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Head-to-head · updated 13 September 2026

DataShield vs Ragie: who should run the RAG pipeline, them or you?

Ragie sells the RAG pipeline you did not want to build. Their own line: "The context engine for agents, assistants and apps." Push a PDF, a Slack export or a video at the API. It comes back as clean, cited context. The parsing is the good part. Their agentic OCR pulls tables, forms and charts out of messy scans. It uses bounding boxes to do it. Agentic Retrieval splits a hard question into parts. It checks its own answers, then cites them. They shipped an MCP server in March 2025. That was before most of this market could spell the acronym.

One thing first, since you will find it anyway. Today their homepage carries a banner. It reads "Ragie service will end on July 19." Their app and status subdomains no longer resolve. We found no post that explains it. Ask them before you plan anything. We take no pleasure in that, and the rest of this page is not about it. It is about a design choice. Every managed context engine makes it: your documents go to their index, and their control plane decides what comes back. Ours runs the other way. The corpus sits on your own hardware. Embeddings get computed locally. The record of what each agent read is a hash chain. You can check it without trusting us.

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DataShield vs Ragie at a glanceEight questions a regulated buyer asks a RAG vendor. Scored from each side's public material. DataShield vs Ragie at a glance Eight questions a regulated buyer asks a RAG vendor. Scored from each side's public material. DataShield Ragie Tamper-evident record of what an agent retrieved PII and PHI tokenized before the embedding model Embeddings computed locally, no text egress Runs on your own boxes, no vendor control plane Document parsing, OCR and multimodal ingest Managed connector catalogue SOC 2 Type II, HIPAA, GDPR Prices published before a sales call shipped partial / roadmap not offered Sources at the bottom of this page.

The short version

Pick DataShield when

  • The documents have patient names, account numbers or case files in them. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation.
  • Nothing may leave the building, including the text you send off to be turned into vectors. We embed locally with nomic-embed-text, so the chunks never cross your border. How the stack fits together.
  • Someone will one day ask you to prove the retrieval log was not edited. Our chain answers that with math. Run the verifier.
  • You want to pull an agent's authority mid-session and have the very next tool call fail closed. How Auth does it.

Pick Ragie when

  • Your inputs are messy scans, forms and tables. Their agentic OCR is better at that than ours, and it is not close.
  • You need audio and video in the same index as the text, through one pipeline, with no work from you.
  • You want SharePoint, Slack, Notion, Confluence, Drive, Zendesk and friends syncing by tomorrow, and you would rather pay $250 a month per connector than write them.
  • Your team is three people and the whole point is that nobody runs a vector database. Their free tier gets you a working app before lunch.

Bottom line: Ragie is better at turning documents into context. We are built for the case where the documents are sensitive, the buyer is regulated, and the agent has to be told no. If you are reading this because the banner on their homepage sent you looking, start at /demos and bring one of your worst PDFs.

Feature by feature: managed RAG-as-a-Service against a self-hosted corpus

Competitor cells describe what Ragie's public site, pricing page and launch posts say as of the date above. If we got something wrong, email support@myorg.ai. We will correct it and credit you.

