Ellipse Gradient for Header

Cohere Integration Guide

Integrate Cohere’s REST-based language, embedding, reranking, classification, and tokenization APIs into enterprise workflows and applications.

Cohere integration options at a glance

Cohere’s primary integration surface is a REST API covering Chat, Embed, Rerank, Classify, Tokenize, and model operations. Selected operations also support asynchronous or batch-style processing, which can be coordinated through job submission and scheduled polling. Applicable Chat requests can include document-like content, although Cohere does not provide a general-purpose file storage API. Authentication generally uses an API key in a Bearer authorization header, while private or cloud-specific deployments may use different arrangements. Martini can securely consume these APIs, transform enterprise payloads, orchestrate multi-step workflows, expose controlled APIs, and write results to business applications, databases, or vector-search platforms.

Integration pointSupported by Cohere?Common use casesHow Martini supports it
REST APIsYesCohere’s primary integration surface supports Chat, Embed, Rerank, Classify, Tokenize, model listing, and related operations.Martini can consume Cohere REST endpoints, configure headers and payloads, map responses, and orchestrate calls with surrounding enterprise systems.
Bulk / async / batch APIsLimitedSelected operations support asynchronous or batch-style processing for larger workloads. Coverage is not universal across every Cohere endpoint.Martini can submit jobs, persist returned identifiers, schedule status polling, retrieve results, and reconcile partial failures.
File / attachment APIsLimitedApplicable Chat requests can include document-like or other supported input content, but a general-purpose Cohere file storage API was not confirmed.Martini can retrieve content from a repository, normalize or extract text, and map supported content into Cohere requests.
SDKsYesCohere provides client libraries for languages including Python, TypeScript/JavaScript, Java, and Go.Martini can use the underlying REST APIs directly; custom JVM-compatible logic can be added when SDK-specific behavior is required.
OpenAI-compatible APILimitedCohere documents compatibility patterns for selected APIs and use cases, subject to validation for the model, endpoint, and deployment.Martini can implement the confirmed endpoint contract, while keeping model and deployment configuration externalized and testable.
AuthenticationYesStandard public API requests use an API key in the HTTP Authorization header as a Bearer token. Deployment-specific authentication may differ.Martini stores keys in secrets or protected environment configuration and applies them to outbound API requests without embedding them in workflows.
Webhooks / outbound callbacksNot confirmedA general-purpose Cohere webhook or outbound event framework was not confirmed. Long-running work should generally use job polling.Martini can expose APIs for consumers and schedule polling workflows, but should not assume Cohere will send callbacks.
Database / analytics accessNot confirmedCohere does not expose direct SQL or general database access as part of its standard public API.Martini can invoke Cohere and separately connect to supported databases or vector platforms through their own APIs or database interfaces.

How Cohere exposes data and business events

Cohere REST APIs

REST is Cohere’s primary integration mechanism. The API includes Chat, Embed, Rerank, Classify, Tokenize, model operations, and related request/response endpoints. Cohere integrations are generally synchronous API exchanges, with request limits and model-specific constraints.

Martini implementation pattern

Martini implementation pattern: expose or receive an enterprise API request, build the Cohere payload, add the secured Bearer API key, invoke the required REST endpoint, validate the response, and transform the result for the consuming application or downstream workflow.

Implementation sequence

Receive the enterprise request or retrieve source content
Load the model and deployment configuration
Map and validate the Cohere request payload
Invoke the Cohere REST endpoint with secured authentication
Validate generated, ranked, classified, or embedded output
Transform and return or persist the result

Cohere Batch API

Cohere supports asynchronous or batch-style processing for selected operations. These jobs generally require the caller to submit work, retain a job identifier, poll status, and retrieve results when processing completes.

Martini implementation pattern

Martini implementation pattern: use one workflow to submit deterministic batches and persist the returned job identifier, then use a scheduled workflow to poll status, retrieve completed results, reconcile each source item, and handle failed or incomplete work separately.

Implementation sequence

Partition source content into deterministic batches
Submit the supported asynchronous operation
Persist the Cohere job identifier and source correlation data
Poll job status on a controlled schedule
Retrieve completed results
Reconcile successful and failed source items

Cohere Chat document inputs

Applicable Chat requests can include document-like or other supported content. This is an input capability for selected requests, not a confirmed general-purpose file storage and management service.

