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." The people behind it know the field. Douwe Kiela worked on RAG at Meta, before starting the company in 2023. They coined RAG 2.0 in March 2024. They shipped a Grounded Language Model in March 2025, and pitched it as the most grounded in the world. Agent Composer followed in January 2026. Now a rocket engineer can build an agent without writing code. Is your problem that the answer is wrong? They've spent three years and $100M on exactly that problem.
Our problem is a different one. We don't sell retrieval quality, and we publish no accuracy number either. 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. We turn datasets into tokens at intake. Agents query tokens over MCP after that. Turning a token back into real data is a guarded, checked step. We compute embeddings on your own machines. Nothing leaves for a third party to vectorize. Below is the honest split. It includes the rows they win.
The short version
Pick DataShield when
- The corpus holds PII or PHI. 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 gear. Their floor is a VPC. Ours is a box in your data centre. The embedding model runs there too.
- You need to pull an agent's rights mid-session. The very next checked tool call should fail. Not the next token refresh.
- Someone will ask you to prove what the agent read. Our chain answers with math, not a screenshot. Run the verifier.
Pick Contextual AI when
- Answer quality is the whole job. Their Grounded Language Model, reranker, and evaluation tools exist for one reason. 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. Qualcomm is on the logo wall, too.
- You want to buy pieces. Parse, Rerank, Generate, and LMUnit are sold and priced apart. That's rare, and genuinely useful.
- You need a SOC 2 Type II report in the vendor file today. They have one. We don't.
Bottom line: they make the answer right. We make the access provable, and the data safe to embed. Most regulated teams need both. The two stack cleanly. Govern the dataset here, then point their agents at what comes out.
Feature by feature
Competitor cells describe what Contextual AI's public site, docs, and blog say. We use the date above. If we got something wrong, email support@myorg.ai. We'll fix it and credit you.
| What matters | DataShield | Contextual AI | Edge |
|---|---|---|---|
| Retrieval and answer quality | Hybrid retrieval over pgvector. A keyword leg, and a vector leg. You pick the model, and it handles reranking. We publish no accuracy number. We're not going to start. | The core product. RAG 2.0 trains retriever and generator as one system. The Grounded Language Model is tuned for accuracy, with inline citations. They publish their own evaluation tools, too. | ◇ |
| Document handling | PDF, 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. $40 per 1,000 for multimodal. Built for diagrams, tables, and engineering drawings. | ◇ |
| Sensitive data before indexing | 129 field classes cover PII, PHI, money data, and secrets. Results repeat exactly, every time. We tokenize columns at intake. For column-masked datasets, chunk text comes from the masked view. We index free text in documents as written, apart from a standing SSN filter. Our docs say so. | We found no PII or PHI classification, masking, or tokenization step in their public docs. Whatever you hand the parser is what gets indexed. | ◆ |
| Embeddings | nomic-embed-text runs on your machines, 768 dimensions. Stored in your own Postgres. No text leaves your gear to become a vector. | Managed platform. Embedding happens in their cloud. Or your VPC, at the Enterprise tier. | ◆ |
| Tokenization | Tokens stay the same each time. They keep joins working. The vault can reverse them. We apply them at intake. Plus quasi-identifier generalization, as a rule. Dates become a year, decade, or age band. ZIPs become 3 or 4 digits. Phones, SSNs, and emails get partly masked too. Each column gets its own cut score. Turning a token back into real data needs admin tier. It's tenant-scoped, and refused if the audit write fails. | Not a word they use. Their answer to sensitive data is access control and tenancy. Not data minimising. | ◆ |
| Agent authorization | On Auth's MCP surface, every checked call passes a scope ceiling. It carries an authority tier too. We re-check it mid-session, before it goes out. Then we count it and tie it to the agent. Ontology's own MCP server logs in by API key instead, with per-tool tier gating. That's a weaker gate, worth knowing. | Role-based access control, and SSO, at the workspace level. We found no per-call check point for a retrieval request. | ◆ |
| Break-glass | Scoped, time-boxed emergency access for agents. Admin and IP-allowlist gated. Fully logged. It revokes itself. | Not described in their public docs. | ◆ |
| Audit evidence | A SHA-256 hash chain, with Ed25519-signed checkpoints. Those checkpoints chain too. The check returns clean, or names the damage. Tampered, or truncated. It names the break. See how. | Standard platform logging. We found no public tamper-evidence method for retrieval calls. Ask them: what would a regulator actually get? | ◆ |
| GDPR erasure | Crypto-shred: we destroy the subject's key material. Every ciphertext for that subject goes dark at once. The audit chain still checks out afterwards. ISO 27560 consent receipts sit alongside it. | Custom data retention policies, at the Enterprise tier. How a single subject gets removed from an index isn't described. | ◆ |
| MCP and agents | More than 200 MCP tools, across Ontology, Auth, Corpus, and Lighthouse. That includes 12 RAG commands. Agents query the checked corpus directly, not through a wrapper. | Agent Composer builds multi-step agents, with a drag-and-drop GUI. It links to Chroma, Elastic, Snowflake, Weaviate, CrewAI, and n8n, too. No MCP server appears in their docs integration list. | ◈ |
| Deployment | Self-hosted, in your own cloud or data centre. Or a dedicated single-tenant box we run for you. Ed25519 audit-signing keys stay on your instance. They sit in a key folder you control, with keyed rotation. An unknown key fails closed. KMS or HSM custody is a built seam in Auth. It's not a shipped tie-in yet. HMAC tokenization keys sit in your environment today, or come from your machine key. Never a KMS. | Multi-tenant SaaS, single-tenant SaaS, or customer VPC, at the Enterprise tier. No on-site option is described. | ◆ |
