Your search is failing somewhere right now. We find the layer that broke and re-tune it.
Search operation, not search observation.
Production search fails silently. We read your real failures, attribute each one to a layer (lexical, semantic, or rerank), and re-tune that layer. The assets we produce (fine-tuned embeddings, learned token weights, distilled rerankers) are yours, not licensed from a vendor.
Assembling a search stack is easy now. Keeping it good isn't.
Anyone can wire OpenAI embeddings, Pinecone, and a Cohere reranker in a weekend. The market has solved assembly. What it has not solved is operation: what to do after launch, when the system starts failing in production-specific ways.
Three questions nobody answers:
When my search fails, which layer caused it? Lexical, semantic, or rerank. Guess wrong and you spend a week tuning the wrong component.
Has the frontier moved past me? New embedding models ship monthly. Public leaderboards like MTEB are overfit and routinely contradicted on real data. So which single change actually buys you quality?
What do I actually own? When your search system is three vendor APIs, your IP is rented.
Existing tools observe these problems. We fix them.
The loop: production failures in, tuned components out.
your search stack
production traffic
A score tells you something broke. It can't tell you which layer.
Tools like Ragas, Arize, Galileo, and LangSmith can tell you retrieval quality moved. They rarely tell you why. A lower score might come from weak exact-match behavior, an embedding model missing domain language, a reranker over-penalizing the right passage, or a chunking strategy that split the evidence in half.
PreciseSearch turns those failures into an attribution map. We separate lexical misses from semantic misses, rerank misses, and indexing mistakes, then tune the layer responsible instead of rebuilding the whole stack.
The output is not a dashboard note. It is a changed component: learned sparse weights, fine-tuned embedding adapters, revised hybrid-search settings, or a distilled reranker that runs inside your serving environment.
Don't rebuild every six months. Upgrade what actually matters.
New embedding models, sparse retrievers, and rerankers keep shipping. Public leaderboards make each one look urgent. But a model that wins on MTEB can lose on your corpus once latency, cost, fusion, and reranking are in the loop.
PreciseSearch keeps the upgrade question grounded in your operating stack. We evaluate new candidates against your queries, your index, and your production constraints, then identify the smallest swap that actually moves the frontier.
In one client assessment, 10 of 12 newer embedding models failed to improve end-to-end stack performance. The incumbent stack was a year old; the new models were up to 3× more expensive, and several looked stronger on public leaderboards. In general, layer-local wins often disappear once retrieval, reranking, latency, and cost are measured together.
What this looks like in production.
Legal search stack, production constraints. The goal was not a leaderboard win in isolation. The stack had to preserve frontier-level retrieval quality under real serving constraints: latency budget, embedding cost, sparse-serving complexity, and assets the team could keep improving inside its own VPC.
We selected dense and sparse candidates by post-tuning behavior, measured the stack inside a production mirror with the reranker in the loop, and jointly tuned the retrievers in the same setup that would serve traffic. The final stack matched the all-premium build on Recall@40, with lower latency, lower embedding cost, and no GPU sparse-serving overhead.
You walk away with a proprietary stack.
The loop produces assets, not just answers. Every re-tune becomes IP your competitors can't replicate by paying the same vendors. Owned by you, not licensed from us.
Built for teams shipping search to production.
Verticals
- AI-native SaaS where retrieval quality is the product
- Legal: case law, depositions, contracts
- Finance: document QA under an accuracy floor
- Any domain where search quality is a customer-facing KPI
Roles
- CTOs and technical founders building AI-first products
- Heads of AI accountable for a customer-facing retrieval KPI
- Search / RAG infrastructure leads operating a stack at scale
Book a 30-min call with the founder.
No data upload. We'll spend the call understanding where your retrieval is breaking, and leave you with a concrete next step: typically a two-week attribution report, a paid pilot on one layer, or a scoped engagement, whichever fits.
The deep dives behind PreciseSearch.
The Search That Works series, and the field note behind it.
From a production legal-search engagement: a ten-point component lead all but disappeared in the stack that shipped.
Top of the MTEB law leaderboard: a 32k context, multilingual coverage, and roughly 3x the accuracy per dollar of the nearest legal rival.
The retrieval engagement behind the parity result: the all-premium build's accuracy at lower latency and 9× lower embedding cost.