Retrieval-augmented knowledge engines.
We design and build production-ready Retrieval-Augmented Generation (RAG) systems that integrate vector search, semantic caching, document parsing, and metadata filtering to connect LLMs to your private data safely and accurately.
import { Redis } from "@upstash/redis";
import { Index } from "@upstash/vector";
const redis = new Redis({ url: "REDIS_URL", token: "TOKEN" });
const index = new Index({ url: "VECTOR_URL", token: "TOKEN" });
const queryVector = await getEmbeddings(userQuery);
const cacheHit = await redis.get(userQuery);
if (cacheHit) return JSON.parse(cacheHit);
const vectorResults = await index.query({ vector: queryVector, topK: 3 });
// Return formatted sources...Semantic Cache Gateway
We deploy low-latency semantic caching on prompt embeddings to return instant hits for redundant queries, minimizing LLM token costs and network overhead.
Tools & Frameworks We Engineer With
Vector Pipeline
- text-embedding-3: OpenAI high-speed embed model
- Cohere v3: Multilingual and semantic embeddings
- BGE-Large: Top-tier open source embeddings
- PyTorch: Custom embedding orchestration
Vector Storage
- Pinecone: Managed cloud vector database
- pgvector: PostgreSQL semantic index extension
- Qdrant: High-performance vector engine
- Milvus: Distributed enterprise vector database
Retrieval Flows
- LlamaIndex: Data framework for LLM apps
- LangChain: Chain construction and prompt routing
- LangGraph: Stateful cyclic agent graphs
- Unstructured: Complex document extraction API
QA & Monitoring
- Ragas: Framework for RAG evaluation
- TruLens: Retrieval quality and cost telemetry
- LangSmith: Debugging and prompt tracing
- Arize Phoenix: RAG observability and evaluations
Who We Work With
Enterprise Knowledge Base
Querying thousands of internal PDF manuals, legal briefs, and financial sheets instantly.
Healthcare Intakes
Grounding triage bots in medical directories and practitioner reference materials safely.
Financial Tech Audits
Querying ledger schemas, regulatory files, and compliance policies.
Our Delivery Lifecycle
We operate under a structured, predictable lifecycle. Click any step to inspect deliverables.
Data Audit & Pipeline Discovery
We analyze your document schemas, formats, and API structures to define retrieval indexing constraints.
{
"sourceDocs": "Confluence & PDF",
"embedding": "text-embedding-3-small",
"chunkSize": 512,
"vectorIndex": "pgvector"
}Frequently Asked
RAG is a technique that retrieves authoritative data from your private database and inputs it directly into the prompt context. This allows the LLM to write replies based strictly on your files rather than training data, preventing false outputs.
By caching prompt embeddings and checking for matches before invoking the LLM, we can reply directly with saved responses for similar queries. This deflects up to 60% of LLM queries, lowering monthly API token spend.
Ground your AI in private knowledge.
Talk to our RAG architects to scope custom data parsing pipelines, vector indexing architectures, and semantic query routing.