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Head-to-head · updated 13 September 2026

DataShield vs Contextual AI: grounded answers are one problem, provable access is another

Contextual AI calls itself "the context layer for expert AI," and the people behind it know the field. Douwe Kiela worked on RAG at Meta before founding the company in 2023. They coined RAG 2.0 in March 2024, shipped a Grounded Language Model in March 2025 that they pitch as the most grounded in the world, and added Agent Composer in January 2026 so a rocket engineer can build an agent without writing code. If your problem is that the answer is wrong, they have spent three years and $100M on exactly that problem.

Our problem is a different one. We do not sell retrieval quality and we publish no accuracy number, because we have no benchmark document to point at. What we sell is what happens to the data before it reaches a model, and what you can prove afterwards. Datasets are tokenized at ingest; agents query tokenized data over MCP; detokenization is a privileged, audited operation. Embeddings are computed locally, so nothing leaves for a third party to vectorise. Below is the honest split, including the rows they win.

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DataShield vs Contextual AI at a glanceEight questions regulated buyers ask us. Scored from each vendor's public material. DataShield vs Contextual AI at a glance Eight questions regulated buyers ask us. Scored from each vendor's public material. DataShield Contextual AI Grounded generation tuned against hallucination Document parsing for dense technical PDFs SOC 2 Type II and an external pen test PII and PHI classification before indexing Tokens in the corpus, not raw values Local embeddings, nothing sent out to vectorise Authority re-checked on every tool call Tamper-evident audit chain you can verify shipped partial / roadmap not offered Sources at the bottom of this page.

The short version

Pick DataShield when

  • The corpus holds PII or PHI and you want it classified and tokenized before anything gets embedded. Raw values in a vector index are hard to take back.
  • The whole thing has to run on your own hardware. Their floor is a VPC. Ours is a box in your data centre, and the embedding model runs there too.
  • You need to pull an agent's authority mid-session and have the very next governed tool call fail. Not the next token refresh.
  • Someone will ask you to prove what the agent read. Our chain answers with math instead of a screenshot. Run the verifier.

Pick Contextual AI when

  • Answer quality is the whole job. Their Grounded Language Model, reranker and evaluation tooling exist because they treat hallucination as an engineering problem, not a disclaimer.
  • Your source material is dense technical documentation: specs, filings, test reports. Their parser is priced per page for a reason, and Qualcomm is on the logo wall.
  • You want to buy pieces. Parse, Rerank, Generate and LMUnit are sold and priced separately, which is rare and genuinely useful.
  • You need a SOC 2 Type II report in the vendor file today. They have one. We do not.

Bottom line: they make the answer right. We make the access provable and the data safe to embed. Most regulated teams need both, and the two stack cleanly: govern the dataset here, point their agents at what comes out.

Feature by feature

Competitor cells describe what Contextual AI's public site, docs and blog say as of the date above. If we have mischaracterised something, email support@myorg.ai and we will correct it, credited.