Martini implementation pattern

Martini implementation pattern: retrieve documents from an enterprise repository, extract or normalize supported content, enforce size and sensitive-data rules, invoke Chat, and route the validated response to the business process.

Implementation sequence

Retrieve the source document or text content
Extract and normalize supported input content
Apply redaction and size validation rules
Build the Cohere Chat request
Validate the response against business requirements
Store or return the approved result

Common Cohere integration patterns

Pattern 1: Generate support responses

When to use this pattern

Use this pattern when a support or service application needs summaries, suggested replies, or resolution drafts while retaining business validation and human approval controls.

Integration direction
Salesforce
Martini
Cohere
Salesforce
Example Mapping
Cohere FieldCanonical FieldTarget Field
case.subjectrequest.subjectchat.messages[0].content
case.descriptionrequest.contextchat.messages[1].content
generated_textdraft.responsecase.draft_reply
response_metadataprocessing.auditcase.integration_metadata
Martini implementation pattern

Martini receives the case, retrieves permitted history or knowledge content, redacts sensitive fields, and builds a versioned Cohere Chat request. The workflow validates length, structure, and policy conditions before writing a draft back to Salesforce. Timeouts and transient provider errors are retried with bounds, while unsuitable content is routed for review rather than automatically published.

Martini capabilities used
  • API exposure
  • API consumption
  • workflows
  • data mapping
  • business rules
  • validation
  • error handling

Pattern 2: Build semantic retrieval

When to use this pattern

Use this pattern when enterprise content must be indexed for semantic search and retrieved passages need relevance ordering before generation or presentation.

Integration direction
Content repository
Martini
Cohere
Elasticsearch
Example Mapping
Cohere FieldCanonical FieldTarget Field
document.textcontent.chunkembed.texts
embed.response.embeddingscontent.vectorelasticsearch.vector
rerank.results.indexcandidate.ranksearch.result.rank
rerank.results.relevance_scorecandidate.relevancesearch.result.score
Martini implementation pattern

Martini retrieves versioned content, performs deterministic chunking, invokes Cohere Embed, and writes vectors with source and model metadata. At query time, it sends candidate passages to Rerank, applies tenant and relevance rules, and can pass approved context to Chat. Re-indexing is controlled by content and model version so changes remain traceable.

Martini capabilities used
  • workflows
  • API consumption
  • data mapping
  • transformation
  • business rules
  • scheduling
  • error handling

Pattern 3: Classify and route documents

When to use this pattern

Use this pattern when incoming emails, cases, or documents must be assigned to queues or workflows using labels and confidence thresholds.

Integration direction
Zendesk
Martini
Cohere
ServiceNow
Example Mapping
Cohere FieldCanonical FieldTarget Field
ticket.descriptionclassification.inputclassify.inputs
classify.predictions.labelrouting.categoryassignment_group
classify.predictions.confidencerouting.confidencerouting_metadata.confidence
ticket.idsource.correlation_idintegration_reference
Martini implementation pattern

A Martini trigger retrieves the source item and submits normalized text to Cohere Classify or a suitable Chat request. The workflow accepts only known labels above a configured confidence threshold, routes uncertain items to a fallback queue, and uses the source identifier to prevent duplicate updates. Technical failures are retried separately from low-confidence business outcomes.

Martini capabilities used
  • workflow triggers
  • API consumption
  • mapping
  • validation
  • conditional routing
  • business rules
  • duplicate handling

Pattern 4: Process batch document enrichment

When to use this pattern

Use this pattern for large document sets requiring summaries, classifications, embeddings, or extracted fields without sending the entire collection in one request.

Integration direction
Content repository
Martini
Cohere Batch API
Data platform
Example Mapping
Cohere FieldCanonical FieldTarget Field
document.idsource.correlation_idbatch.item.reference
document.contentbatch.inputbatch.request.payload
job.idprocessing.job_idjob_tracking.cohere_id
result.outputenrichment.valuedata_platform.enrichment
Martini implementation pattern

Martini partitions content into deterministic batches, submits supported Cohere batch work, stores job and source identifiers, and uses a scheduler to poll status. Completed results are validated and reconciled individually; partial failures remain retryable without resubmitting successful items. Rate limits, model limits, and resumability are handled as workflow state rather than hidden script behavior.

