lift
Datalab turns PDFs, scans, and slides into structured markdown, JSON, and tagged output using its own Chandra OCR models.
Datalab is the pick when a missed field costs you more than a per-page fee. The benchmark numbers are published next to the price sheet — 90.7% on olmOCR-bench tables, 80.4% on its top-43-language set against Gemini 2.5 Flash's 67.6% — and the company publishes an auditable extraction benchmark (OmniExtractBench, 620 documents, deterministic grader) rather than a marketing chart. What you give up is flat-rate simplicity: you pay per processor per 1,000 pages, so a three-processor pipeline multiplies your cost, and the $400/mo Team tier is the floor for production rate limits (400 requests/min vs. 25 on Free). If you want conversational Q&A over a document, reach for a chat-based LLM instead
Verified 11h ago · liveness 65/100 · cite: rightaichoice.com/tools/lift
- AI research labs building training-grade corpora from web crawls, scientific papers, and 1000+ page books
- Financial services and insurance teams extracting structured fields from complex, non-standard documents
- Healthcare and clinical trial teams parsing documents in regulated environments where data cannot leave the network
- Developers composing multi-step document pipelines (convert, extract, evaluate) and promoting them to production
- Users wanting freeform conversational Q&A over documents — reach for a chat-based LLM instead
- Low-volume users: the Free tier's $20/month allowance runs out quickly on real documents
- Teams needing a fully no-code tool for non-technical staff — this is an API-and-SDK-first platform
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Skip Datalab if you want to chat with a document in a browser window, or if you're processing a handful of clean standardized forms a month where a flat-rate per-document OCR API will undercut a per-processor rate card.
Every processor in a request bills separately — a convert + extract + eval pipeline at $10 + $15 + $2 per 1,000 pages is $27 per 1,000 pages, not $15.
Free gives $20/month of usage on a work email ($10 personal) against the full rate card — enough to evaluate, not to run production. Team at $400/mo raises rate limits to 400 requests/min and concurrency to 400 and adds a BAA/DPA, admin MFA enforcement, and email + Slack support; the $400 becomes included usage, not a fee. Enterprise is custom for VPC, air-gapped, SSO, and volume discounts. Against flat-rate OCR APIs Datalab looks expensive on clean documents and competitive on messy ones;
In short
lift — Datalab turns PDFs, scans, and slides into structured markdown, JSON, and tagged output using its own Chandra OCR models. Best for AI research labs building training-grade corpora from web crawls, scientific papers, and 1000+ page books, Financial services and insurance teams extracting structured fields from complex, non-standard documents, Healthcare and clinical trial teams parsing documents in regulated environments where data cannot leave the network. Free to start; paid plans from $400/mo.
What's new in lift
Checked todayAcross the latest 5 updates: 3 feature updates and 2 news mentions.
Datalab releases OmniExtractBench, an auditable extraction benchmark
OmniExtractBench covers 620 documents drawn from four vendors' benchmarks, graded by one deterministic grader with per-decision explanations. Code and data are public.
Davis County School District uses Chandra for braille STEM conversion
A braille transcriber uses Chandra to convert math and science documents into braille-ready markdown and LaTeX, cutting QC time by roughly 90%.
Datalab adds track changes for PDFs and scans
Track changes now reads redlines off PDFs and scanned pages using a new vision model; insertions, deletions, and margin comments return as inline markup.
Form filling v2 runs on Datalab's document agent
Form filling v2 measures geometry instead of guessing and verifies each fill against the produced page; benchmarking surfaced three silent defects in v1.
Tagged, accessible PDF processor ships
A new processor converts PDFs, including pixel-only scans, into tagged PDFs built for assistive technology, validated by automated tests and a screen reader.
What people actually say about lift — is it worth it?
