Solutions · Use case
Search & Re-Ranking.
Generative AI, scoped to the shape of the problem it solves.
Semantic and hybrid search with LLM-based re-ranking for retrieval-quality lift.
Use-case context
Search is the single highest-traffic surface in most digital products, and BM25-only stacks leave measurable revenue and engagement on the table once query intent gets richer than keyword match. Bolt-on vector search alone tends to underperform a tuned BM25 baseline once the corpus is messy, so naive replacements often look worse in A/B. We build hybrid retrieval — BM25 plus dense vectors plus a learned re-ranker — tuned against your actual 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 the existing search stack. Latency and per-query cost are tracked as hard ceilings. We do not roll out search-ranking changes without an A/B test or interleaving test against the incumbent ranker — relevance changes that look better offline frequently 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 that include search & re-ranking in their applicable use-cases.
- 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 search-ranking changes without an A/B test or interleaving test against the incumbent ranker — relevance changes that look better offline frequently lose in live traffic.
Talk to us about a search & re-ranking engagement
A 30-minute call to scope where search & re-ranking actually 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 the existing search stack. Latency and per-query cost are tracked as hard ceilings.
Fixed-fee
After paid discovery
Two-week discovery assembles the labelled evaluation set with your subject-matter experts, then converts into a fixed-fee proposal with explicit gates.
Your repo
Your IP, day one
Code, infrastructure-as-code, evaluation harness, and runbooks land in your accounts — no vendor lock-in on the data, models, or evaluation history.
Related use-cases
Internal Copilots
Role-shaped copilots over internal knowledge corpora — 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 the existing search stack. Latency and per-query cost are tracked as 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 search-ranking changes without an A/B test or interleaving test against the incumbent ranker — relevance changes that look better offline frequently 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. During the paid two-week discovery we map the actual data surface — what exists, what is labelled, what residency posture it carries — and the proposal for the next gate is shaped against that, not against 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?
With a paid two-week discovery: workshops with leadership and operators, an evaluation-set assembled with your subject-matter experts, and a fixed-fee proposal for the next gate. The evaluation set anchors every subsequent decision — 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