Datalab

Launch Week - Day 4: Spreadsheet Parsing 🚀 We just shipped native spreadsheet parsing in the Datalab API. Spreadsheets look structured until you actually try to parse them: ❌ hidden/collapsed columns ❌ staggered tables that overlap ❌ sparse grids with stray cells ❌ images of tables (seriously) Getting reliable structure out of grids is genuinely hard. We apply the same layout-aware parsing we use for PDFs to spreadsheets. This means you can finally automate workflows that were previously manual: ✅ Ingesting large financial models ✅ Normalizing messy loss runs ✅ Standardizing vendor price lists ✅ Extracting tables + metadata with sheet context Integration is simple: - No changes required if you're already using our API - Supports .csv, .xlsx, .xls, .xlsm, or .xlst - Pricing: $6 per 1,000 pages (500 non-empty cells = 1 page) Spreadsheet support is live via the API for all customers. If you hit edge cases or have degenerate samples, send them to us at support@datalab.to. More in the comments 👇

Launch Week — Day 3: Introducing Agni 🚀 Today we’re launching Agni, a new model in our pipeline that solves one of OCR’s core failures: consistent multi-page section hierarchy. Most OCR systems classify headers using only page-level cues, which breaks immediately in long documents — causing mis-leveled <h1>/<h2> tags, drifting subsections, and unstable TOCs and chunk boundaries. Agni fixes this with document-level reasoning. It processes all header candidates across the full sequence and assigns stable structure with <100 ms of overhead. Agni provides: 🔥 Document-level header modeling (not page-local). 🔥 Consistent <h1 → h2 → h3> hierarchy across 100+ pages. 🔥 Semantic + layout fusion for accurate level inference. 🔥 Drift-resistant structure even as formatting changes. 🔥 Robust handling of irregular patterns (STEM, examples, appendices). 🔥 Integrated by default across all Datalab parsing modes. This delivers cleaner structure, deterministic chunking, and far more reliable retrieval for RAG + long-context LLMs. Support for 1,000+ page docs is next — send us your hardest examples. Stay tuned - we still have 2 more days of launch announcements 👀 More in the links below 👇

Launch Week — Day 2: We made Chandra faster (Again) 🚀 Today we’re introducing Chandra Small, our new latency-optimized OCR model available now via the Datalab API. Chandra Small is 2–3x faster than the standard Chandra model with minimal performance degradation. We trained it with quantization-aware training (QAT), making it quantization-friendly and enabling even lower latency in production. A few highlights: ⭐ 2–3x faster inference ⭐ ~30% latency reduction from reduced token usage ⭐ 2–4 pages/sec on an H100 ⭐Maintains strong performance on benchmarks like olmOCR You can try Chandra Small today by using Fast mode in the API. Stay tuned for tomorrow's launch and in the meantime, check out the links below!

We're kicking off December with Launch week 🚀 Day 1: Chandra 1.1, our latest upgrade to Datalab’s SoTA OCR model. Chandra 1.1 brings big improvements across: - Layout (more accurate on long, complex documents) - Math (better on scientific formulas + olmOCR benchmarks) - Tables (handles large, messy, multi-hundred-cell tables) - Multilingual (80+ languages, major gains in Arabic + Indic) You can try it now in our playground or via our API. We’re already working on Chandra 1.2, so send us your toughest edge cases. And stay tuned 👀 four more announcements coming this week! Chandra 1.1 details in comments below!

We’re teaming up with Operators & Friends, Build., and pebblebed to host Building for the Real World, a focused hack night in San Francisco. Every percentage point of productivity gain in manufacturing, logistics, and infrastructure compounds across the entire economy. AI has the potential to unlock transformative improvements in these industries—we've seen our own customers like Purchaser and Bluon do exactly this. Join us on December 2 as we gather 100 builders at the pebblebed warehouse for six hours of building. Excited to support this event alongside OpenAI, ElevenLabs, Lovable, fal, Daytona and Vidoc Security Lab! Details to apply below!

We just made our OCR API 3× faster without sacrificing accuracy 😎 Last month, we launched Chandra, our state-of-the-art OCR model. But it wasn't fast enough by our standards. Customers process thousands of invoices, receipts, and contracts per hour. Every millisecond of latency matters for customer-facing products and high-volume automation. We implemented Eagle3 speculative decoding—a technique that uses a smaller "predictor" model to help the main model work faster by predicting multiple words ahead when text is straightforward. The results: ✅ 3× faster worst-case processing (p99 latency) ✅ 40% higher throughput ✅ 25% faster on average ✅ Zero accuracy loss What this means: - Faster customer experiences - Lower infrastructure costs - Handle peak loads without slowdown - Enable real-time processing Big kudos to Zach Nussbaum for this undertaking and for working to make these improvements available so quickly! Read his write-up in the links in the comments below 👇🏻

Congratulations to our friends at Replicate! Amazing team, product, and community. Excited to grow our partnership through this next chapter 🚀

Traditional OCR Benchmarks Are Hitting Their Limits. We hit 93.9% on olmOCR, a leading OCR benchmark by Ai2. But our newer models that worked better in practice weren't improving scores. Sometimes they got worse. The problem 🛑 Most benchmarks check if outputs match exactly, character by character. This fails outputs that are correct but formatted slightly differently: text with a newline, different capitalization, or spacing variations. When we tested whether our "failures" were actually wrong, many were correct but failed on formatting. With ~3-4% labeling issues, we're near the 96-97% ceiling of what these benchmarks can measure. The takeaway 📗 Traditional benchmarks worked to get OCR "good enough." Now they measure formatting quirks, not quality. This mirrors LLMs. Companies moved from generic benchmarks to real-world testing. We need better evaluation methods. If you're working on OCR evaluation or have similar challenges, send us a note at hi@datalab.to or DM us through our company page Datalab 🔗 to full blog post in comments below!

📝 If your teams handle contract redlines or document reviews, you know how time-consuming it is to compare versions, track edits, and summarize comments. Our new Track Changes Extraction feature automates that process. You can now extract insertions, deletions, and comments from Word documents with author names, timestamps, and context, all output in Markdown or HTML. Once extracted, this data can be sent to an LLM or integrated into your internal systems to: ✅ Generate redline summaries automatically ✅ Track negotiation patterns across counterparties ✅ Identify unresolved issues before final review ✅ Flag edits that shift risk or obligations This feature is already being used by hyper-growth legal-tech companies and legal teams that needed a more accurate way to handle redlines at scale. Track Changes is available now through the Marker API, your user dashboard, and our public Playground. Vikram Oberoi shares more in the links below!

We love to see our customers reach new heights. Gamma was one of our first customers, trusting us to power their PDF upload feature during a key period of growth. Grateful for their partnership and excited to keep building together. Congratulations to the Gamma team! 💙 🚀