Often we replace weak native CMS search or ageing Solr setups; sometimes we augment them in phases. Migration plans include side-by-side evaluation, redirect of search URLs if needed, and rollback paths. If keyword search still excels for exact SKU entry, hybrid mode keeps both — semantic for discovery, lexical for part numbers. We are explicit when your data quality must improve before vectors help; fixing taxonomy can precede embeddings. Pilots measure click-through and time-to-right-document so you extend or stop on evidence, not enthusiasm for the latest AI headline.
AI
AI-powered site search
Semantic search and product discovery that understands intent — better than basic keyword matching.
Semantic site search and product discovery for richer customer journeys.
What you get
- Semantic indexing
- Relevant result ranking
- Product discovery UX
- API keys secured server-side
Who AI-powered search is for
AI-powered search from Faraday Web Services is for organisations whose visitors describe problems in plain language — or search with synonyms, symptoms, and job titles — while the site still indexes SKUs, internal codes, and jargon. Typical clients include B2B manufacturers, distributors, training catalogues, professional bodies with large resource libraries, and ecommerce brands where keyword-only search drives bounce and support tickets.
You are a strong fit when analytics show high search exit rates, support repeats “where is…?” questions, or your catalogue outgrew manual tagging. You are also a strong fit when answers must stay inside approved content — policies, datasheets, compatibility guides — not the open web. Public chat may overlap; AI chatbot integration handles dialogue, while search returns ranked results fast. Deeper automation belongs in AI integrations and AI business automation.
Implementations often follow custom website design or platform upgrades where information architecture is finally trustworthy. We connect indexes via API integrations to PIM, ERP, or CMS exports so results reflect stock, locale, and permissions.
Semantic search versus keyword search
Keyword search fails when users do not know your terminology. Semantic search maps queries and documents into embeddings so “motor won’t start” can surface troubleshooting guides titled with model numbers. Hybrid approaches combine lexical matching for SKUs with semantic recall for concepts — tuning is empirical, not a slider left at default.
Relevance is a product decision: boost newer docs, deprioritise archived PDFs, pin seasonal collections, or hide B2B-only items from retail visitors. We document ranking rules editors can reason about, and we A/B test with real query logs — not demo phrases alone. When content gaps appear, findings feed AI-assisted content workflows or on-page SEO to close them properly.
Facets, filters and business rules
Vectors do not replace filters. Category, voltage, region, language, and stock status still narrow results deterministically. We keep business rules in code or configuration your team controls — not buried inside an opaque SaaS dashboard. That separation makes audits easier when compliance asks why a discontinued product appeared.
Logged-in and permission-aware results
Partner portals and customer areas must not leak documents through search that navigation would hide. Indexing respects roles: collections per audience, field-level redaction, and rebuild jobs when permissions change. SSO integrations are scoped with least privilege — search is often the first place misconfigured ACLs show up.
Indexing, embeddings and freshness
Search quality lives in the index pipeline: HTML and PDF extraction, language detection, chunk boundaries, metadata (title, URL, date, product family), and deduplication. Embeddings are regenerated on schedules or webhooks when content publishes — stale indexes erode trust faster than no search at all.
Large catalogues use incremental updates and queue workers so editors are not blocked. We cache embeddings where sensible and monitor rebuild duration after bulk imports. For multilingual sites, per-locale indexes prevent French queries from retrieving English-only chunks unless you explicitly allow cross-language fallback. We also define synonym dictionaries maintained by merchandising — embeddings help, but approved synonyms still matter for part numbers, legacy names, and acronyms buyers type literally.
Performance, security and graceful degradation
Search must stay fast under traffic. We set latency budgets, pagination, and optional query suggestions that do not call models on every keystroke. Provider outages should fall back to keyword or cached results — visitors see useful links, not an empty state. API keys and embedding jobs run server-side with security hardening consistent with the rest of your site. Load tests simulate peak campaigns and Monday-morning support spikes so you discover slow queries before a product launch drives tenfold traffic to the help centre.
Logging captures queries and clicks for tuning while redacting personal data where policies require it. Rate limits protect against abuse and runaway API spend. Dashboards show top failed queries — a backlog for content and merchandising teams.
Core Web Vitals and front-end discipline
Search UI loads deferred assets, avoids layout shift when results render, and keeps accessibility for keyboard and screen-reader users. We test on mobile templates where most B2B research still happens. If legacy templates are heavy, we pair search rollout with performance optimisation rather than stacking scripts blindly.
Implementation approach
Discovery reviews query logs (if available), top content types, systems of record, and success metrics — click-through, reduced support volume, faster order placement. Pilots cover a subset of the catalogue or knowledge base on staging with side-by-side comparison to legacy search. Rollout includes editor runbooks and monitoring alerts. Stakeholder workshops translate internal jargon into indexable labels — without that step, even strong embeddings fail to connect buyer language to the datasheet your engineers titled with internal codes only.