What mattersDataShieldRagieEdge
Document parsing and multimodal ingestText extraction from PDF, DOCX and EML. It stores each file as a versioned derived file. You can ingest from inline uploads, a file repo, guarded URLs, or straight out of S3-compatible storage. No OCR, no audio, no video.Agentic OCR with tables, forms, charts and bounding boxes. It also does native audio and video retrieval through the same pipeline. This is their best work.◇
Managed connectorsNone of that. We read from object storage and from the sources you register as providers. Slack and Confluence are your problem to connect.Ragie Connect covers Drive, Notion, Confluence, Slack, SharePoint, Dropbox, Intercom, Zendesk and more. It has embeddable connectors, so your customers can link their own accounts.◇
Retrieval engineeringHybrid retrieval over pgvector: a keyword leg and a vector leg, 768 dimensions. It reindexes as you go. Solid, not a research project.Three indexes: vector, keyword, summary. It adds reranking, recency bias, and deep search. Agentic Retrieval breaks a query apart and grades its own evidence.◇
Tokenization and data handlingDatasets are tokenized at ingest. Agents query tokenized data over MCP. Detokenization is a privileged, audited step. Tokens are deterministic and join-preserving, so a join still works once the names are gone. Quasi-identifier generalization turns dates into year, decade or age band, and ZIPs into 3 or 4 digits. It also handles partial phones, SSNs and emails, with a measured cardinality-reduction score per column.Partition-level isolation by tenant, workspace or customer, plus AES-256 at rest. We found no field-level tokenization or masking before indexing in their public docs.◆
Embeddings and egressnomic-embed-text runs locally. Chunk text never leaves your network to get vectorized. Every dataset value bound for a prompt crosses a fail-closed gate too. A PII or PHI column with no configured treatment gets redacted. A PHI dataset refuses an AI endpoint that is not marked BAA or ZDR approved.Their managed pipeline does the parsing, chunking and embedding. That means the document text lands in their service first. That is the deal you sign.◆
Audit evidenceSHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification names the failure: tampering, insertion, deletion, or truncation. Try the verifier.Not described in their public material. Ask what record survives of which agent retrieved which chunk. Ask who can edit it.◆
Agent authorizationEvery governed tool call passes a scope ceiling, a consented-tool allowlist, an authority tier, and a revocation re-check before dispatch. It fails closed. The call is metered and attributed to the agent.Partition scoping decides which slice of the index a key can see. That is coarse access control, set once. We found no per-call authority check.◆
Break-glassScoped, time-boxed emergency access for agents. It auto-revokes and cannot be quietly deleted from the log.Not described in their public material.◆
GDPR erasureCrypto-shred of per-subject key material, plus ISO 27560 consent receipts. The audit chain still verifies after the subject is gone.You can delete documents. They state they do not train on your data. What happens to derived chunks, embeddings and summaries is not spelled out. Ask.◆
MCP and agentsMore than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse. Auth issues MCP tool tokens with scope ceilings. Delegation is RFC 8693 token exchange with an enforced ceiling.A hosted Streamable HTTP MCP server. Its tool descriptions are context-aware and change as the knowledge base changes, so an agent picks retrieval over web search. Genuinely thoughtful, and shipped early.◈
DeploymentSelf-hosted in your own cloud or data center, or a dedicated single-tenant server we operate. Docker images ship for Auth, Ontology, Corpus and Lighthouse. Guardian verifies a signed deploy manifest.Their site offers cloud, your own VPC, or on-prem. We could not test the last two. The developer path is clearly the hosted one.◈
CertificationsSOC 2 not yet certified, and we will not imply otherwise. Our standards work maps to HIPAA §164.312(b), §164.514, GDPR Art. 17, ISO 27560, and RFC 8785.SOC 2 Type II, GDPR, HIPAA and CCPA. They state this on the homepage and in their October 2025 launch post. They beat us here today.◇
PricingPublished model and a scoped instant quote, no sales wall.Published too: free developer tier, $100 a month Starter, $500 a month Pro, quote-only Enterprise. Per-page, storage and connector rates are all listed.◈

◆ DataShield leads◇ Ragie leads◈ comparable

Ragie claims come from ragie.ai, its pricing page, and its own launch posts. We last checked on 13 September 2026. We link them below rather than work from memory.

Three things you get here that you won't get from a managed RAG API

The model sees tokens, not names

Most RAG services index what you give them. Say the contract has a member ID in paragraph four. That ID ends up in the chunk, in the embedding, and one day in a prompt. We tokenize at ingest instead. Tokens stay join-preserving, so analysis still works. Detokenization is a privileged call, and it gets logged. What the catalog does first.

Proof an examiner can check

A retrieval log that can be edited proves nothing. Ours is a hash chain with signed checkpoints. The verifier says what broke: tampering, insertion, deletion, or truncation. That is the property EU AI Act Article 12 and HIPAA §164.312(b) reviewers keep asking about. Try it in your browser, no signup.

