Solutions · Use case
Search & Re-Ranking.
Generative AI, scoped to the problem it solves.
Semantic and hybrid search with LLM-based re-ranking for retrieval-quality lift.
Use-case context
Search is the highest-traffic surface in most digital products, and BM25-only stacks leave measurable revenue and engagement on the table once query intent goes beyond keyword match. Bolt-on vector search alone underperforms a tuned BM25 baseline on a messy corpus, so naive replacements often look worse in A/B. We build hybrid retrieval (BM25, dense vectors, and a learned re-ranker) tuned against your real click and conversion logs, with relevance evaluation baked into the deployment loop. The re-ranker is small, fast, and observable, not a frontier-LLM call on every query. Measured by NDCG@k and MRR against a labelled relevance set, plus click-through rate and downstream conversion or task-success lift in live A/B against your existing stack. Latency and per-query cost are hard ceilings. We do not roll out ranking changes without an A/B or interleaving test against the incumbent ranker. Relevance changes that look better offline often lose in live traffic. In our coverage footprint this lands first across legal, technology, retail: the sectors where the data shapes and evaluation criteria line up cleanly with what this use-case actually measures.
How this shows up across industries
Where search & re-ranking lands in production engagements.
Retail · Generative AI Consulting
See industry →In retail, generative AI shifts from buzzword to bottom line via search re-ranking, product-attribute extraction, and post-purchase support deflection.
Telco · Generative AI Consulting
See industry →In telco, generative AI plays in customer-care deflection, network-incident summarisation, and product-recommendation engines.
Retail · AI Integration Services
See industry →Retail integrations connect AI into ecommerce stacks (search, recommendation, and customer-service tooling) without rewriting them.
Media · Data Science & Analytics
See industry →In media, data science targets recommendation, audience segmentation, and content-yield analysis.
Data shape
Product / document / case catalogues, query logs, click logs, conversion or task-success signals, and a relevance-judgement set assembled with your subject-matter experts.
Where it lands first
Industries where search & re-ranking applies.
- 01
Legal
Citation-grounded generative AI for matter management, document review, and drafting workflows.
- 02
Technology
Generative AI inside the product, the codebase, and the internal tooling. Built by engineers, for engineers.
- 03
Retail
Generative AI for search, product-attribute extraction, and post-purchase support.
- 04
Media
Generative AI for editorial research, asset tagging, and personalised distribution. With human-in-the-loop quality gates.
Where we draw the line
We do not roll out ranking changes without an A/B or interleaving test against the incumbent ranker. Relevance changes that look better offline often lose in live traffic.
Talk to us about a search & re-ranking engagement
A 30-minute call to scope where search & re-ranking moves the curve against your evaluation criteria.
Book strategy callWhy work with Veso AI on search & re-ranking
Measured
Evaluation, not opinion
Measured by NDCG@k and MRR against a labelled relevance set, plus click-through rate and downstream conversion or task-success lift in live A/B against your existing stack. Latency and per-query cost are hard ceilings.
Proven
Quality you can measure
In the first two weeks we build a labelled evaluation set with your experts, so quality is measured from day one, not hoped for.
Production
Built to operate
We hand over working software, infrastructure-as-code, and an evaluation harness your team can run and maintain.
Related use-cases
Internal Copilots
Role-shaped copilots over internal knowledge: Confluence, runbooks, policies, code.
Document Intelligence
Citation-grounded retrieval and summarisation over heterogeneous document corpora.
Structured Extraction
Schema-conformant extraction of fields, entities, and tables from messy inputs.
FAQ
Search & Re-Ranking: frequently asked questions
How is success measured for search & re-ranking engagements?
Measured by NDCG@k and MRR against a labelled relevance set, plus click-through rate and downstream conversion or task-success lift in live A/B against your existing stack. Latency and per-query cost are hard ceilings. The evaluation harness is part of the deliverable, not an afterthought. We build it during the engagement so your team can run it against the next prompt, model, or pipeline change without us.
Where does Veso AI NOT apply search & re-ranking?
We do not roll out ranking changes without an A/B or interleaving test against the incumbent ranker. Relevance changes that look better offline often lose in live traffic. This is a deliberate trust boundary, not a capability gap. We are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in those surfaces does not justify it.
Which industries does search & re-ranking apply to?
In our coverage footprint, search & re-ranking most commonly lands in legal, technology, retail, media. The specific deployment shape varies by industry: data shapes, evaluation criteria, and regulators differ enough that we re-scope each engagement against the sector it lands in.
What data shape do you need to start a search & re-ranking engagement?
Product / document / case catalogues, query logs, click logs, conversion or task-success signals, and a relevance-judgement set assembled with your subject-matter experts. In the first two weeks we look at the real data (what exists, what is labelled, where it has to live) and build the plan around what is actually there, not around an assumption.
Which Veso AI services ship search & re-ranking?
search & re-ranking ships under our Generative AI Consulting, Custom Software Development, AI Integration Services, Data Science & Analytics service lines, depending on the integration surface and the build-vs-platform trade-off. Most engagements draw on more than one. The boundary between consulting, custom build, and integration is a scoping decision we make explicit during discovery.
How does a search & re-ranking engagement typically start?
We spend two weeks with your leadership and operators, build an evaluation set with your subject-matter experts, and come back with a clear plan and a clear price. That evaluation set anchors every decision after it (model choice, prompt strategy, retrieval design) so quality is measurable from week one, not from go-live.
Industries where search & re-ranking applies
- Legal Citation-grounded generative AI for matter management, document review, and drafting workflows.
- Technology Generative AI inside the product, the codebase, and the internal tooling. Built by engineers, for engineers.
- Retail Generative AI for search, product-attribute extraction, and post-purchase support.
- Media Generative AI for editorial research, asset tagging, and personalised distribution. With human-in-the-loop quality gates.
Service lines that ship search & re-ranking