What mattersDataShieldContextual AIEdge
Retrieval and answer qualityHybrid retrieval over pgvector: a keyword leg and a vector leg, with reranking left to the model you point at it. We publish no accuracy number and we are not going to start.The core product. RAG 2.0 trains retriever and generator as one system, the Grounded Language Model is tuned for factuality with inline citations, and they publish evaluation tooling of their own.◇
Document handlingPDF, DOCX and EML text extraction on ingest, from files, URLs or S3-compatible storage, stored as a versioned derived file. Good enough for most corpora. Not a research-grade parser.Parse is a product line, priced at $3 per 1,000 pages for text and $40 per 1,000 for multimodal. Built for diagrams, tables and engineering drawings.◇
Sensitive data before indexing129 field classes covering PII, PHI, financial data and secrets, with deterministic and reproducible verdicts. Columns get tokenized at ingest, and chunk text for column-masked datasets is built from the masked view. Free text in documents is indexed as written apart from a standing SSN filter, and we say so in the product docs.We found no PII or PHI classification, masking or tokenization step in their public material. What you hand the parser is what gets indexed.◆
Embeddingsnomic-embed-text running locally, 768 dimensions, stored in your own Postgres. No text leaves your infrastructure to be turned into a vector.Managed platform. Embedding happens in their cloud, or your VPC at the Enterprise tier.◆
TokenizationDeterministic, join-preserving, vault-reversible tokens applied at ingest, plus quasi-identifier generalization (dates to year, decade or age band; ZIPs to 3 or 4 digits; partial phones, SSNs and emails) with a measured cardinality-reduction score per column. Detokenization is admin-tier, tenant-scoped and refused if the audit write fails.Not vocabulary they use. Their answer to sensitive data is access control and tenancy, not data minimisation.◆
Agent authorizationOn Auth's MCP surface every governed call passes a scope ceiling, an authority tier and a mid-session revocation re-check before dispatch, then gets metered and attributed to the agent. Ontology's own MCP server authenticates by API key with per-tool tier gating, which is a weaker gate and worth knowing.Role-based access control and SSO at the workspace level. We found no per-call decision point for a retrieval request.◆
Break-glassScoped, time-boxed emergency access for agents, admin and IP-allowlist gated, fully audited, auto-revoking.Not described in their public material.◆
Audit evidenceSHA-256 hash chain with Ed25519-signed checkpoints that are themselves chained. Verification returns clean, attested damage or tampered, and names the break. See how.Standard platform logging. We found no published tamper-evidence mechanism for retrieval calls. Ask them what a regulator would get.◆
GDPR erasureCrypto-shred: destroy the subject's key material and every ciphertext for that subject goes dark at once. The audit chain still verifies afterwards. ISO 27560 consent receipts alongside it.Custom data retention policies at the Enterprise tier. How a single subject gets removed from an index is not described.◆
MCP and agentsMore than 200 MCP tools across Ontology, Auth, Corpus and Lighthouse, including 12 RAG commands. Agents query the governed corpus directly rather than through a wrapper.Agent Composer builds multi-step agents with a drag-and-drop GUI, and there are integrations with Chroma, Elastic, Snowflake, Weaviate, CrewAI and n8n. No MCP server appears in their docs integration list.◈
DeploymentSelf-hosted in your own cloud or data centre, or a dedicated single-tenant server we operate. Ed25519 audit-signing keys can live in your KMS or HSM. HMAC tokenization keys sit in your environment or derive from your machine key today, not in a KMS.Multi-tenant SaaS, single-tenant SaaS, or customer VPC at the Enterprise tier. No on-premises option is described.◆
Maturity and assuranceLive in production, Guardian and Lighthouse since April 2026. SOC 2 not yet certified, and we say so on every page.Founded 2023, $100M raised, backers include Nvidia, Snowflake Ventures and HSBC Ventures, SOC 2 Type II done, pen testing by Doyensec, named customers including Qualcomm.◇
PricingPublished model and a scoped instant quote, no sales call first.Component APIs are publicly priced, which is more honesty than most of this market offers. The platform Enterprise tier is quote only, and the free tier caps you at one workspace and one data integration.◈

◆ DataShield leads◇ Contextual AI leads◈ comparable

Contextual AI claims are drawn from contextual.ai, its blog, pricing, security and docs pages, last checked 13 September 2026. We link them below rather than work from memory.

Three things you get here that you won't get from a context layer

The corpus is already tokenized

A vector index is a copy of your data in a shape nobody can read by eye. That is not the same as safe. We classify the fields first, tokenize the sensitive ones, then index. The tokens are deterministic, so joins still work, and reversing one is a privileged call that gets logged. How the data plane works.

