Agentic Index
Catalog vs Lemrock (2026)
Both vendors last verified
Both solve the same new problem, making a brand's catalog readable and buyable by AI assistants so shoppers asking ChatGPT or Perplexity can find and buy the product. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Catalog keeps a verified, continuously synced record of each product and serves it, with tools to check stock, quote shipping, build a cart and check out, from an AI storefront on the brand's own subdomain, distributed over ACP and UCP. Lemrock ingests a brand's catalog, structures it for retrieval by language models and builds the brand an agent storefront that serves discovery, recommendations and purchases inside ChatGPT, Perplexity, Mistral, Gemini and DeepSeek, with brand controls over tone and exposure. The missions are nearly identical, so the difference comes down to geography and to which assistants each one reaches.
On the Agentic Index agent infrastructure ranking, neither Catalog nor Lemrock clears the bar, which asks for all five production contract capabilities documented in full. Each documents none of the five in full. 33 of the 186 vendors in the lane clear it. See the agent infrastructure ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. Catalog and Lemrock are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Catalog if
- An agent storefront on your own subdomain, distributed over ACP and UCP, is the mechanism you want for reaching assistants.
- Documented agent protocols (ACP and UCP) and a published extraction API are what your team needs to see before committing.
- Verified, source backed product records that keep price and stock in sync are the core of what you need.
Choose Lemrock if
- Appearing inside thematic assistants, such as a beauty advisor or a travel planner, as well as the general ones is where you want to be.
- Insights on what people and agents ask for, search and buy are how you want to steer exposure.
- You want the vendor to build and run your agent storefront and conversational apps end to end.
| Feature | C Catalog |
L Lemrock |
|---|---|---|
| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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CatalogIntegrations & Tool Calling Agents take action in the merchant's store through Catalog. Its parallel agent storefront gives shopping agents tools to check availability, quote shipping, add to cart and start checkout. Catalog distributes over ACP, where agents discover products, build a cart and pay inside ChatGPT with the brand as merchant of record. It also distributes over UCP, with catalog, checkout, identity and loyalty capabilities. Catalog has also offered source connectors and Stripe checkout. SourceCatalog, getcatalog.ai home and its markdown twin (FAQ, agentic commerce)read 2026-09-25 |
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LemrockIntegrations & Tool Calling A brand gets an agent storefront that Lemrock builds to serve discovery, recommendations and transactions across AI environments. Demos show shoppers buying inside a chat. Lemrock also cites fast, secure e-commerce integrations. It names no protocol, connector list, tool definitions or checkout mechanism, and it does not say how an agent acts in the brand's store. SourceLemrock, lemrock.com (home, about)read 2026-09-25 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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CatalogWorkflow Orchestration Catalog extracts, normalizes, enriches and syncs product data and serves it with tools to outside agents. The shopping agent decides the steps. Catalog names no workflow model of its own. SourceCatalog, getcatalog.ai home and markdown twinread 2026-09-25 |
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LemrockWorkflow Orchestration An "LLM orchestration layer" appears among the technology Lemrock names, in a tagline. Lemrock describes no steps, branching, agents or execution model behind it. SourceLemrock, lemrock.com (home)read 2026-09-25 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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CatalogTriggers & Channel Coverage Price, stock and variant changes sync continuously, and enrichment keeps running in the background as the catalog grows and changes, with no one calling it. The example record shows stock polled minutes ago. The storefront itself answers when a shopping agent calls it. Catalog does not say whether an event, webhook or schedule drives the sync. SourceCatalog, getcatalog.ai home and markdown twin (FAQ)read 2026-09-25 |
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LemrockTriggers & Channel Coverage Shoppers reach the brand's storefront from ChatGPT, Perplexity, Mistral, Gemini and DeepSeek and from thematic assistants, and each assistant invokes it when a shopper asks. Lemrock describes no event, schedule or webhook that starts work. SourceLemrock, lemrock.com (home)read 2026-09-25 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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CatalogKnowledge Grounding & RAG Each product gets a record built from the brand's data, images, reviews, customer questions and approved sources. One example record carries revisions, 41 fields from 6 sources and verification times. Price, stock and variants stay in continuous sync, and Catalog serves the record to agents at the brand's AI storefront. Agents can query a persistent store of the brand's own catalog that stays current. SourceCatalog, getcatalog.ai home and markdown twin (How it works, FAQ)read 2026-09-25 |
