Retrieval Is Where Most AI Products Break
Most AI accuracy problems aren't model problems. They're retrieval problems. We build the RAG pipelines, vector stores, and knowledge architectures that give your AI the right context, every time.
Senior engineers who've built production RAG systems across healthcare, finance, and enterprise SaaS, embedded with your team or leading the build.
Retrieval & Knowledge System Services
RAG Pipeline Architecture
Ingestion, chunking strategy, embedding selection, retrieval evaluation. We design pipelines that actually return the right context, not just similar context.
Vector Store Implementation
Pinecone, Weaviate, pgvector. We pick the right store for your latency and scale requirements and build the indexing pipeline around it.
Document Intelligence
Unstructured PDFs, contracts, reports, and databases turned into queryable knowledge. We handle extraction, normalization, and metadata enrichment.
Retrieval Evaluation & Improvement
We build eval harnesses that measure retrieval precision, recall, and answer relevance. Then we fix the gaps.
Knowledge Base Design
Structured and unstructured sources unified into a coherent retrieval layer your agents and LLMs can actually use.
Hybrid Search
Combining semantic and keyword retrieval for the cases where pure vector search falls short. Re-ranking, context compression, query expansion.
How We Build Retrieval Systems
Audit Your Current Retrieval
We benchmark what you have. Most teams discover their retrieval quality is the binding constraint, not their model.
Design the Ingestion Pipeline
Chunking strategy, metadata schema, embedding model selection. The decisions you make here determine everything downstream.
Build and Evaluate
We ship the pipeline and immediately instrument it. Precision, recall, latency. We don't call it done until the numbers prove it works.
Optimize and Monitor
Production retrieval drifts as your data changes. We set up the monitoring to catch it before your users do.
Built on the Retrieval Stack
Vector Stores
Pinecone 
Weaviate 
PostgreSQL (pgvector) 
Elasticsearch Embedding & Retrieval
OpenAI Embeddings 
HuggingFace 
LangChain 
Python Data
PostgreSQL Elasticsearch 
Apache Airflow Retrieval Systems: Real Use Cases
Enterprise Search
Replace the broken internal search your employees gave up on. One query surface across documentation, wikis, databases, and past communications.
Customer Support AI
Agents that pull from your actual product documentation, not their training data. Answers that are accurate and cite the source.
Legal & Contract Intelligence
Search and extract across thousands of contracts, clauses, and regulatory documents. Flag deviations. Surface precedents.
Financial Document Processing
Earnings calls, SEC filings, research reports. Extract structured data, track changes across time, answer questions across a corpus.
Product Knowledge Bases
Customer-facing AI that actually knows your product. Accurate answers, source citations, automatic updates when documentation changes.
Why Companies Choose Us for Retrieval
Retrieval is the hardest part of RAG. We treat it that way.
Most teams spend 80% of their time on the LLM and 20% on retrieval. Then wonder why their answers are wrong. We invert that ratio.
We eval before we ship.
Every pipeline we build ships with an evaluation harness. You get a number that tells you how well retrieval is working, not a demo that looks good on curated examples.
We know when vector search isn't enough.
Pure semantic retrieval fails on exact lookups, recent data, and structured queries. We build hybrid systems that handle the full range of real queries.
We've seen every ingestion problem.
PDFs with bad formatting, databases with inconsistent schemas, documents in multiple languages. We've handled them all and built the tooling to handle them at scale.
Build Your Retrieval Pipeline
Tell us what your AI needs to know. We'll build a retrieval pipeline that's accurate, measurable, and production-ready. Not a demo that looks good on hand-picked examples.
Retrieval & Knowledge Systems: FAQ
What's the difference between RAG and fine-tuning?
RAG retrieves external knowledge at inference time. Fine-tuning bakes knowledge into the model weights. RAG is better when your knowledge changes frequently or when accuracy and source citation matter. Fine-tuning is better when you need consistent behavior or specific output formatting.
How do I know if my retrieval is good enough?
You measure it. Retrieval quality metrics tell you exactly where the gaps are: hit rate, MRR, NDCG, answer relevance. We build eval harnesses as part of every engagement.
What chunk size should we use?
It depends on your documents and query patterns. There's no universal answer. We benchmark multiple chunking strategies against your actual queries and pick the one that performs best.
Can you work with our existing data infrastructure?
Yes. We build retrieval layers on top of what you have: existing databases, document stores, data warehouses. We add the vector layer without replacing your current infrastructure.
How long does it take to build a RAG pipeline?
It depends on your data sources and complexity. Simple single-source pipelines are faster to deliver than multi-source systems requiring custom chunking strategies and evaluation harnesses. We scope before we start, so you know what you're getting before we build it.