AI API

A focused set of LLM-backed primitives that the Orbit platform uses internally — message analysis, reply suggestion, intent classification, summarization, content generation, and a small knowledge-base utility. All LLM requests route through Orbit’s partner-vetted Anthropic gateway — you don’t manage model-provider keys directly. Base path: /api/v1/ai Authentication: API key (X-API-Key) or session JWT. Service-to-service callers (with X-Internal-Token) get the developer role to cap blast radius.

Analysis & insight

Method	Path	Purpose
`POST`	`/api/v1/ai/analyze`	General message / content analysis
`POST`	`/api/v1/ai/analyze-message/{id}`	Analyze a specific message by ID
`POST`	`/api/v1/ai/classify-intent`	Classify a message’s intent
`POST`	`/api/v1/ai/summarize-conversation`	Summarize a long conversation
`POST`	`/api/v1/ai/dashboard-insights`	Generate dashboard insight prose

Generation & rewriting

Method	Path	Purpose
`POST`	`/api/v1/ai/suggest-reply`	Draft a reply to an inbound message
`POST`	`/api/v1/ai/generate-content`	Generate marketing content from a brief
`POST`	`/api/v1/ai/improve-text`	Rewrite for tone / clarity / length
`POST`	`/api/v1/ai/translate`	Translate to a target language
`POST`	`/api/v1/ai/compose-campaign`	Compose a multi-step campaign from a goal
`POST`	`/api/v1/ai/optimize-channel`	Recommend best channel/time for a contact

Inference building blocks

Two general-purpose endpoints let you run embeddings and chat inference on the same managed fleet — and the same wallet — as the rest of your Orbit comms. Both are OpenAI-compatible, so existing SDKs drop in by pointing their base URL at /api/v1/ai.

Method	Path	Purpose
`POST`	`/api/v1/ai/embeddings`	Generate embedding vectors for one or more texts
`POST`	`/api/v1/ai/chat/completions`	Run a chat completion (general inference)

Both endpoints call a model provider on every request and are billed per token, metered to your workspace wallet. They share the same rate limit as the other AI routes.

Embeddings

POST /api/v1/ai/embeddings returns one vector per input. Send input as a single string, or as an array of up to 96 strings (each 1–10,000 characters) to embed a batch in one request.

curl -X POST https://api.orbit.devotel.io/api/v1/ai/embeddings \
  -H "X-API-Key: dv_live_sk_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "input": ["order status", "refund policy"]
  }'

Field	Type	Required	Notes
`input`	`string` or `string[]`	yes	One text, or up to 96 texts (each 1–10,000 chars).

The response carries the vectors and per-request token usage under data. A malformed body (missing input, an empty string, or more than 96 items) returns 400.

Chat completions

POST /api/v1/ai/chat/completions is OpenAI-compatible. Point any OpenAI SDK at your /api/v1/ai base URL — the SDK appends /chat/completions — and send a messages array. OpenAI model ids are remapped to the platform’s managed model, so you don’t manage provider keys.

curl -X POST https://api.orbit.devotel.io/api/v1/ai/chat/completions \
  -H "X-API-Key: dv_live_sk_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      { "role": "system", "content": "You are a concise support assistant." },
      { "role": "user", "content": "Summarize our refund policy in one line." }
    ]
  }'

Field	Type	Required	Notes
`messages`	`object[]`	yes	1–50 turns; each has `role` (`system`/`user`/`assistant`) and `content` (1–100,000 chars).
`model`	`string`	no	Optional model id; OpenAI ids are remapped to the platform’s fast tier.
`temperature`	`number`	no	Sampling temperature, `0`–`2`.
`max_tokens`	`integer`	no	Output token cap, `1`–`8192`.
`response_format`	`object`	no	Set `{ "type": "json_object" }` to force JSON output (default `text`).

The completion and token usage are returned under data. A body missing messages (or with an out-of-range field) returns 400.

Knowledge base

An embedded, org-wide knowledge base: ingest documents (upload, URL, markdown, or a bulk help-center import), keep them fresh with a staleness scorecard and per-document verification, and query them with raw semantic search or a grounded answer with citations. For a separately provisioned, per-knowledge-base store, use the Knowledge Bases API instead.

Method	Path	Purpose	Request
`GET`	`/api/v1/ai/kb/documents`	List embedded KB documents (keyset-paged)	Query: `cursor`, `limit` (1–100, default 100)
`POST`	`/api/v1/ai/kb/documents`	Upload or import a document	Multipart file, or JSON `{ "source_type": "url", "source_url", "name"?, "metadata"? }` / `{ "source_type": "markdown", "name", "content" }`
`POST`	`/api/v1/ai/kb/import/help-center`	Bulk-import a public Zendesk Guide help center — one document per published article	JSON `{ "subdomain", "provider"?, "locale"?, "name_prefix"?, "max_articles"? (1–1000, default 250) }`
`GET`	`/api/v1/ai/kb/documents/{id}`	Get a document	Path: `id` (UUID)
`DELETE`	`/api/v1/ai/kb/documents/{id}`	Delete a document and its vectors	Path: `id` (UUID)
`POST`	`/api/v1/ai/kb/documents/{id}/reindex`	Force reindex	Path: `id` (UUID)
`POST`	`/api/v1/ai/kb/documents/{id}/verify`	Stamp a document as reviewed and still accurate (`last_verified_at`)	Path: `id` (UUID)
`GET`	`/api/v1/ai/kb/staleness`	Org-wide freshness scorecard — stale-document counts by reason	Query: `limit` (1–1000, default 25)
`GET`	`/api/v1/ai/kb/connectors`	Browse the knowledge-connector catalog	Query: `category`?, `status`?, `search`?
`GET`	`/api/v1/ai/kb/connectors/{id}`	Get one connector plus its activation detail	Path: `id` (connector slug)
`POST`	`/api/v1/ai/kb/search`	Semantic search across documents (raw chunks + scores)	JSON `{ "query", "limit"? (1–20, default 5), "threshold"? }`
`POST`	`/api/v1/ai/kb/answers`	Grounded answer with citations and a confidence score	JSON `{ "question", "knowledge_base_ids"?, "limit"? (1–10, default 8), "threshold"?, "min_confidence"?, "language"? }`

Example — suggest a reply

curl -X POST https://api.orbit.devotel.io/api/v1/ai/suggest-reply \
  -H "X-API-Key: dv_live_sk_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "conversation_id": "conv_abc"
  }'

API Reference

All Endpoints

AI API

AI API

Analysis & insight

Generation & rewriting

Inference building blocks

Embeddings

Chat completions

Knowledge base

Example — suggest a reply

See also

​AI API

​Analysis & insight

​Generation & rewriting

​Inference building blocks

​Embeddings

​Chat completions

​Knowledge base

​Example — suggest a reply

​See also

AI API

Analysis & insight

Generation & rewriting

Inference building blocks

Embeddings

Chat completions

Knowledge base

Example — suggest a reply

See also