| Maturity and assurance | Live and in use. Guardian and Lighthouse have run since April 2026. SOC 2 isn't certified yet. We say so on every page. | Founded in 2023. $100M raised. Backers include Nvidia, Snowflake Ventures, and HSBC Ventures. SOC 2 Type II, done. Pen testing by Doyensec. Named customers include Qualcomm. | ◇ |
| Pricing | Published model and a scoped instant quote, no sales call first. | Part APIs are publicly priced. That's more honesty than most of this market offers. The platform Enterprise tier is quote only. The free tier caps you at one workspace and one data link. | ◈ |
◆ DataShield leads◇ Contextual AI leads◈ comparable
Contextual AI claims come from contextual.ai, its blog, pricing, security, and docs pages. Last checked 13 September 2026. We link them below, not just 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's not the same as safe. We classify the fields first. We tokenize the sensitive ones. Then we index. Tokens stay the same each time, so joins still work. Reversing one is a guarded call, and it 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 job, over the client corpus. It doesn't know yet. On Auth's MCP surface, we re-check the next call against current rights. It 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's an export from a log store. One an admin could have edited. Ours is a hash chain, with signed checkpoints. 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've spent three years on one question. Why does the model say things that aren't in the documents? RAG 2.0 was a real argument. It said bolting a frozen retriever onto a frozen model leaves quality on the table. The Grounded Language Model followed. It came with inline citations. It claimed top results on the FACTS benchmark. Then AgentLens for evaluation. A reranker. A parser, priced per page, for multimodal work. Agent Composer, for people who'd rather drag boxes than write orchestration code. Their security page lists a done SOC 2 Type II, outside pen testing, and a bug bounty. Ours doesn't. We have none of those yet. If a buyer needs that report this quarter, we lose. And we should.
Here is the push-back. It's narrow on purpose. Everything above improves the answer. None of it changes what sits in the index. Their public docs describe no classification of PII or PHI before parsing. No tokenization. No per-call authorization on a retrieval. No tamper-evident record of which chunks fed which output. For a bank or a hospital, that's the part the regulator asks about. It doesn't get better as the answers get more accurate. A very good answer, built from data the agent shouldn't have read, is still a finding. So run their agents, if their agents are better. Just make sure the corpus underneath was checked 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, it can. Each record commits to the one right before it. We sign and chain every checkpoint with Ed25519. The check returns clean, or names the damage. Tampered, or truncated. 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 method for the retrieval log. Ask them to show one.
What happens to a revoked agent mid-session?
DataShield checks rights again on each checked tool call, on Auth's MCP surface. Revocation lands on the next call, not the next hour. Contextual AI has role-based access control and SSO. We couldn't 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. Every ciphertext for that subject goes unreadable, at once. Then it issues an ISO 27560 consent receipt. Actor names in the chain get HMAC commitments, not real names. The evidence still checks out. 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're not competing on grounding, reranking, or parser quality. We'd rather say that than lose a deal slowly. Govern the dataset here. Classify it. Tokenize it. Embed it on your own machines. Authorize the calls. Keep the chain. Then let their agents reason over what comes out. There's one case where we do compete. The buyer who can't 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 don't have a benchmark document. A number without a method file is just marketing. Our RAG is real, and live in production. Pgvector. Local nomic-embed-text embeddings. Hybrid keyword and vector retrieval. Logged promotion of a corpus, from personal to org-wide. If accuracy is your deciding factor, run both against your own documents. That's the only test that counts anyway.
Does DataShield have SOC 2?
Not yet. We won't hint otherwise, ever. Contextual AI does, and that's a real gap. What we offer instead: a public threat model. A verifier you can run without an account. Design-partner terms that include source escrow, so a small vendor isn't a single point of failure. Details on the security page.
- Contextual AI's current positioning: "The context layer for expert AI." That's the tagline, word for word. - contextual.ai, 13 Sep 2026
- RAG 2.0: "end-to-end optimizes the language model and retriever as a single system," rather than bolting together frozen off-the-shelf parts. - Contextual AI blog, 19 Mar 2024
- The Grounded Language Model is pitched as "the most grounded language model in the world," with inline attributions and state-of-the-art results on the public FACTS set. - Contextual AI blog, 4 Mar 2025
- Agent Composer offers "multi-tool coordination, multi-step reasoning, and complex workflows across enterprise data," built through a drag-and-drop GUI. - Contextual AI blog, 26 Jan 2026
- Part APIs are publicly priced. Parse: $3 per 1,000 text pages, $40 per 1,000 multimodal pages. Rerank: $0.05 per million tokens. Generate: $3 in and $15 out per million tokens. VPC deployment and SAML/SSO sit behind the quote-only Enterprise tier. - contextual.ai/pricing, 13 Sep 2026
- Security posture: done SOC 2 Type II, pen testing by Doyensec, multi-tenant isolation, RBAC and SSO. Deployment runs from multi-tenant SaaS to customer VPC. No on-premises option is described. - contextual.ai/security, 13 Sep 2026
- Founded in 2023 by Douwe Kiela and Amanpreet Singh. A $20M seed in June 2023. An $80M Series A in August 2024. Backers include Nvidia's NVentures, HSBC Ventures, and Snowflake Ventures. - Wikipedia, 13 Sep 2026
- Their documented integrations: Chroma, Elastic, Snowflake, Weaviate, CrewAI, Ragas, Google Model Garden, and n8n. No MCP server is listed. - docs.contextual.ai, 13 Sep 2026
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