We scanned public community sources for lift on Aug 5, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is lift? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Convert processor: PDFs, spreadsheets, and slides to structured markdown, HTML, and JSON
- Extract processor: field-level structured data extraction against predefined schemas
- Segment processor: page-level and block-level boundaries between documents merged into one file
- Eval processor: score output quality against Datalab criteria or your own rubric
- Chandra OCR model handling messy scans, cursive handwriting, and complex layouts
- Tagged PDF processor: converts PDFs including pixel-only scans into accessible PDFs for assistive tech, validated with a screen reader
- JATS XML processor: converts scientific paper PDFs into DTD-validated JATS 1.2 XML with a conformance verdict per run
- Track changes processor: reads redlines off Word documents, PDFs, and scanned pages, returning insertions, deletions, and margin comments as inline markup
- Word-level bounding boxes with a confidence score for every word (add-on)
- Table extraction benchmarked at 90.7% on olmOCR-bench
- Multilingual parsing across 43+ languages via Chandra; open-source Marker/Surya covers 90+ languages
- Managed batch processing at 100M+ pages a day with allocated worker pool
- Form fill processor: populate a form's fields, with v2 measuring geometry and verifying each fill against the produced page
- Document generation processor: create documents from structured input
- Chart understanding (+$3/1k pages) and infographic parsing (+$4/1k pages) add-ons
About lift
Datalab is a document intelligence platform built by a research lab that trains its own OCR and parsing models — Marker, Surya, and the flagship Chandra model (11.1k GitHub stars). You send a document to a processor and get back structured markdown, HTML, or JSON. The processor lineup covers Convert (PDFs, spreadsheets, and slides to markdown/HTML/JSON with word-level bounding boxes and redlines), Extract (structured fields against your schemas), Segment (find boundaries between merged documents), Eval (score output against your rubric), plus newer primitives for form fill, document generation, track changes, chart and infographic parsing, and cross-page merging in beta. Recent 2026 additions include a tagged, accessible PDF processor validated by screen reader testing, a JATS 1.2 XML processor for scientific papers, and track changes that now reads redlines off PDFs and scanned pages via a vision model. A Document Agent is in closed beta — you specify the desired output, supply samples, and it builds a custom suite of verifiers and tools. Datalab owns the models, so you can run on the managed cloud (us-east-1, SOC 2 Type II, 99.99% uptime), inside your own VPC on AWS/GCP/Azure, or fully air-gapped on-prem. Managed batch is advertised at 100M+ pages a day. The buyers here are teams where a misread field is expensive: frontier AI labs building training corpora, financial services and insurance operations, clinical and healthcare teams, and developers composing multi-step RAG pipelines. Pricing is per-processor per 1,000 pages — Convert at $4 fast or $10 accurate, Extraction at $6/$15/$20 depending on tier — against a $20/month Free allowance on a work email ($10 personal) or a $400/month Team plan. The company publishes its benchmark numbers next to the rate card: 90.7% on olmOCR-bench tables, 80.4% on an internal top-43-language multilingual set against Gemini 2.5 Flash at 67.6%.
Behind the Verdict
The unusual thing about Datalab is that it tells you where it stands. Every number on the benchmarks section cites a public dataset or a runnable eval — 90.7% on olmOCR-bench tables against Chandra 2 OSS at 89.9%, 90.4% on arXiv math, 80.4% on an internal top-43-language multilingual set. In August 2026 the company ran a competitor's extraction benchmark, found major scoring bugs in it, fixed them, and published the result: Datalab scored 93.6% and led, and the accompanying post advised readers to run their own evals anyway. That is not how vendors typically behave, and it is the strongest signal on the site. Where the platform earns its place is at volume on documents that break simpler parsers. The processor model is composable rather than monolithic: you wire Convert, Extract, and Eval into a versioned pipeline in the playground, promote it to production, then monitor it with continuous evals against a rubric and a reference corpus so regressions get flagged when models change underneath you. For anyone who has been burned by an OCR vendor silently updating a model, that monitoring layer is the actual product. Strengths: own models (Marker, Surya, Chandra — 67.7k combined stars) means deployment flexibility that API-reseller competitors can't offer, including fully air-gapped on-prem with no internet, which is the only realistic option for some clinical and defense-adjacent work. Word-level bounding boxes with per-word confidence are available as a +$3/1,000-page add-on, which matters when you need to prove where a number came from. Extraction on the balanced and accurate tiers returns per-field citations and runs multi-pass with per-field verification — the $6 turbo tier returns JSON only, no citations. Weaknesses: pricing is genuinely hard to predict. Because every request pays for each processor it runs, a pipeline that converts, extracts, then evaluates pays three times; add chart understanding (+$3) and word bounding boxes (+$3) and a $6 extraction request becomes $12 before you've written a line of application code. The Free tier's $20/month allowance (on a work email; $10 on a personal one) evaporates quickly on real documents, and custom-processor creations are capped at 1/month — Team raises that to 4/month then $5 each. EU data residency costs 1.25× usage on every tier. VPC and air-gapped deployment are Enterprise-only. And this is not a no-code tool: it's API-and-SDK-first, with the playground for composition and testing rather than for non-technical staff. Where it fits: AI research labs building training corpora from web crawls, scientific papers, and 1000+ page books; financial services and insurance teams pulling structured fields out of non-standard documents; regulated teams that need the model inside their own network. Where it doesn't: anyone who wants to chat with a PDF, teams processing a handful of clean, standardized forms a month, and buyers whose entire decision is per-page cost on simple documents. The 2026
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Real-world workflow fit
Concrete scenarios for the personas lift actually fits — and what changes day-one when you adopt it.
