Vector database & embeddings
We design and build vector search and embedding pipelines for US companies: the retrieval layer underneath RAG and semantic search, tuned so it actually surfaces the right results. Most RAG problems are retrieval problems, and this is the part that fixes them.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds across US time zones, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Retrieval that works
Chunking, hybrid search, and reranking tuned to your data and measured for recall.
The right store
Pinecone, Weaviate, pgvector, Qdrant, chosen for your scale and budget.
Embedding pipelines
Ingestion that keeps your index fresh as content changes.
Evaluated, not guessed
We measure retrieval quality directly so you know it's finding the right chunks.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
Which vector database should we use?
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It depends on scale, latency, and whether you want managed or self-hosted. pgvector is often plenty; Pinecone, Weaviate, and Qdrant each fit different needs. We'll recommend based on your situation, not a partnership.
Our RAG retrieves the wrong chunks. Can you fix it?
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Usually, yes. Bad retrieval is almost always chunking, embedding choice, or lack of reranking. We measure recall directly and fix the retrieval layer before touching prompts.
Do you handle keeping the index up to date?
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Yes. We build ingestion pipelines that re-embed and update the index as your content changes, so search doesn't go stale.
How do you measure if retrieval is good?
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We build an evaluation set of questions with known answers and measure whether the right chunk lands in the top results. Retrieval quality becomes a number you can track.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.
Start the conversation