GoKawiilA 4B open-source model post-trained with Castform retrieved search results as accurately as GPT-5.6 Sol, while costing 100x less
“Most teams' best training data is just sitting in their databases. The problem is that turning raw data into something usable is hard, and letting agents read, search, and mutate data cheaply at scale requires advanced infra. Pointing Castform at Neon skips both.” Ying Hang Seah, cofounder, Castform
A "good agent" needs to be strong in 2 areas:
Context : can we provide the tools to find the right data?
: can we provide the tools to find the right data? Model: can the model decide what to search for?
Neon (Lakebase Postgres) and their new Search extensions solve the first; Castform solves the second.
Evolution of agentic search
In ~2022, the industry was going all in on embedding search. Every database provider added one, and pgvector was Neon's most downloaded extension. To provide context to LLMs, engineers handcrafted RAG pipelines, which in essence, is some form of embedding similarity search.
In ~2025, agents started to gain more traction. Developers started creating multi-hop search workflows, decomposing big problems into smaller ones. Retrieval has shifted from the one-shot search systems to agentic retrieval. Instead of issuing a single query, models plan and search multiple times in a loop. Every loop iteration meant another call to the frontier model, increasing the overall cost and latency per user request.
Concretely, a typical multi-turn search request with gpt-5.6-sol takes >10s and costs ~$0.03 end-to-end, making it prohibitively slow and expensive.
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