Build a two-step pipeline in the playground — Convert on the accurate tier ($10/1k pages) to get citation-ready markdown from scanned statements, then Extraction on the balanced tier ($15/1k pages) with per-field verification against your own schema for line items, totals, and dates.
Outcome: Fields arrive with per-field citations you can show an auditor, and continuous evals against a reference corpus flag any regression when Datalab ships a model update.
Run web crawls, scientific papers, and 1000+ page books through managed batch at 100M+ pages a day with the worker pool allocated, using Convert to markdown plus chart and infographic add-ons where the source has figures.
Outcome: A clean, citation-ready corpus with word-level bounding boxes available as an add-on so you can trace any token back to its position on the page.
Deploy Datalab's models inside your own VPC on AWS, GCP, or Azure — or fully air-gapped with no internet connection — and run the same hosted API and SDK your developers already prototyped in the playground.
Outcome: The same extraction pipeline that worked in the playground runs inside the network boundary, with a BAA/DPA and admin MFA enforcement in place.
Use Cases
- Extract line items and totals from invoices automatically, with per-field citations on the balanced or accurate extraction tier.
- Parse receipt data for expense reporting and accounting.
- Convert contract fields into structured database entries.
- Digitize surgical or medical forms for health records.
- Process bank statements or pay stubs for loan applications.
- Classify and extract data from insurance claim documents.
- Build training-grade corpora from web crawls, scientific papers, and 1000+ page books for AI research labs.
- Convert math and science documents into braille-ready markdown and LaTeX — Davis County School District cut QC time roughly 90% doing this with Chandra.
Models Under the Hood
as of 2026-09-22
Limitations
- Per-processor pricing means cost scales with the number and types of processors each request runs — a convert + extract + eval pipeline pays three separate rates.
- Add-ons stack on top: word bounding boxes are +$3 per 1,000 pages, chart understanding +$3, infographic parsing +$4.
- On the Free plan you get a $20/month usage allowance on a work email ($10 personal), a 25 requests/min rate limit, 25 concurrent requests, and just 1 custom-processor creation per month.
- EU data residency costs 1.25× usage on every tier.
- VPC deployment and fully air-gapped on-prem are Enterprise-only.
- The extraction tiers differ meaningfully — turbo ($6) returns JSON only, while fast and balanced add per-field citations and multi-pass verification.
- There is no published connector or integration directory.
as of 2026-09-29
Verification history
We have re-verified lift 12 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 12 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published lift tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
A developer evaluating Datalab on real documents, or a small team running occasional batch jobs, who can live with a 25 requests/min rate limit.
What this tier adds
Free entry point: full hosted API and SDK at the same per-processor rates, with a $20/month usage allowance on a work email ($10 personal) and 1 custom-processor creation per month.
Team
$400/mo
Ideal for
A production team of engineers running document pipelines where rate limits, BAA/DPA coverage, and a support channel matter.
What this tier adds
Raises rate limits and concurrency from 25 to 400, turns the $400/mo into included usage, adds BAA/DPA and admin MFA enforcement, and lifts custom-processor creations to 4/month then $5 each.
Enterprise
Custom
Ideal for
Regulated or high-volume organizations that need the models running inside their own network with negotiated terms and volume discounts.
What this tier adds
Adds VPC deployment, fully air-gapped on-prem, SSO, custom rate limits and processor creation limits, volume-based discounts, and dedicated support with an MSA/SLA.
Where the pricing makes sense
The company stage and team size where lift's pricing actually pencils out — and where peers do it cheaper.
