters from World War II, used Document Transcribe to transcribe and translate them (some were in French from European friends). She plans to share a compiled book of all the letters and their translations with her family in the future. Those letters, once nearly illegible and siloed in a box, became a shared piece of family history that everyone could read and appreciate. This is exactly the kind of outcome that inspires me. Seeing fragile historical documents get a new life in the digital age, and helping people connect with the past in ways they couldn’t before, is why I started this journey.

What’s Next

Standing at this intersection of history and technology, I often marvel at how far things have come, and how fast. A short time ago, I was resigned to the fact that I might never fully translate all the documents I had collected. Now, not only is that possible, but I’ve helped build a tool that can do it for others, too. And yet, I know we’re just scratching the surface of what’s possible. The experience of building Document Transcribe with AI assistance opened my eyes to how quickly the gap between what AI can do et what humans can do is closing. I mentioned that Lovable and Cursor got me about 90% of the way there, with me doing the final 10%. That gap is rapidly closing. Every month, these AI coding tools improve. The suggestions get smarter, the generated code gets cleaner, and the amount of manual tweaking required drops. It’s not far-fetched to imagine that in the near future, that last 10% might be handled by AI as well, or at least reduced to 1% with just a few hints from a developer. This means solo founders like me will be able to build even more ambitious tools in even less time.

For the field of historical document preservation, the implications are exciting. We’re likely to see AI models that can handle even older and more challenging scripts, like medieval Latin texts or ancient Greek manuscripts, opening up entirely new realms of research that currently demand highly specialized skills. I can envision a not-too-distant future where an archivist can point their phone at a page of an eighteenth-century diary and get an instant transcription, translation, and even a summary with context (“this appears to be a merchant’s account of a voyage, mentioning trade goods and prices in 1765…”). As AI becomes more adept at understanding context, we might get features like automatic metadata tagging: the AI could read a document and tag dates, locations, people’s names, or even detect the sentiment or purpose of a letter. This would be a huge help in cataloging and finding connections between documents. Researchers and collectors will benefit from a kind of augmented intelligence, where the grunt work is handled by machines and the human can focus on interpretation, cross-referencing, and storytelling.

On the development side, tools like Lovable are moving towards a world where anyone with an idea, even without deep programming experience, can create custom software to meet their needs. In other words, the barrier to building solutions is getting lower and lower. I was able to build Document Transcribe as a one-person part-time effort; in the future, that could be the norm rather than the exception. This democratization means more niche problems (like “I want to catalog and translate old family letters”) will get solved, because the people who understand those problems deeply can also create the tools themselves or have AI co-create with them. We’ll likely see a blossoming of specialized applications for heritage preservation, built by enthusiasts for enthusiasts, powered by AI under the hood.

For Document Transcribe, I’m continuously working on improvements. One lesson from AI development is that there’s always a newer model or technique on the horizon. I’m keeping an eye on advances in OCR and language models so I can integrate them and push the accuracy and features even further. For instance, newer AI models might handle complex table data in documents (like old ledgers) or automatically link a transcript to relevant historical reference information. The final mile of quality, that last bit of manual effort, is shrinking rapidly as AI improves. However, it’s still essential to carefully review outputs and ensure accuracy to maintain trust and reliability. After all, technology must always serve the ultimate goal of preserving history.

In the end, this journey from collector to founder has been deeply personal and rewarding. I didn’t start out intending to launch an AI startup; I just wanted to read my old documents and share their stories. The fact that the tools and timing converged to make this possible still amazes me. If there’s one takeaway from my story, it’s that passion coupled with technology can unlock incredible possibilities. We’re at a point where a history buff with a laptop and an idea can do what entire teams couldn’t do a decade ago. I’m excited to keep improving Document Transcribe and to see others join in this space, because there are countless archives, libraries, and attics full of history waiting to be brought to light. The past has a way of speaking to us when we can actually read it. And thanks to AI, more voices from the past are coming through loud and clear. I can’t wait to see what’s next, both for my own project and for the wider community of researchers and collectors. The work of preserving our history is never done, but for the first time, I feel like the odds are truly in our favor.

Check it out at DocumentTranscribe.com

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And if you’re a collector, researcher, or historian working with historical documents, I’d love to hear from you. What challenges do you face? Would a tool like this be useful to you? Let me know in the comments!