Deasy Labs
Deasy Labs turns SharePoint, email archives, and PDF piles into curated, metadata-enriched datasets ready for RAG and agent pipelines.
Deasy Labs targets a real gap rather than another chat layer: the curation work between your unstructured repositories and your retrieval stack. Its defensible pieces are the ones a prompting wrapper can't fake — petabyte-scale sensitive data detection, taxonomy design and build, per-file quality and relevance scoring against a named use case, and auto-refresh so slices don't go stale. For an enterprise already running SharePoint, S3, and a retrieval framework like LlamaIndex, that's a meaningful shortcut. It is not a turnkey assistant, and the connected-source plus LLM-endpoint model means work before value. If you only need to label a few thousand documents, lighter tools will do.
Verified 6d ago · liveness 54/100 · cite: rightaichoice.com/tools/deasy-labs
- Enterprise AI teams running RAG or agent systems at scale
- Data engineers buried in manual preparation of SharePoint, email, and PDF repositories
- Organizations with petabyte-scale unstructured data needing automated sensitive data governance
- Business analysts who need to curate AI-ready datasets without coding
- Teams looking for a turnkey chatbot or end-user AI application
- Organizations with clean structured data and no unstructured silos
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Skip Deasy Labs if you need an end-user AI application rather than a data layer, or if your content is already structured and clean — Deasy's value is entirely in curating messy unstructured repositories upstream of your retrieval stack.
Deasy Labs is priced for enterprise data programs, not individual seats: it's a fit for organizations with petabyte-scale SharePoint, email, or PDF estates where curation replaces months of manual data engineering. Smaller teams with a few thousand documents will find lighter labeling and chunking tools cheaper.
In short
Deasy Labs — Deasy Labs turns SharePoint, email archives, and PDF piles into curated, metadata-enriched datasets ready for RAG and agent pipelines. Best for Enterprise AI teams running RAG or agent systems at scale, Data engineers buried in manual preparation of SharePoint, email, and PDF repositories, Organizations with petabyte-scale unstructured data needing automated sensitive data governance. Contact Sales pricing.
Viability Score
How well maintained and how widely used is Deasy Labs? 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: October 2026
How we score →Key Features
- Connect to SharePoint and Amazon S3 source repositories
- OCR, parsing, and chunking of unstructured files
- Automatic taxonomy design and build from your content
- Metadata tagging at thousands of files per minute
- Sensitive data detection at petabyte scale, every file screened
- File quality and relevance scoring against a specific use case
- Slice datasets by relevance, topic, time, quality, or sensitivity
- Write enriched metadata back to source systems
- Ship datasets downstream to RAG pipelines and retrieval systems
- Auto-refresh of datasets as new content lands in sources
- Centralized metadata governance: taxonomy, tag definitions, owners, accuracy
- Domain-specific metadata enrichment
- UI for business teams
- APIs and Python SDK for engineers
- Deploy in your own cloud environment
About Deasy Labs
Deasy Labs is an enterprise data curation platform for the step most AI projects skip: preparing the data before it powers a retrieval pipeline or agent. It connects to raw files in cloud sources such as SharePoint and S3, then OCRs, parses, and chunks each file in one pass. From there it learns your content, designs and builds a taxonomy, tags thousands of files per minute, screens every file for sensitive data at petabyte scale, and scores each file's quality and relevance against the specific use case you're building. With metadata in place you can slice datasets by relevance, topic, time, quality, or sensitivity, then either write metadata back to your source systems or ship the dataset downstream to RAG pipelines and retrieval systems. Datasets are set to auto-refresh so new files landing in a source are enriched and the slices stay current. Deasy also centralizes metadata governance — taxonomy, tag definitions, owners, accuracy — so every AI project in the organization starts from the same foundation instead of each team rebuilding its own enrichment pipeline. It is aimed at enterprise AI and data engineering teams with large unstructured repositories, plus business analysts who need to curate datasets through a UI rather than code, with APIs and a Python SDK for engineers. It deploys in your own cloud environment and connects to your existing models and LLM endpoints.
Behind the Verdict
The pitch here is refreshingly narrow: Deasy does not try to be your chatbot, your vector store, or your agent framework. It sits upstream of all three. The five-step flow on the site — connect to SharePoint and S3 with OCR, parsing and chunking; tag thousands of files per minute while designing a taxonomy and scoring relevance; slice by relevance, topic, time, quality or sensitivity; deliver downstream or write metadata back to source; then continuously monitor and refresh — is a coherent lifecycle rather than a feature list, and the auto-refresh step is the part most teams hand-roll badly. Strengths worth naming. Sensitive data detection is described at petabyte scale with every file screened, which matters if regulated content lives in the same repositories you're feeding to a model. Metadata write-back to the source system is a genuine differentiator: enrichment becomes a property of the data rather than a one-off export, so the next team or agent that touches those files inherits the work. Centralized taxonomy, tag definitions, owners, and accuracy addresses the common enterprise failure where five teams build five incompatible enrichment pipelines. And the split interface — a UI simple enough for business teams plus APIs and a Python SDK for engineers — means curation isn't bottlenecked on one data engineer. Weaknesses and honest friction. This is infrastructure for teams that already have a retrieval pipeline; if you haven't built one, Deasy has nothing to feed. It requires your sources (SharePoint, S3) and your LLM endpoints to be connected, so there is integration work and some data engineering literacy involved, and full workflow design may need technical expertise. The named connectors in the material we have are SharePoint, Amazon S3, Google Cloud Vertex AI, Gemini, LlamaIndex, and Qdrant — enough to slot into a Google-cloud retrieval stack, but check your own stack against what the vendor confirms. Customer proof skews enterprise: Octopus Legacy on knowledge management, a healthcare AI engineer on extracting metadata from medical texts for RAG pipelines in clinical settings. That's the right shape of evidence for this category, though the sample is small. Where it fits. Organizations with large unstructured silos — decades of email, a million PDFs, sprawling SharePoint — that have started an AI program and hit the wall of data relevance and safety. Where it doesn't: teams wanting an end-user AI application, shops that are already fully structured, and anyone who wants to hand-label rather than automate enrichment. The TechCrunch quote the site leads with frames the category well — matching each generative AI use case with the best possible set of data — and that is precisely the job Deasy has taken on.