Martini capabilities used
  • workflows
  • scheduling
  • API consumption
  • batch orchestration
  • data mapping
  • correlation tracking
  • retry handling

Applications commonly integrated with Cohere

Cohere can be placed behind Martini workflows that connect enterprise applications to language, embedding, reranking, classification, and batch-processing capabilities. The following are typical architecture patterns rather than claims of certified direct integrations; each application connection depends on its own APIs, permissions, and deployment requirements.

Application Scenario Direction Martini Pattern
Salesforce Generate case summaries, classify leads, draft service responses, or enrich CRM text fields with controlled model output. Salesforce → Martini → Cohere → Salesforce Martini receives or retrieves Salesforce data, redacts sensitive fields, maps the case or lead context into a Cohere Chat or Classify request, validates the result, and writes approved output back to Salesforce with correlation and audit details.
ServiceNow Summarize incidents, classify requests, recommend relevant knowledge content, and assist service-agent workflows. ServiceNow → Martini → Cohere → ServiceNow A Martini workflow consumes ServiceNow data, applies prompt and classification rules, invokes Cohere, evaluates confidence or response structure, and updates ServiceNow or routes uncertain results for human review.
Zendesk Create ticket summaries, classify support requests, suggest replies, and identify escalation categories. Zendesk → Martini → Cohere → Zendesk Martini retrieves ticket history and selected knowledge content, maps it to Cohere Chat or Classify, applies redaction and confidence thresholds, and writes a draft or routing decision back to Zendesk.
Jira Summarize issues, classify defects, generate descriptions, and identify potential duplicate tickets. Jira → Martini → Cohere → Jira Martini receives Jira issue data, normalizes descriptions and comments, invokes Cohere for classification or summarization, validates the response, and updates Jira only when business rules permit.
Slack Provide controlled internal question-answering, thread summarization, and message enrichment workflows. Slack → Martini → Cohere → Slack Martini receives permitted Slack content through the relevant Slack API, removes disallowed data, sends selected context to Cohere Chat, and posts a bounded response or summary back to the approved channel.
Microsoft Teams Generate meeting or conversation summaries and post controlled responses to channels. Microsoft Teams → Martini → Cohere → Microsoft Teams A Martini API or workflow receives Teams content through Microsoft Graph, applies content and access rules, invokes Cohere, validates the generated response, and posts the approved result to Teams.
Snowflake Classify warehouse text, enrich data, or generate embeddings for semantic retrieval and analysis workflows. Snowflake → Martini → Cohere → Snowflake Martini reads eligible text through Snowflake access, batches and token-checks the inputs, calls Cohere Embed or Classify, and writes vectors or classifications back through the appropriate Snowflake interface or downstream vector platform.
Elasticsearch Generate embeddings for indexed content and rerank search results before returning them to applications. Elasticsearch → Martini → Cohere → Elasticsearch Martini retrieves source content or candidate search results from Elasticsearch, calls Cohere Embed or Rerank, applies relevance and tenant rules, and returns or indexes the transformed results.

How to build a Cohere integration in Martini

Objective

Establish the Cohere API or deployment configuration without exposing credentials in workflow payloads or source-controlled mappings.

Instructions in Martini

  • Configure the Cohere base URL, model, timeout, and deployment parameters by environment.
  • Store the API key in Martini secrets or protected environment configuration.
  • Apply the Bearer token in the outbound Authorization header.
  • Confirm the selected deployment’s authentication and networking requirements.

Objective

Select an API, event from the source application, or schedule that matches the required processing pattern.

Instructions in Martini

  • Use an exposed Martini API for synchronous application requests.
  • Use a source-system trigger where the source application provides one.
  • Use a scheduler for batch submission and asynchronous job polling.
  • Do not design around Cohere webhooks because a general outbound webhook mechanism was not confirmed.

Objective

Collect the source text, documents, candidate search results, or application context needed for the Cohere operation.

Instructions in Martini

  • Retrieve only permitted source fields and related context.
  • Extract or normalize supported document content before Chat requests.
  • Chunk large collections deterministically.
  • Preserve source identifiers and a business correlation ID.

