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DataGrout

DataGrout is an enterprise operating layer for AI agents. It unifies scattered APIs and MCP servers behind one governed endpoint, adds persistent Prolog-backed memory, verifies every workflow cryptographically, and cuts token spend through neuro-symbolic execution.

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Overview

What is DataGrout?

DataGrout positions itself as an operating layer between AI agents and the systems they need to touch. Its argument is that what breaks in production is infrastructure, not models: an MCP server can expose more than a thousand tools when a language model copes with about fifty, multi-step pipelines fail silently on type mismatches between systems, agents forget everything between sessions, and continuously polling agents burn budget on conditions that never changed. The site offers a pointed example, an agent asking whether it is morning yet every thirty minutes and spending 47 USD overnight.

The answer is four composable pillars. Foundry turns a plain-English goal into a reusable, schema-aware query tool: Toolsmith reads the live integration schema, validates the generated query against EBNF grammars for SOQL, QBOQL, REST or OData, and saves it as a versioned, licensable skill any agent can call. Hub handles connectivity, aggregating many MCP servers into one endpoint, resolving duplicate tools by capability ranking, vaulting credentials, and reaching on-premise systems through outbound-only Private Connectors. Intelligence plans and executes: Discovery searches the tool mesh semantically, the Gateway builds type-safe plans by A* search over SemIO types, Prism reshapes data from a natural-language description using cached self-healing code, and Flow validates plans before anything fires. Memory gives agents a persistent Prolog fact space through Logic, with constraints that enforce themselves and namespaces isolated per project or customer.

Two ideas run through all of it. The runtime is neuro-symbolic: Prolog dispatch resolves routing, policy checks and sequencing at no token cost, and the model is invoked only when symbolic reasoning meets real ambiguity. And every workflow yields a Cognitive Trust Certificate, an Ed25519-signed proof that the plan was acyclic, type-safe and within budget, followed by an execution receipt.

Around the pillars sit more than twenty MCP-callable tools, from zero-credit deterministic data operations to Warden's three-tier prompt-injection detection, plus a production Conduit SDK in Python, TypeScript, Rust, Elixir and Ruby. The publisher is DataGrout AI LLC of Austin, Texas, a subsidiary of Abuyo, Inc. dba SelectHub.

What it does

  • Expose every API and MCP server behind one governed endpoint, with a single auth model and a single policy stack
  • Find the right tool by semantic search instead of dumping a catalogue of a thousand tools into the context window
  • Give agents persistent, Prolog-backed memory that survives sessions and answers in under a millisecond
  • Plan type-safe multi-step workflows by A* search, validate them before execution and prove them afterwards
  • Cut token spend by resolving routine conditions symbolically, firing the model only on genuine ambiguity
  • Block prompt injection, destructive operations and PII leakage before a call reaches an upstream system
  • Reach on-premise and air-gapped systems through an outbound-only tunnel, without opening a firewall port
Audience

When to use DataGrout / When not to

A quick filter to help you decide if DataGrout is the right fit.

When to use DataGrout

  • Engineering teams putting AI agents into production rather than demos, who need connectivity, memory and governance in a single layer
  • Developers and AI engineers hitting N x M context explosion, where an MCP server exposes over a thousand tools and the model copes with roughly fifty
  • Engineering managers tired of every squad rewriting OAuth wrappers, pagination handlers and tool definitions from scratch
  • CTOs and CIOs looking for a model-agnostic integration layer that keeps the organisation free of any single LLM vendor
  • CISOs who need prompt-injection defence, scoped credentials, PII redaction and cryptographic proof of what an agent actually did

When not to use DataGrout

  • Non-technical end users hoping for a ready-made assistant: onboarding runs through an MCP server URL or the Conduit SDK
  • Anyone wanting a mobile or browser experience, since there is no iOS, Android or extension build
  • Organisations needing a contractual guarantee that their data will never train a vendor model, which the EULA expressly permits with no documented opt-out
  • Buyers in heavily regulated environments who need SOC 2 or ISO 27001 today, as the vendor states it is still working toward formal certification
  • Teams that must document data residency or reach an EU Article 27 representative, neither of which is published
Get started

How to use DataGrout

A typical end-to-end flow, from setup to results.