Our process page describes studio phases; you work with engineers who tune ranking, not resellers of a black-box appliance. When organic discovery matters alongside on-site search, coordinate with SEO audit findings so new landing pages and indexable hubs complement what search exposes internally.
Pricing factors and ongoing cost
Investment scales with document volume, languages, update frequency, SSO complexity, and custom ranking. Embedding and query API costs are modelled with monthly caps and alerts. Proposals separate build, index migration, and optional tuning retainers. We include a handover session for your developers or agency on how embeddings refresh, how to pause indexing during CMS migrations, and which environment variables must never reach front-end bundles — so search keeps working after our hypercare window ends.
Request estimates via free quote or contact. Compare related capabilities in the services catalogue. Company background: about; policies: legal information.
Why Faraday for AI search
Clients choose us when search is business-critical — not a demo typeahead — and must respect permissions, performance, and truth in stock and policy content. We implement alongside the same team delivering custom websites and API integrations, so search survives real catalogues and CRM-driven updates.
English and French delivery supports cross-border catalogues. After search stabilises, teams often add chatbots that cite the same corpora, or AI-driven SEO to close content gaps surfaced in failed queries. Common questions: site FAQ. We also train merchandising and support leads to read “zero result” reports as a product signal — which specs are missing, which synonyms buyers use, and which PDFs never made it into the index pipeline.
Frequently asked questions
Proper site search returns documents and snippets grounded in your index — not free-form essays. If you add generative summaries, they are scoped, cite sources, and fail closed when retrieval is weak. That is different from open chat. We configure thresholds and abstention messages so visitors are not misled. Chat-style experiences belong in {link:ai-chatbot-integration|chatbot integration} with the same corpus discipline. Regression tests on representative queries run when you change embeddings or ranking rules so a prompt tweak does not silently widen creative answers on factual product pages.
Webhooks or scheduled jobs re-index on publish, unpublish, and bulk import. Runbooks define who triggers full rebuilds, how long they take, and how to pause indexing during incidents. Dashboards flag stale collections. For ERP-driven stock, integration frequency is agreed — hourly, nightly, or event-driven — so search does not promise availability the cart cannot fulfil. Editors receive a simple checklist when they add a new PDF or retire a product line so search stays aligned with merchandising without waiting for someone to notice drift in analytics.
Embeddings and queries should minimise personal data. Discovery classifies fields indexed, redacts where required, and chooses regions or enterprise agreements when GDPR demands it. Logs may store anonymised query strings for tuning with retention limits. If authenticated search could expose private orders, ACL checks happen before results render — not after. Your DPO receives a data-flow diagram for indexing and query paths rather than a generic vendor privacy PDF alone, which speeds internal sign-off before production traffic hits the new index.
Choices depend on scale, residency, and ops comfort — managed vector services, self-hosted open-source, or database extensions. Model providers for embeddings may differ from chat models. We document costs per million tokens and storage. Staging uses separate projects; production keys never ship to front-end bundles. Vendor changes are planned with re-embedding windows communicated to editors. We favour architectures your team can operate: backup, monitoring, and capacity notes are part of handover, not a black-box SaaS you cannot inspect when costs spike.
A pilot on a bounded corpus — for example a knowledge hub or one product family — often completes in a few weeks after access and sample content are ready. Enterprise catalogues with SSO, multiple languages, and ERP feeds take longer because data mapping dominates. We share phased dates for index design, staging review, load testing, and production cutover. Sample exports from PIM, DAM, or legacy Solr early in discovery prevent the critical path from stalling on “we will send fields next week” while engineering is otherwise ready.
Yes. We integrate via plugins, custom endpoints, or headless front ends without mandatory replatforming. Performance and theme conflicts are tested on key templates. If WordPress search failed because content lives in PDFs or external PIM, we fix ingestion — not only the UI widget. Mobile and accessibility reviews cover the search modal and results list, because most B2B traffic still discovers answers on a phone between meetings. Staging previews let editors validate snippets before visitors see changed ranking behaviour on high-traffic pages.
Hypercare covers launch-week defects; optional retainers handle relevance tuning, new collections, and provider updates. Monthly reviews of failed queries drive content and ranking tweaks. Many clients expand into {link:ai-integrations|broader AI integrations} once search analytics prove which workflows deserve automation next. Executive summaries translate zero-result queries, API spend, and top content fixes into language leadership understands without reading embedding jargon. Synonym lists and business terminology are revised when support reports phrasing the index does not yet recognise on live traffic.
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