Objective

Coordinate Cohere calls with validation, enrichment, persistence, and downstream application actions.

Instructions in Martini

  • Select Chat, Embed, Rerank, Classify, Tokenize, or a supported batch operation.
  • Persist job identifiers when processing is asynchronous.
  • Use conditional branches for confidence thresholds and fallback routes.
  • Keep model names and prompt or mapping versions configurable.

Objective

Convert enterprise payloads into Cohere request structures and transform responses into stable application-specific formats.

Instructions in Martini

  • Map messages, documents, texts, model parameters, and candidate passages explicitly.
  • Use token counts and request-size rules before sending large inputs.
  • Validate structured responses, labels, scores, citations, and required fields.
  • Record model, prompt, source, and processing metadata where appropriate.

Objective

Prevent technically successful but unsuitable model output from being written directly to business systems.

Instructions in Martini

  • Apply redaction and data-governance rules before the Cohere call.
  • Use confidence thresholds and known-label rules for classification.
  • Require human approval where generated content should not be published automatically.
  • Route invalid or incomplete responses to an exception or review path.

Common Cohere data objects used in integrations

ObjectTypical UseCommon target systemsMartini handling
Chat request and responseSubmit messages, conversation history, system instructions, tools, documents, and generation parameters; receive generated text, citations, tool calls, usage, and metadata.Salesforce, ServiceNow, Zendesk, Slack, Microsoft Teams, internal APIsMartini maps source context into the request, applies redaction and prompt rules, validates the response, and routes approved output to target systems.
Embed responseRepresent text or other supported inputs as vectors for semantic search, retrieval, deduplication, or classification workflows.Elasticsearch, Snowflake, vector-search platforms, content repositoriesMartini chunks and versions source content, invokes Embed, tracks model and source metadata, and writes vectors through the target platform interface.
Rerank resultScore and reorder candidate documents or passages returned by a separate retrieval system.Elasticsearch, search applications, knowledge platforms, retrieval servicesMartini passes candidate results to Rerank, applies relevance thresholds and business filters, and returns or persists the ordered results.
Classify resultReturn predicted classifications and confidence information for supplied text or examples.ServiceNow, Salesforce, Zendesk, Jira, workflow queuesMartini validates labels and confidence thresholds, applies fallback routing for uncertain results, and records the source correlation ID.
Tokenization resultProvide token and token-count information for prompt validation, cost estimation, and model-limit management.Prompt orchestration workflows, monitoring stores, data platformsMartini uses token counts before generation or batching, applies truncation or summarization rules, and records relevant processing metadata.
ModelRepresent an available Cohere model and its capabilities, such as chat, embedding, reranking, or classification support.Configuration stores, model-selection services, application APIsMartini can retrieve or configure model information, keep model names externalized by environment, and validate capability compatibility before invocation.

Authentication and security considerations

API-key authentication

Standard Cohere API requests use an API key in the HTTP Authorization header as a Bearer token. Private or cloud-specific deployments may use different authentication and networking arrangements.

Secret handling

Store Cohere keys, base URLs, model names, and deployment parameters in Martini secrets or protected environment configuration. Do not place credentials in workflow payloads, mappings, source control, or routine logs.

Data governance

  • Review prompts and source documents for personal, confidential, regulated, or proprietary information.
  • Redact or tokenize sensitive fields before sending content to Cohere when required by policy.
  • Restrict access to generated responses because they may reproduce sensitive source content.
  • Avoid logging full prompts and completions unless logging is explicitly required and protected.

Operational considerations for Cohere integrations

Limits and throughput

Cohere limits vary by account, plan, model, endpoint, and deployment. Use bounded exponential backoff with jitter for transient rate-limit responses, and control concurrency for large document workloads.

Request sizing

Chat, Embed, and Rerank requests have token, document-count, input-size, or model-specific limits. Estimate or validate token counts, chunk content deterministically, and apply truncation or summarization rules before invocation.

Asynchronous processing

For supported batch operations, persist the job identifier and source correlation data, poll on a schedule, retrieve completed results, and reconcile partial failures without resubmitting successful items.

