Context-aware retrieval for AI Agents

Stop stuffing context windows. Power your agents with a stateful relevance engine that retrieves chunks based on user history and intent, not just semantic similarity.

Faster retrieval, smarter context

Build in-house

Retrieval logic

  • Similarity Only
  • Relevance (Similarity + User State)
Feature Current Approach With Shaped
Context window Stuffed with random chunks Optimized with high-precision chunks
User memory Manual lookups in Redis First-class input in the query
Latency High (Network hops) <50ms (Vertically integrated)

Give your agents memory this week

Day 1

  1. Connect Data
    Ingest Knowledge Base & User Events streams.

Days 2-6

  1. Configure Logic
    Define ranking signals in declarative YAML.

Day 7

  1. Deploy agent
    Push to production with <50ms latency.

Reduce token costs

Stop wasting tokens on irrelevant chunks. Filter retrieval by user intent before hitting the LLM context window.

Long-term memory

Standard vector DBs are amnesiac. Shaped natively stores user interactions, giving your agent instant access to past behavior.

Ship stateful agents in one sprint

Step 1: Connect your data

Ingest your documents, vector embeddings, and—crucially—user interaction streams into a single unified schema. No complex ETL required.

Step 2: Configure retrieval logic

Stop writing Python glue code to filter chunks. Define complex retrieval strategies—combining vector similarity, keyword matching, and user history—in a single declarative query.

Step 3: Deploy automatically

Shaped handles the infrastructure—training, scheduling, and auto-scaling your retrieval endpoint. Ship production-ready agent memory without managing vector indices or inference servers.

One context engine, every agent

Whether you are building a customer support bot, a shopping assistant, or an internal research tool, they all share the same unified understanding of the user.

Solutions for every platform

Semantic search
Retrieve knowledge based on meaning using modern embedding models.

User history
Personalize responses based on past clicks, views, and chat sessions.

Hybrid retrieval
Combine keyword matching (BM25) with vector search for precise recall.

Metadata filtering
Apply hard constraints (availability, permissions) within the retrieval query.

Ready to fix your RAG pipeline?

Join engineering teams using Shaped to drive engagement and revenue.

100M+

Vectors indexed

<50ms

p99 latency

1B+

Predictions