  1. Create a free account on the DataGrout app: no credit card is required and the setup wizard opens straight away
  2. Choose your path, depending on whether you are connecting an existing agent, a no-code platform, or building with the SDK
  3. Copy the MCP server URL that DataGrout issues for your account
  4. Paste it into your agent's connector settings, for instance in Claude under Settings, then Connectors, then Add custom connector
  5. Let DataGrout confirm the connection automatically, then simply talk to your agent as before
  6. For a developer setup, install the Conduit SDK instead with pip, npm or gem
  7. Call bootstrap_identity() once: an ECDSA P-256 key pair is generated locally, the signed certificate lands in ~/.conduit/ and the private key never leaves your machine
  8. Add the integrations you need from the MCP registry, or point DataGrout at any MCP-compliant server by URL
  9. Discover tools by describing your goal in natural language rather than naming an API, then call them through namespaced wrappers such as client.prism or client.logic
  10. Read the credits, model and token counts returned with every response, and try MCP Inspector or the LLM Cost Calculator free without an account
Quick read

Pros & Cons

Pros

  • One governed endpoint replaces a sprawl of MCP servers, each with its own auth model and policy surface
  • Cost control is designed in, not bolted on: a capped estimate before every run, an itemised receipt after, and BYOK that removes the LLM markup entirely
  • Symbolic reflexes handle around 90% of routine conditions at near-zero token cost, which the publisher claims is ten to a hundred times cheaper than always-on inference
  • Genuine cross-session memory, queryable in under a millisecond with no model call involved
  • Tamper-evident cryptographic proof of what ran and under which policy, shareable with an auditor
  • A permanent free plan with 5,000 monthly credits and no credit card, plus several tools usable with no account at all
  • Unusual candour in the legal and security pages, including a named sub-processor list and an open admission that no formal certification is held

Cons

  • The EULA expressly allows DataGrout to use customer data and output data to train its own AI models, and no opt-out is documented
  • No formal certification: the security page states the company is still working toward SOC 2, ISO 27001 and equivalents
  • No hosting country or region is disclosed, only the providers, and cross-border transfers are announced without naming a transfer mechanism
  • No EU Article 27 representative, no named data protection officer and no explicit GDPR compliance claim
  • A very young operation with no About page, no team and no contact page: the domain dates from July 2025 and the first archive from April 2026
  • Credit-based billing whose dollar value shifts by tier makes comparison with per-call competitors awkward, and monthly plan credits expire at the end of the billing month
  • The public site is entirely client-rendered, and the vocabulary of MCP, mTLS, Prolog and SemIO assumes an engineering reader
Pricing

Pricing & Plans

A permanent free plan is available and requires no credit card, granting 5,000 credits per month with no option to top up. The lowest paid entry point is Almost Free at USD 19 per month, which raises the allowance to 10,000 monthly credits and permits recharges at USD 5 per 1,000 credits. Getting Real costs USD 249 per month for 125,000 credits, and an Enterprise tier is quoted on request. Every tier includes BYOK, under which model costs are paid directly to the LLM provider and DataGrout charges only for platform usage.