Idempotency and validation

  • Persist source identifiers, model names, prompt or template versions, and processing status.
  • Do not blindly repeat non-deterministic generation after a timeout unless duplicate output is acceptable.
  • Validate response structure, labels, confidence, length, and business suitability before writing results.
  • Keep model availability and schema assumptions configurable and testable because capabilities and output behavior can change.

Why use Martini instead of scripts or point-to-point integrations?

Centralized orchestration

Martini coordinates source applications, Cohere APIs, repositories, databases, search platforms, and downstream business processes in maintainable workflows instead of scattering provider calls across individual scripts.

Reusable integration logic

Reusable workflows and APIs can standardize authentication, redaction, prompt construction, model configuration, validation, correlation, and error handling across multiple consumers.

Controlled enterprise APIs

Martini can expose a governed API façade so internal applications do not need direct access to Cohere credentials or provider-specific request structures.

Operational reliability

  • Apply consistent mapping, transformation, retries, scheduling, and exception routing.
  • Separate technical failures from low-confidence or unsuitable model output.
  • Track asynchronous jobs and support resumable batch processing.
  • Use environment-specific secrets and configuration without duplicating integration code.

Frequently asked questions

How can Cohere be integrated with enterprise systems?

Cohere is primarily integrated through REST APIs for Chat, Embed, Rerank, Classify, Tokenize, and model operations. Enterprise workflows can send controlled text or document-like inputs, receive generated or analytical responses, and store results in business applications, databases, search platforms, or vector systems. Selected asynchronous or batch operations can be coordinated through job submission and polling.

Can Martini integrate with Cohere?

Yes. Martini can consume Cohere’s REST APIs, securely apply API-key authentication, map enterprise data into Cohere request structures, validate responses, orchestrate batch polling, and expose controlled APIs for internal consumers. A dedicated native Martini Cohere connector is not documented in the supplied materials.

Do I need a connector to integrate Cohere with Martini?

No. A dedicated Cohere connector is not required. Martini can use Cohere’s confirmed REST APIs and authentication method, then provide the workflows, transformations, validation, scheduling, and downstream application integration around those API calls.

Is there any extra Lonti cost to integrate Cohere with Martini?

Lonti does not charge an additional per-connector or per-vendor fee to integrate Cohere. The integration is subject to the provisioned capacity of the Martini environment. Separate costs may apply from Cohere, cloud infrastructure, vector databases, or other third-party systems based on subscription, usage, and deployment model.

Which Cohere integration methods should an enterprise use?

REST APIs are the primary and recommended mechanism for new integrations, including Chat, Embed, Rerank, Classify, Tokenize, and model operations. Selected asynchronous or batch operations can be used for larger workloads. SDKs and OpenAI-compatible patterns may be relevant in specific implementations, but compatibility should be validated for the selected model, endpoint, and deployment.

Does Cohere provide webhooks, events, GraphQL, or SOAP APIs?

A general-purpose Cohere webhook or outbound callback framework was not confirmed, and no official GraphQL or SOAP API was confirmed. Cohere’s integration model is primarily request/response oriented. For asynchronous processing, Martini should generally submit jobs and poll status on a schedule unless the selected deployment documents another mechanism.

How should Cohere synchronization and batch processing work?

Use deterministic chunking and batching, preserve a stable source correlation ID, and record the Cohere job identifier for asynchronous work. A Martini scheduler can poll job status, retrieve completed results, reconcile each source item, and separately handle partial failures. Model, prompt, content, and mapping versions should be retained when output traceability matters.

How does Martini handle Cohere mapping, errors, retries, and generated output?

Martini maps source fields into Cohere requests and transforms responses into target-specific structures. Workflows can apply token, schema, label, confidence, and business validation before persistence. Transient rate-limit, timeout, and provider errors can be retried with bounded backoff, while authentication, validation, unsupported-model, and unsuitable-content failures should be routed for correction or review. Martini can also expose a controlled API façade around Cohere for internal applications.

Related Martini documentation

APIs

Workflows

Data

Security

Operations

Integrate Cohere with Martini

Use Martini to build secure, maintainable Cohere integrations that connect enterprise applications to language, embedding, reranking, classification, and batch-processing workflows.

EXPLORE MARTINI
BOOK A DEMO