Authority that can change mid-flight

An analyst quits on a Friday. Their agent is 20 minutes into a 40-minute research job over the client corpus. On Auth's MCP surface the next governed call is re-checked against current authority and fails closed. A workspace role change catches up at the next login. How Auth does it.

Proof, not a screenshot

Ask any RAG vendor what an examiner gets when they want the March retrievals. Usually it is an export from a log store that an admin could have edited. Ours is a hash chain with signed checkpoints, and the verifier tells you what broke, not just that something did. Try it in your browser.

Where Contextual AI is genuinely stronger

Start with the obvious. They have spent three years on one question: why does the model say things that are not in the documents? RAG 2.0 was a real argument, that bolting a frozen retriever onto a frozen model leaves quality on the table. The Grounded Language Model followed, with inline citations and a claim of state-of-the-art results on the FACTS benchmark. Then AgentLens for evaluation, a reranker, a parser priced per page for multimodal work, and Agent Composer for people who would rather drag boxes than write orchestration code. Their security page lists a completed SOC 2 Type II, external penetration testing and a bug bounty. Ours does not, because we have none of those yet. If a buyer needs that report this quarter, we lose and we should.

Here is the push-back, and it is narrow on purpose. Everything above improves the answer. None of it changes what sits in the index. Their public material describes no classification of PII or PHI before parsing, no tokenization, no per-call authorization on a retrieval, and no tamper-evident record of which chunks fed which output. For a bank or a hospital that is the part the regulator asks about, and it does not get better as the answers get more accurate. A very good answer built from data the agent should not have read is still a finding. So run their agents if their agents are better. Just make sure the corpus underneath was governed before the embedding job started, and that you kept proof.

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.

Can you cryptographically prove an audit log entry wasn't deleted?

DataShield: yes. Each record commits to the one before it, checkpoints are Ed25519-signed and chained, and the verdict is clean, attested damage or tampered. Try it at /verify. Contextual AI: their security page covers encryption, SSO, RBAC, intrusion detection and a SOC 2 Type II. We found no tamper-evidence mechanism for the retrieval log. Ask them to show one.

What happens to a revoked agent mid-session?

DataShield re-checks authority on every governed tool call on Auth's MCP surface, so revocation lands on the next call, not the next hour. Contextual AI has role-based access control and SSO. We could not find a mid-session revocation path in their public docs. Ask how long a compromised agent keeps reading after you pull its access.

How does GDPR erasure interact with the audit trail?

DataShield destroys the subject's key material, which makes every ciphertext for that subject unreadable at once, and issues an ISO 27560 consent receipt. Actor identities in the chain are HMAC-committed, so the evidence still verifies. Contextual AI offers custom retention at the Enterprise tier. Ask what happens to a single person's data inside an existing index, and how long it takes.

Is DataShield a Contextual AI alternative, or do we run both?

Both, usually. We are not competing on grounding, reranking or parser quality, and we would rather say that than lose a deal slowly. Govern the dataset here: classify it, tokenize it, embed it locally, authorize the calls, keep the chain. Then let their agents reason over what comes out. The one case where we do compete is the buyer who cannot put the corpus in anyone else's cloud at all. A VPC is their floor. Self-hosted is our default.

Why won't you publish a RAG accuracy score?

Because we do not have a benchmark document, and a number without a method file is marketing. Our RAG is real and in production: pgvector, local nomic-embed-text embeddings, hybrid keyword and vector retrieval, audited promotion of a corpus from personal to org-wide. If accuracy is your deciding factor, run both against your own documents. That is the only test that counts anyway.

Does DataShield have SOC 2?

Not yet, and we will not imply otherwise. Contextual AI does, and that is a real gap. What we offer instead is a public threat model, a verifier you can run without an account, and design-partner terms that include source escrow so a small vendor is not a single point of failure. Details on the security page.

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See the parts a context layer leaves out: tokenize a dataset, revoke an agent mid-session, then break a live audit chain and watch the verifier name the damage. Demo Center access is free with a work email.

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