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Real-world workflow fit
Concrete scenarios for the personas Deasy Labs actually fits — and what changes day-one when you adopt it.
Connect the company SharePoint tenant and an S3 bucket, run OCR, parsing, and chunking across the corpus, then apply tags, sensitive data screening, and quality/relevance scoring against the support-assistant use case before shipping slices into the existing RAG pipeline.
Outcome: A purpose-built dataset delivered downstream, with metadata also written back to SharePoint so other teams inherit the enrichment.
Use the UI to review the taxonomy Deasy learned from the document set, accept or author tags for product documentation, and slice by topic and time for an internal AI search tool.
Outcome: Curated, metadata-enriched content ready for AI search without writing code.
Stand up one metadata standard — taxonomy, tag definitions, owners, accuracy — and turn on auto-refresh so newly landing files are screened for sensitive data and enriched automatically.
Outcome: Every AI project in the organization pulls from the same governed foundation, and answers stay current as content changes.
Use Cases
- Curate SharePoint repositories into a high-quality dataset for an enterprise RAG assistant
- Screen millions of PDFs for PII and regulated content before any of it reaches a model
- Slice decades of email archives by topic and time to power a support agent
- Enrich product documentation with domain metadata so an AI search tool returns precise answers
- Continuously refresh datasets as new files land in an S3 bucket
- Centralize one metadata taxonomy across departments so enrichment pipelines are reused instead of rebuilt
Models Under the Hood
as of 2026-09-24
Limitations
- Deasy is upstream infrastructure: it prepares datasets for a retrieval pipeline or agent framework you already have, and it does nothing until it is connected to your sources (SharePoint, S3) and your own models or LLM endpoints, so there is integration work before first value.
- Full workflow design can require data engineering know-how, even though the tagging UI is built for business users.
- The evidence base is enterprise-weighted — Octopus Legacy, a healthcare AI engineer — with a small public customer sample, so verify fit against your own stack during evaluation.
- Named connectors in the material available to us are SharePoint, Amazon S3, Google Cloud Vertex AI, Gemini, LlamaIndex, and Qdrant; confirm any others directly with the vendor.
as of 2026-10-02
Verification history
We have re-verified Deasy Labs 7 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-checked, vendor evidence unchanged
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- — 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
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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.
Where the pricing makes sense
The company stage and team size where Deasy Labs's pricing actually pencils out — and where peers do it cheaper.
Deasy Labs is priced for enterprise data programs, not individual seats: it's a fit for organizations with petabyte-scale SharePoint, email, or PDF estates where curation replaces months of manual data engineering. Smaller teams with a few thousand documents will find lighter labeling and chunking tools cheaper.
Setup time & first value
How long it actually takes to get something useful out of Deasy Labs — broken out by persona, not the marketing-page minute.
Enterprise data engineers: expect meaningful setup — source connections to SharePoint and S3 plus your LLM endpoints must be wired before the first curated dataset ships. Business analysts: once sources are connected, first value is quick, since Deasy learns the content and suggests tags you can accept or edit in the UI.
Switching to or from Deasy Labs
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual scripted preprocessing: point Deasy at the same SharePoint or S3 sources and let OCR, parsing, chunking, tagging, and enrichment run in one pass instead of bespoke scripts.
- →From ad-hoc per-team tagging spreadsheets: Deasy designs and builds a single taxonomy so enrichment work stops being duplicated department by department.
- ↗To hand-rolled curation scripts: datasets ship downstream to your RAG pipeline, and metadata written back to sources can be read without Deasy.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Deasy Labs”, and we withheld 6: 6 could not be judged, because “Deasy Labs” 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 Deasy Labs.
Official links
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Featured Head-to-Head Comparisons
Deasy Labs vs Spider Cloud
Choose Deasy Labs if your bottleneck is curating and governing messy internal files (SharePoint, S3) for RAG at scale. Choose Spider Cloud if you need fast, cost-effective web scraping and crawling to feed AI agents with real-time external data. They solve different data acquisition problems — pick based on whether your data lives inside your enterprise or across the web.
Deasy Labs vs Screenplayiq
If you need to automate curation of massive unstructured data for enterprise AI, choose Deasy Labs. If you are a screenwriter or producer wanting data-driven script analysis and box office prediction, choose ScreenplayIQ. They serve completely different markets and use cases; no direct competition.
Deasy Labs vs Temporal Ai
Deasy Labs and Temporal AI solve fundamentally different problems. Choose Deasy Labs if your bottleneck is preparing massive unstructured data (SharePoint, PDFs) for AI — it automates curation, tagging, and governance. Pick Temporal if you need a rock-solid orchestration platform for AI agents and workflows that must survive failures, with human oversight. They can complement each other: Deasy prepares data, Temporal orchestrates the pipelines that consume it.
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Sight Machine
Agentic manufacturing platform that turns plant data into agent-ready models and finds more output every run.
LlamaIndex
LlamaParse turns messy PDFs, tables, charts and handwriting into clean, LLM-ready structured data for RAG and extraction pipelines.
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