Just want to say how energizing it is to see this space maturing through thoughtful products like Extend and Reducto. Congrats to both for your Series A. I’d also mention GetOmni, as they’re doing great work leading the open-source front with their ZeroX project. We’ve learned a lot by observing your execution, and frankly, anyone serious about document intelligence tracks this ecosystem closely. It’s been encouraging to see ideas we were exploring early last year reflected in your recent successes. No shame there; good ideas often converge over time.
When we started fundraising (previous to GPT-4o), few investors believed LLMs would meaningfully disrupt this space. Finding the right supporters meant enduring a lot of rejection and delayed us quite a bit. Raising is always hard, and especially in Spain, where even a modest €500K pre-seed round typically requires proven MRR in the order of €10K.
We’re earlier-stage, but strongly aligned in product philosophy. Especially in the belief that the challenge isn’t just parsing PDFs. It’s building a feedback loop so fast and intuitive that deploying new workflows feels like development, not consulting. That’s what enables no-code teams to actually own automation.
From our experience in Europe, the market feels slower. Legacy tools like Textract still hold surprising inertia, and even €0.04/page can trigger pushback, signaling deeper friction tied to organizational change. Curious if US-based teams see the same, or whether pricing and adoption are more elastic. We’ve also heard “we’ll build this internally in 3 weeks” more times than we can count—usually underestimating what it takes to scale AI-based workflows reliably.
One experiment we’re excited about is using AI agents to ease the “blank page” problem in workflow design. You type: “Given a document, split it into subdocuments (contract, ID, bank account proof), extract key fields, and export everything into Excel.” The agent drafts the initial pipeline automatically. It helps DocOps teams skip the fiddly config and get straight to value. Again, no magic—just about removing friction and surfacing intent.
Some broader observations that align with what others here have said:
- Parsing/extraction isn’t a long-term moat. Foundation models keep improving and are beginning to yield bounding boxes. Not perfect yet, but close. - Moats come from orchestration-first strategies and self-adaptive systems: rapid iteration, versioning, observability, and agent-assisted configuration using visual tools like ReactFlow or Langflow. Basically, making an easier life to the pipeline owner. - Prompt-tuning (via DSPY, human feedback, QA) holds promise for adaptability but is still hard to expose through intuitive UX—especially for semi-technical DocOps users without ML knowledge. - Extraction confidence remains a challenge. No method fully prevents hallucinations. We shared our mitigation approach here: http://bit.ly/3T5nB3h. OCR errors are a major contributor—we’ve seen extractions marked high-confidence despite poor OCR input. The extraction logic was right, but we failed to penalize for OCR confidence (we’re fixing that). -Excel files are still a nightmare. We’re experimenting with methods like this one (https://arxiv.org/html/2407.09025v1), but large, messy files (90+ tabs, 100K+ rows) still break most approaches.
I’d love to connect with other founders in this space. Competition is energizing, and the market is big enough for multiple winners. You guys, along with llamaparse, are spearheding from what I see the movement. Also, incumbents are moving fast. Like Snowflake + Landing AI partnership, but fragmentation is probably inevitable. Feels like the space will stratify fast, some will vanish, some will thrive quietly, and a few might become the core infrastructure layer.
We’re small, building hard, and proud to be part of this wave. Kudos again to @kbyatnal and @adit_a for raising the bar, would be great to chat anytime or even offer some workspace if you ever visit Spain!