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Durham, North Carolina · Recruitment catalogue discovery

Recruitment Catalogue Discovery Development for Durham

A visitor may describe the work they want without knowing the category your recruitment catalogue uses. Natural-language discovery can help them explore published opportunities, while explicit fields keep requirements and locations understandable. We help Durham, North Carolina product teams develop that search-to-posting journey around their approved catalogue data.

Our Appster work, recorded in Mexico, connects semantic vector search to catalogue administration and prerendered public destinations. It supplies relevant discovery engineering for a separately scoped recruitment platform. Curiosive uses AI to accelerate implementation under engineer review and stays involved as a long-term product partner as postings, search examples and operating needs change.

Start a partnershipFrom $2,000 per month
Concept of a natural-language request connected to filtered opportunity cards, a selected posting and a retired-record tray
AI-generated opportunity discovery concept; not an Appster screenshot, real job posting or applicant assessment.
25
published case studies
5+
years building for production
12
countries the work runs in
100%
of code read by a senior engineer
1 day
to hear back, from the engineer
Browse industry workflows →

Separate descriptive search from explicit posting fields

Begin with representative opportunities and the fields visitors use to understand them. A free-form request and a selected location or role category serve different purposes. Agree which structured filters constrain the result set and which text helps retrieve potentially relevant records. Show the request that produced the visible results, and keep the employer-approved requirements on the posting itself. Evaluate retrieval with approved examples rather than treating semantic similarity as a decision about an applicant.

Relevant work: Appster's semantic app catalogue

The Appster case describes natural-language discovery through semantic vector search over an OpenAI-embedded application catalogue. A full administration panel maintains records, while prerendered public pages expose catalogue destinations. Next.js, Supabase, TypeScript and Tailwind CSS form the stack. The project is recorded in Mexico and concerns app discovery. It demonstrates search within a maintained full stack product; recruitment posting schemas, expiry policies and existing-system connections would be defined for a new Durham engagement.

Keep the search index connected to publication state

A posting can change its requirements, close or become temporarily unavailable. Identify the source that owns those changes and agree when they reach public pages and search. Define whether a closed posting remains readable with an explanation or follows another approved destination rule. A visitor returning through an older result should understand the current record state. Review a corrected location, a withdrawn opportunity and an incomplete update alongside a newly published posting.

Give discovery results a clear destination and next action

Each result needs enough context to identify the intended opportunity and open its current details. