Embedder — Vector Infrastructure for Intelligent Systems

Vector infrastructure · Est. 2026 · LATAM

Embedding infrastructure, refined.

Production-grade RAG pipelines and vector compute for teams building intelligent systems. Minimal surface area. Maximum retrieval precision.

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curl -X POST https://api.embedder.lat/v1/embed \
  -H "Authorization: Bearer $EMBEDDER_KEY" \
  -d '{"input": "semantic retrieval at scale"}'

semantic cluster

Platform

Infrastructure built for retrieval-first AI

Every component is designed for operators who care about precision, observability, and operational calm — not dashboard theater.

Vector Indexing

Sub-millisecond approximate nearest neighbor search across billion-scale corpora with tunable recall.

RAG Orchestration

Composable retrieval, reranking, and context assembly — without the pipeline sprawl.

text → [0.12, -0.04, …]

Multi-Modal Embeddings

Unified embedding space for text, documents, and structured data — one API surface.

São Paulo→Mexico City→Bogotá12ms p99

Edge-Ready Latency

Regional inference nodes across Latin America. Global routing with local compliance.

Architecture

A pipeline you can reason about

Ingest

Stream documents, transcripts, and structured records through normalized chunking pipelines with schema-aware metadata preservation.

Embed

Route payloads to task-specific embedding models. Automatic dimension normalization and batch optimization reduce cost without sacrificing quality.

Index

HNSW and disk-backed indices with configurable recall targets. Hot-warm tiering keeps frequently accessed vectors in memory.

Retrieve

Hybrid search combining dense vectors, sparse signals, and learned rerankers. Full trace export for every query path.

99.97%

Platform uptime SLA

<18ms

Median retrieval latency

40B+

Vectors indexed

LATAM edge regions

Developers

Three lines to production retrieval

SDKs for Python, TypeScript, and Go. OpenTelemetry-native tracing. Idempotent upserts and deterministic query replay for debugging. View full documentation →

REST & gRPC APIs
Terraform provider
SOC 2 Type II (in progress)

from embedder import Client

client = Client(api_key=os.environ["EMBEDDER_KEY"])
index = client.index("production-corpus")

results = index.query(
    vector=client.embed("quarterly revenue trends"),
    top_k=8,
    filter={"region": "latam"},
)

// TypeScript
const { matches } = await embedder.index("docs").query({
  vector: await embedder.embed(input),
  topK: 5,
  includeMetadata: true,
});

Early access · 2026

Build on vectors that scale with intent

We are onboarding a limited cohort of design partners across fintech, legal tech, and enterprise knowledge systems.

hello@embedder.lat