Free, USD 0 forever
  • 5
  • 000 credits per month with no recharge
  • 2 external MCP servers multiplexed
  • all DataGrout MCP servers
  • Intelligent Discovery
  • 7-day log retention
  • CTC execution receipts and chat support
Getting Real, USD 249 per month
  • 125
  • 000 credits per month
  • recharge at USD 2.50 per 1
  • 000 credits
  • ad hoc credits roll over
  • 10 external MCP servers multiplexed
  • 30-day log retention
  • chat and email support with an SLA
Enterprise, custom quote
  • bespoke credit volume
  • unlimited MCP server multiplexing
  • private cloud or on-premise deployment
  • SSO and SAML
  • audit logs
  • a dedicated account manager and SLA-backed support
Plan 5
  • BYOK is included on every tier
  • removing DataGrout's markup on LLM usage
Special offers — A permanent free plan with 5,000 credits per month and no credit card required, presented on the site as your first 5,000 credits being on the house · BYOK on every tier, with no markup on model usage and LLM costs paid directly to the provider · The deterministic tools Data, Frame and Math carry no credit cost at all · MCP Inspector, JSON-RPC Inspector, the LLM Cost Calculator and Lumen are usable free and without an account · Ad hoc credits bought on top of a plan never expire and roll over from month to month · No student, non-profit, annual-commitment or promotional discount is published
Prices and plans listed above may evolve. Always check the official pricing page before subscribing.
Trust & Privacy

Data, GDPR & hosting

A consolidated view of how DataGrout handles your data.

GDPR overview

Implementation is partial, and compliance is never claimed. The privacy policy, effective 27 January 2026, acknowledges that citizens of the European Union hold additional rights under the GDPR and points them to info@datagrout.ai, but nowhere does DataGrout state that it complies with the regulation. Access, correction and deletion are offered by email. No EU representative under Article 27 is designated and no data protection officer is named. Transfers to affiliates and providers in countries with weaker protection are announced without naming any mechanism such as standard contractual clauses. A data processing addendum sits in Exhibit A of the EULA for customers under a purchase order. Governing law is Texas, and US state regimes such as CCPA, CPRA, VCDPA, CPA and CTDPA are covered in noticeably greater detail than the GDPR.

Who owns the data?

Under the EULA the customer keeps all intellectual property rights, title and interest in the Customer Data it exposes to the platform, and equally in the Output Data derived from it. DataGrout takes only a non-exclusive, royalty-free licence, sub-licensable to its affiliates and vendors, to use that data to operate the platform and meet its legal obligations. The agreement states plainly that Customer Data serves that customer alone and is never processed for the benefit of another client. The platform itself is not sold and remains DataGrout's property, as does the anonymised Analytics Information the company derives from usage and of which it declares itself sole owner.

Reuse rights

Customers may reuse the platform's Output Data freely: the EULA leaves all rights, title and interest in it with them. Two limits apply. Output Data may not be used to build models competing with DataGrout or the platform, and the customer alone is responsible for judging its accuracy, the agreement warning against relying on it for decisions with legal or material consequences for an individual. In the other direction DataGrout grants itself far broader rights. Clause 2.5 permits the company to use Customer Data and Output Data to maintain and improve the platform, including for machine learning purposes and to train the platform's AI models, and no opt-out is documented anywhere on the site. Clause 2.6 adds that all data is ultimately processed by a third-party LLM provider operating outside DataGrout's control, which may itself use it for training.

Data retention & training

Retention summary
No retention period is published for personal data. The privacy policy commits only to deleting personal information once it is no longer needed for the purpose it was collected for, unless a lawful justification to keep it applies, such as resolving a dispute or meeting a legal obligation. The only figures given are product-side: observability logs are kept for 7 days on the Free plan, 14 days on Almost Free and 30 days on Getting Real, while mTLS certificates are deliberately short-lived at 30 days. Users may request access, correction or deletion by email, and Californian residents may ask that retention or sharing stop. On termination the EULA requires the customer to delete copies of the documentation and provides for confidential information to be returned or destroyed, but sets no automatic deletion timetable for customer data.
Trains on customer data
Yes
Subprocessors disclosed
Yes
DPA available
Yes
GDPR contact

