Hybrid and multi-vector retrieval with machine-learned, multi-phase ranking - for search, recommendations, RAG and agents. We design, build and operate Vespa applications, drawing on our background in search engineering.
Vespa lets you run real models where the results are ranked. We make sure that power turns into better answers, at a latency and cost you can live with.
Cheap first-phase scoring, richer second-phase models and global re-ranking - each where it pays off.
Lexical, vector and multi-vector signals combined in one query to feed RAG systems and agents.
Content clusters planned for your data, query load and redundancy, with room to scale out.
Judgment lists and offline metrics, so every ranking change is proven before it ships.
Hybrid retrieval and re-ranking behind assistants and agents that must answer from your data.
Product search that blends text match, vectors and business signals in one ranking.
Candidate retrieval and model-based ranking for feeds, related items and personalization.
Precise search over large collections, with grouping for facets and aggregations.
Moving from hand-tuned boosts to multi-phase, machine-learned ranking.
Schemas, queries and ranking rebuilt on Vespa, validated against your current results.
Review your data, queries and ranking needs, and agree on relevance, latency and cost targets.
Build the schema and rank profiles and prove them against your real queries.
Size clusters, deploy to Vespa Cloud or your infrastructure, and cut over with monitoring.
Continuous ranking and performance tuning - or a clean handover with runbooks and training.
Hands-on help designing, building and running Vespa: application package and schema design, rank profiles and multi-phase ranking, hybrid lexical and vector retrieval, model inference in ranking, content cluster sizing, Vespa Cloud or self-hosted deployment, migrations and relevance evaluation.
Vespa tends to fit better when ranking is the hard part: several ranking phases, machine-learned models evaluated per query, tensor math or multi-vector representations, and frequent in-place updates to fields used in ranking. Elasticsearch tends to fit better when you also need log analytics and the wider Elastic Stack, or your team already runs it well.
Yes. Vespa combines BM25 and vector retrieval in one query, can embed text with built-in embedders that run ONNX models, re-ranks results in multiple phases, and documents a RAG searcher that sends retrieved results to an LLM. That makes it a strong retrieval tool for assistants and agents.
It depends on your operations capacity and constraints. Vespa is open source under the Apache 2.0 license and can be self-hosted on your own infrastructure. Vespa Cloud is the hosted platform that provisions and runs the infrastructure for you. We help you weigh control, compliance and operational effort.
We map your indexes and mappings to Vespa schemas, rewrite queries in YQL, rebuild ranking as rank profiles, then feed and verify the data. Before switching traffic we compare relevance and latency on your real queries so the move is proven, not assumed.
A focused architecture or relevance review typically takes 1-2 weeks. New Vespa builds or migrations typically take 4-12 weeks depending on data volume, ranking complexity and integrations. We agree on timelines and success metrics up front.
Tell us what you are building or migrating. In a free 30-minute call we will review where you are, what it would take, and how we would measure success.
