Search relevance & discovery

Unlock your search.
Build better journeys.
Grow together.

Let’s build search your customers love. Together, we turn your data into relevant discoveries, helpful conversations and measurable growth.

Let’s increase your impact Watch the experience
+6% MRRNotion search
Learning to Rank implementation
PRODUCT WALKTHROUGHFictional travel storefront · Autoplay demo
wayfarer.example / stays / austin•••
wayfarer↗
StaysExperiencesSaved trips
Where to?Austin, Texas
Check-in — Check-outAdd dates
Travelers2 adults · 1 room
United States › Texas › Austin

Find your Austin.

Explore stays for your next getaway

Recommended
4 star hotels in Austin
4 starsFree cancellationPool
Illustrative city hotel exterior
★★★★

The Central Hotel

Downtown Austin

City views · Restaurant · Fitness center
8.7 Excellent$240per night
Illustrative hotel swimming pool with sun loungers
★★★★

Garden House Suites

Near Zilker Park

Family suites · Outdoor pool · Near parks
9.4 Excellent$265per night
Illustrative bright hotel suite
★★★★

The Parkside Residence

South Austin

Spacious rooms · Kitchenette · Garden
9.1 Excellent$285per night
01 / 05A query gives you a list.
0:00 / 0:36

Illustrative product experience with fictional properties, prices and availability. Explore the journey from customer intent to a confident choice.

Production experience

Building great search together.
Real teams. Measurable progress.

NotionKnowledge search
+6% MRR

Help users find the right answer earlier.

We implemented Learning to Rank to improve Notion’s search, achieving a 6% improvement in Mean Reciprocal Rank.

Candidate resultsLearning to RankBetter ordering

Mean Reciprocal Rank measures how early the first relevant result appears. Higher is better. Product experiments connect relevance improvements to conversion outcomes.

Seeking AlphaFinancial research
Research at the scale
of millions of users.

For a financial research application serving millions of users, we implemented advanced indexing, time series adjustments and hybrid search.

Ticker symbolsLexical precision
+
ArticlesEmbedding retrieval
Advanced indexing + time series adjustments
Explore the case study
From relevance to revenue

Unlock the potential
of every search.

Help every traveler discover a stay they love. Relevant recommendations create a smoother journey from curiosity to a confident booking.

Let’s connect relevance improvements to the outcomes your team cares about: more discovery, more engagement and more completed bookings.

RelevanceEngagementConversion
THE BUSINESS CASEIllustrative scenario

Same traffic.
More bookings.

100,000 search sessions per month

+300 bookingsevery month
+10%
0%Relative lift20%

Illustrative growth scenario. Traffic stays fixed; the slider changes the assumed conversion lift. Relevance gains such as MRR must be validated separately against bookings in an online experiment.

Building the possibilities

Bring your data to life.
Build the next experience together.

Your catalog, your content and your customer knowledge can do more together. We connect search infrastructure, models and ranking algorithms to unlock that potential.

TensorOpsBUILT TOGETHER. CONNECTED BY RELEVANCE.
THE RELEVANCE ARCHITECTURE

Your data. Our expertise.
More possibilities.

Connect the tools you trust to the experiences your customers love.

An icon-based search architectureDocuments pass through an embedding model into a search index such as Elasticsearch. Catalog data and user queries also feed retrieval. A ranking algorithm turns candidates into relevant product experiences. Evaluation feeds learning back into ranking.01 / CONNECT02 / UNDERSTAND03 / RANK04 / ACTEvaluate · learn · growContentDocuments & articlesYour catalogProducts & attributesCustomer intentEmbedding modelMeaning & contextElasticsearchHybrid search indexRanking algorithmLearning to RankGreat experiencesSearch · answers · agents
Data → understanding → actionExample stack · tailored to your infrastructure
Your data. A better decision.

Agentic search turns a goal into a sequence of retrieval and tool calls. It asks the right follow-up questions, checks structured data and explains the recommendation. Strong relevance is still the foundation.

Built around your priorities

One search experience.
Three reasons to get it right.

VP R&D

Make relevance measurable.

Build a representative evaluation set, uncover opportunities and compare retrieval and ranking changes before rollout.

MRR · NDCG · recall · regression coverage
Product leadership

Make discovery convert.

Test whether users reach useful results faster, reformulate fewer queries and complete more of the journeys that matter.

Search success · abandonment · conversion
CTO

Make it work in your stack.

Integrate with your index, data and permissions. Set latency and cost budgets, then roll out with monitoring and fallbacks.

p95 latency · cost per query · access controls
Under the hood

Build on the stack you have. Improve the results it delivers.

A complete relevance toolkit

Use the right retrieval and ranking approach for each query, with evaluation throughout.

Learning to Rank & reranking

Combine relevance judgments, content features and behavioral signals to put useful results first.

Hybrid retrieval & advanced indexing

Use lexical matching for exact identifiers and embeddings for meaning. Tune indexing and fusion to your domain.

Temporal relevance

Account for timestamps, reporting periods and time series context when freshness changes the answer.

Agentic search & structured tools

Turn intent into clarifying questions, retrieval and calls to SQL, catalog or availability services, with source-grounded answers.

Domain-specific embeddings

Tune embeddings to your vocabulary, your content and the way your customers express their needs.

Evaluation & controlled rollout

Track relevance alongside conversion, latency and cost. Validate changes with online experiments and regression checks.

From diagnosis to production

Let’s unlock your next search opportunity.

Diagnose

Discover the potential

Review real queries, search journeys and your current retrieval stack. Agree on what a better result means for your product.

  • Representative query set and relevance judgments
  • Opportunity mapping across important user segments
  • Baseline quality, latency and cost
Typical timeline: A clear, prioritized roadmapDiscuss this engagement
Evaluate

Prove the improvement

Test retrieval and ranking changes against your baseline, then connect the strongest candidates to a product experiment.

  • Hybrid retrieval and ranking experiments
  • Offline evaluation and regression checks
  • Online test plan tied to a business outcome
Typical timeline: Evidence before a broad rolloutDiscuss this engagement
Production

Ship and keep learning

Integrate into your product with your team. Monitor relevance and business outcomes as queries, content and user behavior evolve.

  • Integration with existing permissions and services
  • Staged rollout, fallbacks and observability
  • Knowledge transfer and ongoing evaluation
Typical timeline: A search system your team can ownDiscuss this engagement
Beyond catalogs and content

The same foundation. Another domain.

MDClone

Natural-language search over clinical data

A generative AI agent translating clinical queries into rigorous statistical analysis. MVP shipped in 3 months.

Read the case study

Let’s build your next search success.

Bring your ideas and ambitions. Together, we’ll unlock the potential in your data, create better customer journeys and grow your product’s impact.

Book a search relevance session
Search Relevance & Agentic Search | TensorOps