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Your Directory Traffic Disappears Into ChatGPT. We Build You Back In.

If you're a founder watching Google zero-click eat your SEO, you need semantic search that AI agents actually cite -- pgvector, auto-enrichment, proof at 137K listings.

AI-powered directories with semantic search via pgvector, AI-generated descriptions, auto-categorization, sentiment analysis, and recommendations. Proof: Deluxe Astrology 28K Claude enrichments, NAS 137K scored listings.

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AI Directory Development

AI directory development is the practice of building structured, machine-readable listing platforms that use vector embeddings, language model enrichment, and semantic retrieval to serve both human visitors and AI agents. Unlike traditional directories that rely on keyword matching and pagerank, these systems store meaning alongside metadata so that tools like ChatGPT and Perplexity can cite specific listings in direct answers. The result is a directory that participates in AI-mediated search rather than disappearing behind zero-click results.

What is holding your current website back?

Common gaps we find in nearly every audit.

Your directory ranks on Google but AI assistants never cite a single listing because your schema is keyword-stuffed text, not semantically indexed data.
Risk: As AI-mediated search captures more discovery queries, directories without vector retrieval become invisible to the growing share of users who never reach a results page.
Listing quality degrades over time because manual curation does not scale past a few thousand entries, leaving thin or duplicate records that damage trust.
Risk: Thin listings suppress engagement metrics and give AI models low-confidence signals, making your domain less likely to be cited as an authoritative source.
You have no programmatic way to score, categorize, or recommend listings, so every editorial decision is a bottleneck that slows growth and increases operational cost.
Risk: Competitors who automate enrichment and scoring ship updates in hours while your team spends weeks on manual taxonomy work, compounding the quality gap over time.

How We Build This Right

Every safeguard, built in from Day 1.

Structured Data Integrity

Every listing is validated against a defined schema before indexing. Malformed or incomplete records are flagged and routed for enrichment rather than silently published, keeping your structured data consumable by AI crawlers and schema validators.

Vector Index Governance

pgvector embeddings are generated from canonical listing content and versioned so that re-indexing after bulk enrichment does not corrupt retrieval results. Index rebuilds are auditable and rolled back without downtime.

Rate-Controlled Enrichment Pipelines

AI enrichment jobs run through queued, rate-limited workers that respect upstream API constraints and log every model call. You have a full record of which listings were touched, by which model version, and when, for audit or rollback.

What We Build

Purpose-built features for your industry.

pgvector Semantic Search

Listings are embedded using production-grade language models and stored in a pgvector index inside your existing Postgres instance. Queries return results ranked by meaning rather than keyword overlap, so a search for 'beginner-friendly hiking near water' surfaces relevant listings even when those exact words never appear in the record.

Automated Listing Enrichment

New and existing listings pass through a configurable enrichment pipeline that generates descriptions, assigns categories, extracts entities, and scores quality without human review. The NAS engagement proved this at 137K listings; Deluxe Astrology used the same pipeline to produce 28K Claude-generated descriptions in a single batch run.

Sentiment and Quality Scoring

Each listing receives a composite quality score derived from completeness, review sentiment, and content coherence. Scores are stored as structured columns so you can filter, sort, and surface only high-confidence entries in featured placements or API responses.

AI-Native Recommendation Engine

Related listing recommendations are computed from vector similarity rather than co-visit heuristics, which means recommendations work from day one without requiring behavioral data. As traffic grows, collaborative signals can be layered on top of the semantic baseline without replacing it.

Built on a Modern, Secure Stack

Next.jsSupabasepgvectorClaude APIVercel

Our Development Process

From discovery to launch. Quality at every step.

01

Data Audit and Schema Design

1-2 weeks

We ingest your existing listings, identify quality gaps, duplicate clusters, and taxonomy inconsistencies, then define a canonical schema that supports both relational queries and vector retrieval. Deliverable is a documented data model and a gap report before any code is written.

02

Search and Enrichment Infrastructure

2-3 weeks

We configure pgvector inside your Postgres instance, wire up the embedding pipeline, and build the enrichment workers that will process your backlog and handle new submissions. By the end of this phase your existing listings are indexed and searchable via semantic query.

03

Bulk Enrichment and Quality Scoring

1-2 weeks

The enrichment pipeline runs against your full listing catalog. Each record receives AI-generated copy where missing, category assignments, entity tags, and a quality score. We monitor model outputs, spot-check samples, and tune prompts until output quality meets the agreed threshold.

04

Frontend Integration and Handoff

2-3 weeks

Semantic search, recommendations, and scoring are exposed through a typed API that connects to your Next.js or Astro frontend. We deliver documented endpoints, an admin panel for enrichment monitoring, and a runbook so your team can manage the pipeline without us after launch.

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Frequently Asked Questions

Here's what you're actually getting: search that understands what users mean, not just what they typed. AI that writes and categorizes your listings automatically. And honestly, a directory that feels modern instead of like something built in 2015. Those aren't small differences -- they show up directly in your SEO rankings and in how long users actually stick around.
Projects start at $15,000 for solid AI-enhanced directory builds. More complex platforms -- custom scoring systems, multi-language support, advanced recommendation engines -- typically run $20,000 to $35,000. That range reflects real scope differences I've seen across dozens of projects, not arbitrary tiers.
Most clients see ChatGPT and Perplexity begin citing their listings within 6 to 10 weeks of launch, once semantic indexing and auto-enrichment pipelines are running. The exact timeline depends on your existing data quality and how many listings need structured cleanup before pgvector embeddings can be generated accurately.
No full migration is required if you are already on PostgreSQL -- pgvector installs as an extension. If you are on MySQL, MongoDB, or another store, we handle the migration as part of the build and keep downtime to a minimum. We assess your stack in the discovery call and give you a clear technical plan before any work starts.
Auto-enrichment means each listing is automatically expanded with structured attributes -- categories, descriptions, location data, and entity tags -- pulled from your existing records and supplemented via APIs. This gives AI models enough context to cite your directory confidently rather than skipping it for a richer source.
Remote collaboration works well with us because we default to async-first communication with scheduled weekly video check-ins. You get a shared project board, a Slack channel, and written specs before any sprint begins. Being UK-based means we share your timezone fully, so responses and decisions rarely wait more than a few hours.
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