Authority that changes mid-flight

An analyst resigns at 4pm on a Friday. Their agent is 20 minutes into a 40-minute research job over your case files. With DataShield the next governed call gets re-checked against current authority, and it fails closed. An API key scoped to a partition just keeps working until someone rotates it. How Auth does it.

Where Ragie is genuinely stronger

We should say this plainly. Ragie is better at the thing most people mean by RAG. Their parser reads a scanned invoice and gives you the table, with coordinates. Audio and video go in the same door as the PDFs. Take a question like "how did gross margin move between 2021 and 2023, and how does that compare to rivals". Agentic Retrieval splits it apart. It gathers evidence, throws out the false positives, and cites the rest. They open-sourced a reference chatbot. They open-sourced their context-aware MCP tooling too. In April 2026 they shipped a Claude Code skill, so an agent could write Ragie code on its own. Developers liked them for good reasons. The testimonial wall reads like real people, not case-study prose. We do not match their parsing, and we are not pretending to.

Here is the push-back, and it has nothing to do with the banner. A managed context engine is a second copy of your documents, sitting somewhere else. Its access model gets set at key-issue time. Its log is one you cannot independently verify. That is a fine trade when the corpus is product docs. It is a different trade when the corpus is clinical notes or a litigation hold. Partition isolation answers "which tenant". It does not answer "was this agent allowed to read this chunk at 14:07 on 3 March". It does not answer "can you prove nobody edited the record since". Those are the questions that turn up eighteen months later. Usually in writing. Usually from someone with a deadline.

Questions worth asking both of us

These are the questions we would want answered if we were the ones buying. Ask them on every call, ours included.

Is Ragie actually shutting down?

We do not know, and we will not claim more than we can show. On 13 September 2026 their homepage carried a banner. It read "Ragie service will end on July 19." Their app and status subdomains did not resolve. Their docs host did not answer. The last changelog entry was 5 June 2026. Their last blog post was 14 April 2026. We could not find an announcement post. Email support@ragie.ai and get it in writing before you plan a migration or a renewal.

Can you cryptographically prove a retrieval log entry wasn't deleted?

DataShield: yes. Each record commits to the one before it. Checkpoints are signed and chained. Verification tells deletion apart from truncation and from tampering. Run it against a sample chain at /verify. Ragie: their public material describes encryption and partition isolation, not tamper evidence. Ask any RAG vendor to show you the mechanism, not the compliance badge.

What happens to a revoked agent mid-session?

DataShield re-checks authority on every governed tool call. Revocation lands on the next call, not the next token refresh. With a hosted RAG API the usual answer is an API key scoped to a partition. That key keeps working until a human rotates it. Ask how long a compromised key keeps reading your index.

How does GDPR erasure interact with the audit trail?

DataShield crypto-shreds per-subject key material and issues an ISO 27560 consent receipt. Actor identities in the chain are HMAC-committed, so the evidence still verifies once the subject is gone. Ragie lets you delete documents. It says it does not train on your data. What happens to chunks, summaries and embeddings derived from a deleted document is not described. That derived layer is where erasure requests usually go wrong.

We're on Ragie now. What does moving to DataShield actually involve?

Your files, not their index. You re-ingest from wherever the documents really live: object storage, a URL list, or direct upload. Ontology classifies the fields against 129 PII and PHI classes. You decide what gets tokenized or generalized. Then the corpus gets built with local embeddings and hybrid retrieval. Agents reach it over MCP under Auth tool tokens. Expect to lose OCR quality on scanned documents. Expect to gain a corpus that runs on your hardware. Bring your three worst files to the Demo Center, and we will tell you honestly whether the parsing holds up.

Does DataShield have SOC 2?

Not yet, and we say so on every page. Ragie has SOC 2 Type II, and we do not. What we have instead: a live production system (Guardian and Lighthouse since April 2026), a public security page with a threat model, a verifier anyone can run, and design-partner terms that include source escrow, so a small vendor is not a single point of failure. Weigh that how you like. But weigh the badge question the right way round. A badge says a process was audited. It does not say your agent was told no.

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