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LemrockKnowledge Grounding & RAG Each brand's product catalog is ingested and structured for LLM interpretation and retrieval. Lemrock matches shoppers' own words to the right product by intent and reports more than ten million products live. It describes the structure itself only in marketing terms. SourceLemrock, lemrock.com (home)read 2026-09-25 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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CatalogMemory & State Persistence The product records Catalog keeps hold data about the brand's catalog. Catalog names no memory kept for the agents that call the storefront. SourceCatalog, getcatalog.ai home and markdown twinread 2026-09-25 |
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LemrockMemory & State Persistence Product matches are personalized by profile, context and history, moving beyond static filters. Lemrock uses that conversation and shopper context when it matches a product. It describes no store, scope or lifetime for that state. SourceLemrock, lemrock.com (home, Hyper-personalization)read 2026-09-25 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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CatalogHuman Oversight & Guardrails Merchants can review every enrichment before it goes live and can allowlist or blocklist any AI surface. Catalog names no sign off gate or approval step the customer configures. SourceCatalog, getcatalog.ai markdown twin (FAQ)read 2026-09-25 |
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LemrockHuman Oversight & Guardrails Brands control what the storefront presents. Lemrock offers full control over brand exposure, and the brand can control tone and storytelling and enforce consistency across AI touchpoints. These are constraints the customer sets on the storefront. Lemrock describes no review or approval step before an action. SourceLemrock, lemrock.com (about, home)read 2026-09-25 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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CatalogSecurity, Identity & Governance Extract API calls authenticate with an API key header. Catalog names no roles, SSO, permissions or attestation, and it publishes no trust or security page. SourceCatalog, getcatalog.ai (home, llms.txt), api.getcatalog.ai and subdomain probesread 2026-09-25 |
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LemrockSecurity, Identity & Governance Lemrock says it is GDPR compliant and that its integrations are secure. It names no access surface, such as SSO, roles or permissions, and publishes no attestation. There is no trust or security subdomain on lemrock.com. SourceLemrock, lemrock.com (about) and subdomain probesread 2026-09-25 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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CatalogObservability & Auditability The merchant dashboard at app.getcatalog.ai sits behind a sign in, and Catalog does not say what it reports. Catalog has offered analytics on sessions by channel and product, recommended versus skipped products, attribute coverage and a readiness score, but it now names no analytics or log surface. SourceCatalog, getcatalog.ai home and app.getcatalog.ai (sign in)read 2026-09-25 |
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LemrockObservability & Auditability AI insights show what people and agents are asking, searching and buying. They track intents and gaps and give real time signals from human queries and machine calls. The insights report on demand and traffic, and Lemrock describes no view of what an agent did in each session. SourceLemrock, lemrock.com (home, Get instant AI insights)read 2026-09-25 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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CatalogDeployment & Data Residency The brand's AI storefront runs on a subdomain of the brand's own domain, hosted by Catalog, and goes with the brand if it leaves. No hosting region is published, and Catalog names no option to run in the customer's environment. SourceCatalog, getcatalog.ai markdown twin (FAQ)read 2026-09-25 |
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LemrockDeployment & Data Residency No hosting region is published, and Lemrock names no customer environment or way to choose where the service runs. Its only related statement is GDPR compliance. SourceLemrock, lemrock.com (about, home)read 2026-09-25 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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CatalogPrebuilt Agents, Templates & Packs Product data and storefront tools go out to other companies' shopping agents, and Catalog names no prebuilt agents or templates. Catalog has offered category specific product schemas, which are data structures, but it no longer names them. SourceCatalog, getcatalog.ai home and llms.txtread 2026-09-25 |