Hosting summary

The platform runs on AWS with automated releases and health monitoring, and the privacy policy also lists Salesforce and Heroku among hosting and platform providers. Beyond those provider names nothing is disclosed: no country, no region and no data residency option on the standard tiers. The marketing site itself resolves to a Render node in the United States, which should not be mistaken for where platform data lives. Protection is described in far more detail than location. Data is encrypted at rest with AES-256 and in transit with TLS 1.2 or above, integration credentials receive an additional application-level layer, runtime secrets sit in AWS Secrets Manager, and the certificate authority signing key is held in an AWS KMS hardware security module validated to FIPS 140-2 Level 2, so that private key material never leaves the hardware. The privacy policy announces cross-border transfers to affiliates and third-party providers, including to countries whose laws do not offer equivalent protection, without naming a transfer mechanism. Enterprise customers can negotiate private cloud or on-premise deployment, and the EULA also contemplates SaaS, vendor-hosted, managed-service and customer-hosted models on public, private or hybrid cloud.

Availability

Where DataGrout works

Country-level availability.

Not available in

🇨🇺 Cuba🇮🇷 Iran🇰🇵 North Korea🇸🇾 SyriaCrimea Region of Ukraine🇷🇺 Russia🇱🇧 LebanonAny other country or region that becomes subject to embargo under applicable export control and sanctions lawThe EULA also warns that access may be restricted in jurisdictions where the underlying third Party LLM services are unavailable, without naming those countries
Watch-outs

Things to keep in mind

Risks and trade-offs to weigh before adopting DataGrout.

  • Clause 2.5 of the EULA lets DataGrout train its own AI models on your customer data and output data, and the site documents no way to opt out
  • All data is ultimately handed to a third-party LLM provider operating outside DataGrout's control and possibly training on it, an exposure for which the publisher expressly disclaims liability
  • The company holds no formal security certification, discloses no hosting country and names no international transfer mechanism
  • An agent given a governed path to production systems is still an agent acting on your systems: approval gates, side-effect controls and IP allowlists exist, but they must be configured rather than assumed
  • Delegating routing, planning and memory to a symbolic layer makes an opaque failure harder to reason about, and the audit trail only helps a team that knows how to read it
  • Liability for use of the site is capped at whatever you paid over the previous six months, or USD 1, whichever is greater
  • Cost control is a feature and not a guarantee: credits still meter real spend, monthly plan credits expire, and a misconfigured continuous agent can burn through them quickly
Setup

Setup & Integrations

Technical difficulty

Low to start, demanding to master. The quickest route is advertised at two minutes and needs no code: create an account, copy your MCP server URL and paste it into your agent's connector settings, and no-code platforms follow the same path. Developers install the Conduit SDK and call bootstrap_identity() once, which provisions the mTLS identity with no manual PKI work. Beyond that first connection, the vocabulary of MCP, SemIO, Prolog and Cognitive Trust Certificates, together with the governance surface of policies, approval gates, PII redaction and IP allowlists, makes this an administration job in its own right.

Deployment

Web appAPIPlugin

Integrations

Salesforce Oracle Fusion QuickBooks SAP HubSpot ServiceNow Snowflake Gmail Google Calendar Google Drive Google Chat GitHub Asana Linear Atlassian Rovo Box Cloudflare Composio Context7 Postman Sentry Stripe Supabase Tavily Linkup Zapier N8n

Supported languages

English
Company

Behind DataGrout

Company name
DataGrout AI LLC
Founded
15/04/2026
Country of origin
🇺🇸 United States
Headquarters
611 S. Congress Ave., Suite 130, Austin, TX 78704, USA
UBO
Abuyo, Inc. dba SelectHub
UBO country
🇺🇸 United States
Domain registrar country
INFORMATION_NOT_FOUND
Legal contact

Fundraising

No fundraising has been announced for DataGrout AI LLC, either on the site or in any press release
The privacy policy and terms of use name Abuyo, Inc. dba SelectHub as the parent company, but publish no valuation, investor or funding round information
Official links

Resources

All the official URLs gathered for verification and reference.

