Integration · Operations and Data

Let Agentforce search the way your business already searches

Elasticsearch is the search and analytics engine behind many product catalogs, help centres, and log pipelines: documents organized into indexes, analyzers that understand language, and relevance ranking tuned against years of real queries. Emerge Digital connects that layer to Salesforce Agentforce so an agent runs the same searches your site and teams do — full-text with filters, ranked by the relevance work you have already done — rather than standing up a second retrieval stack beside it.

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What this unlocks

Agents issue full-text queries against your existing indexes, so the synonyms, analyzers, and ranking rules you tuned keep doing their job inside a conversation.
Exact-term search holds up where wording matters — SKUs, part numbers, error codes, policy clause references — the cases where lexical matching beats similarity-by-meaning.
Filters and aggregations narrow results before they reach the agent, so a search can be scoped to a product line, a date range, or a document type up front.
Multilingual analysis, including Elasticsearch's built-in Arabic analyzer, lets an agent search the bilingual content estates Gulf organizations actually publish.

In the customer journey

Catalog search inside the conversation

A shopper describes what they need and the agent queries the same product index the storefront uses — same synonyms, same ranking — so what it suggests matches what the site itself would surface.

Has anyone seen this error before?

A support agent takes an error code from a customer, searches the log and incident indexes for it, and can say whether the issue is already known — turning a lookup your engineers run manually into part of the reply.

Elasticsearch strengthens Discover and Engage, where finding the right product, article, or record decides whether a conversation goes anywhere. Because the same engine often indexes logs and operational events, it also serves support moments deep in the relationship — an agent that can search what happened answers differently from one that cannot.

How Emerge integrates Elasticsearch

Emerge Digital wires Elasticsearch into Agentforce as a scoped consulting engagement. We identify which indexes agents should be allowed to query, design the query templates — full-text, filtered, aggregated — that turn a conversational question into a well-formed search, and use Elasticsearch's own security model, down to index-level and field-level access, to bound what each agent can retrieve. None of this arrives as a self-install add-on: the value lies in shaping queries around your mappings and relevance tuning, which are different in every cluster.

How we structure an engagement

Related integrations

FAQ

How is this different from adding a vector database?

A vector database finds content similar in meaning to a query; Elasticsearch ranks documents by the lexical relevance you have tuned — and wins outright when the question contains an exact term like a SKU or an error code. Emerge treats the two as complementary, and often the right first step is the search engine you already run.

Do we need to re-index our data for agents to use it?

Usually not. Agents query the indexes and mappings you already maintain. If agent retrieval genuinely needs fields your current mappings lack, Emerge surfaces that during the engagement rather than rebuilding anything by default.

Can the agent see everything in the cluster?

No. Emerge configures access through Elasticsearch's security features, restricting each agent to named indexes — and, where needed, specific fields — so what it can search is decided by configuration, not by how a question is phrased.

Ground your agents in Elasticsearch.

Tell us what your agents need to read and write in Elasticsearch, and we'll design the integration and the governance around it.

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