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LemrockPrebuilt Agents, Templates & Packs There are no agents or templates for a customer to adopt. Lemrock powers product matching inside thematic assistants such as a beauty advisor, a travel planner and a health companion. It also says it builds conversational experiences and apps for brands end to end, as a service. Neither is a set of agents a customer selects and runs. SourceLemrock, lemrock.com (home, Precision LLM Interfaces, Mass-scale LLM Search)read 2026-09-25 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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CatalogModel Flexibility & Routing ChatGPT, Gemini, Claude, Perplexity, Amazon Rufus and Walmart Sparky read Catalog's data, so the brand is reachable from those assistants. Catalog names no models behind its own enrichment and no way for the customer to choose one. SourceCatalog, getcatalog.ai markdown twinread 2026-09-25 |
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LemrockModel Flexibility & Routing Proprietary models that Lemrock runs on its own data do the work, and Lemrock offers customers no model choice. The assistants that read the brand's storefront are channels where shoppers find it. SourceLemrock, lemrock.com (home, Built on science)read 2026-09-25 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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CatalogAPIs, SDKs & MCP Extensibility Catalog's own platform has the Extract API at api.getcatalog.ai, which takes product URLs through POST /v2/extract with an x-api-key header and returns structured product data. Options cover enrichment, reviews, image tags and country, and full API documentation is available. The storefront's agent tools over ACP and UCP are a separate surface for shopping agents. SourceCatalog, api.getcatalog.airead 2026-09-25 |
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LemrockAPIs, SDKs & MCP Extensibility The only way in is a demo request. Lemrock publishes no API, SDK, MCP server or developer documentation. The api.lemrock.com address returns a 404 at its root, and there is no docs or developers subdomain. SourceLemrock, lemrock.com and subdomain probesread 2026-09-25 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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CatalogTesting, Debugging & Optimization The free product data audit reports what AI systems get wrong about a brand's products. Catalog has also offered A/B testing of titles and enrichments and a readiness score, which measured how assistants surface the brand's listings. These look at the customer's product data and at outside assistants. Catalog names no way to test an agent of its own or of the customer's. SourceCatalog, getcatalog.ai (audit, llms.txt)read 2026-09-25 |
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LemrockTesting, Debugging & Optimization The insights report demand and exposure, and Lemrock describes no way to test or score the storefront's recommendations or conversations. The 10x ROAS figure is Lemrock's own performance claim. SourceLemrock, lemrock.com (home)read 2026-09-25 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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CatalogBrowser & Computer Use Given any product URL, the Extract API scrapes the live web page at run time, on request, and returns structured data. Its example uses Nike, Adidas and ASICS pages. Catalog names no agent clicking, navigating or filling in an interface. The CatalogBot crawler ingests pages to feed the brand records. SourceCatalog, api.getcatalog.ai and getcatalog.ai/botread 2026-09-25 |
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LemrockBrowser & Computer Use The storefront serves data and purchases to assistants, and Lemrock describes no agent that operates a browser or desktop. SourceLemrock, lemrock.com (home)read 2026-09-25 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | C Catalog |
L Lemrock |
|---|---|---|
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Entry price Lowest public entry point |
Not publicly listed; early access | No public pricing; contracts are quoted through sales |
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Pricing confidence How public the numbers are |
Contact only | Contact only |
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Billing Primary billing axis |
Undisclosed; likely managed subscription for brands and usage based API for builders. | not published; B2B contract likely scaled by catalog size, interaction volume, or transactions |
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Variable cost Workload / overage exposure |
Medium variable cost | Medium variable cost |
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Free tier / trial Try before you buy |
No free tier
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No free tier
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Buying motion Self-serve vs sales call |
Sales call | Sales call |
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Pricing verified When this pricing was last checked |
Both vendors are early, the category barely exists yet, and the assistant surfaces themselves change quickly, so the list of assistants each one reaches can shift from month to month.
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