Free gives $20/month of usage on a work email ($10 personal) against the full rate card — enough to evaluate, not to run production. Team at $400/mo raises rate limits to 400 requests/min and concurrency to 400 and adds a BAA/DPA, admin MFA enforcement, and email + Slack support; the $400 becomes included usage, not a fee. Enterprise is custom for VPC, air-gapped, SSO, and volume discounts. Against flat-rate OCR APIs Datalab looks expensive on clean documents and competitive on messy ones;
Setup time & first value
How long it actually takes to get something useful out of lift — broken out by persona, not the marketing-page minute.
Managed cloud is the fastest path: sign up, get an API key, and ship a first Convert call in minutes — the free tier gives you $20 of usage on a work email to test with. Composing and testing a multi-processor pipeline in the playground is an afternoon of work for a developer, plus the time to write and validate your extraction schema against sample documents. VPC deployment runs through sales.
Switching to or from lift
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a flat-rate OCR API: run migration credits (up to $5,000) to move your workload, then swap the OCR endpoint for Convert and add Extract only where you need structured fields.
- →From self-hosted open-source Marker or Surya: point your pipeline at the hosted API to get the proprietary Chandra models and managed batch scaling without running GPUs.
- →From a per-document extraction service: rebuild your field schemas as Datalab extraction schemas, then validate with the Eval processor against your rubric before cutting over.
- →From an in-house OCR stack: deploy inside your own VPC or air-gapped so the data path doesn't change, and use the playground to reproduce your current outputs before promoting a versioned pipeline.
- ↗To open-source Marker or Surya: self-host the models you already benchmarked against, accepting lower table and multilingual scores in exchange for zero per-page cost.
- ↗To a flat-rate OCR API: replace the multi-processor pipeline with a single per-page endpoint, losing per-field citations and the Eval regression monitoring.
- ↗To a chat-based LLM: for ad-hoc Q&A over individual documents rather than structured extraction at volume.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “lift”, and we withheld 6: 6 could not be judged, because “lift” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about lift.
Official links
Tools that pair well with lift
Common stack mates teams adopt alongside lift, with the specific reason each pairing earns its keep.
Mistral OCR
Mistral OCR turns PDFs, scans, forms, and handwriting into markdown or structured JSON through a developer API.
LlamaParse
LlamaParse turns messy PDFs, scans, and Office files into clean markdown for AI pipelines, with Auto Mode cutting parse credits by up to
Mindee
AI document processing API that turns any invoice, receipt, or ID into structured JSON with zero model training.
Featured Head-to-Head Comparisons
Lift vs Temporal Ai
Temporal AI and Lift address completely different problems — durable orchestration vs. document parsing. If you're building AI agents or multi-step workflows that must survive failures, Temporal is the obvious choice, especially with its recent Workflow Streams and Task Queue Priority features. Lift is best for teams needing high-accuracy structured data extraction from invoices and forms, but its cloud-only deployment and per-page pricing may not suit sporadic low-volume users.
Lift vs Audioeye
Lift and AudioEye serve completely different needs. Choose Lift if your priority is extracting structured data from documents at scale with high accuracy. Choose AudioEye if you need to achieve web accessibility compliance quickly to reduce legal risk. They are not direct competitors; the decision is about your core business problem.
Lift vs Screenplayiq
ScreenplayIQ and Lift serve entirely different domains. Choose ScreenplayIQ if you're a film professional seeking data-driven script feedback and financial forecasts. Choose Lift if you need to automate data extraction from documents at scale. They are not competitors.
Label Studio vs Lift
If your job is extracting structured fields (names, totals, dates) from high volumes of invoices, receipts, or contracts with high accuracy and minimal setup, Lift's pre-built templates and confidence scoring are purpose-built. If you need to label images, transcribe audio, evaluate LLM outputs, or annotate video for custom AI training, Label Studio's open-source flexibility and broad data type support are unmatched. Choose Lift for operational document automation; choose Label Studio for experimental AI data work.
Alternatives to lift
View allMistral OCR
Mistral OCR turns PDFs, scans, forms, and handwriting into markdown or structured JSON through a developer API.
LlamaParse
LlamaParse turns messy PDFs, scans, and Office files into clean markdown for AI pipelines, with Auto Mode cutting parse credits by up to
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