Compare

Alternatives

Tools that compete with or complement DataGrout.

Z ZapierN n8n
FAQ

Frequently asked questions

What exactly is a DataGrout credit?
Credits are the platform's currency, consumed whenever an agent orchestrates or executes a task. Every call costs one base orchestration credit, plus credits for intelligence features such as discovery or skill minting and for compute and model resources, so consumption tracks the complexity of the work. On the paid tiers a credit works out at roughly USD 0.002.
Is there a free plan, and does it require a card?
Yes to the first, no to the second. The Free tier is permanent rather than a countdown, grants 5,000 credits a month and needs no credit card. It does not allow credit top-ups; the cheapest paid tier, Almost Free, costs USD 19 per month.
Do my credits expire?
Monthly plan credits expire at the end of your subscription month. Ad hoc credits you buy on top do not: they roll over indefinitely and can be banked for bursty workloads.
Can I bring my own LLM keys?
Yes. With BYOK or BYOL you pay your model provider directly and DataGrout charges only for platform usage, dropping its LLM markup to zero. The publisher's own example takes a nine-credit workflow down to about six.
Will my data be used to train AI models?
Yes. The EULA states that DataGrout may use customer data and output data to maintain and improve the platform, including for machine learning purposes and to train the platform's AI models. No opt-out is documented, and the agreement adds that the third-party LLM providers processing the data may train on it as well.
Is DataGrout SOC 2 or ISO 27001 certified?
No. The security page states that the company is actively working toward formal compliance certifications, and invites enterprise customers to request a security questionnaire and architecture overview instead.
Where is my data hosted?
The platform runs on AWS, with Salesforce and Heroku also listed among hosting providers. No country or region is published, and the privacy policy announces transfers to jurisdictions whose laws offer weaker protection.
Which systems can I connect?
The MCP registry names Salesforce, Oracle Fusion, QuickBooks, SAP, HubSpot, ServiceNow, Snowflake, the Google Workspace suite, GitHub, Zapier, n8n and others, and any MCP-compliant server can be added by URL.
How technical is the setup?
The simplest route takes about two minutes and no code at all: create an account, copy your MCP server URL and paste it into your agent's connector settings. Developers can instead install the Conduit SDK, available in Python, TypeScript, Rust, Elixir and Ruby.
Is there a mobile app?
No. DataGrout is server-side infrastructure delivered as a web app, an API and MCP connectors, with no iOS, Android or browser extension build.
Conclusion

Should you pick DataGrout?

DataGrout is a serious piece of infrastructure aimed at a real and well-identified problem: agents that work in a demo and fall apart in production. Its diagnosis, covering context explosion, silent workflow failure, amnesia between sessions and runaway token spend, will be familiar to anyone who has tried, and the four-pillar answer reads as a coherent architecture rather than a bundle of features. The neuro-symbolic runtime and the Cognitive Trust Certificates are genuinely distinctive, and they rest on published research rather than marketing copy.

Commercially the offer is unusually approachable for enterprise infrastructure. The free tier is permanent rather than a countdown, several tools work with no account at all, deterministic operations carry no credit cost, and cost governance is built into the product instead of sold as an add-on.

The reservations sit in the legal pages, and one of them is significant. The EULA expressly permits DataGrout to use customer data and output data to train its own models, with no documented way to opt out. Anyone handling regulated or sensitive material should read clause 2.5 before connecting a production system. Around it lie softer gaps: no formal certification yet, no disclosed hosting country, no EU Article 27 representative and no explicit GDPR compliance claim.

Context matters too. The domain was registered in July 2025 and the site first archived in April 2026, so this is a very young operation, though one backed by an established publisher in Abuyo, Inc. dba SelectHub. The audience is equally clear: engineering teams and technical leadership, not end users. For those teams DataGrout deserves a serious evaluation, with the training clause settled first.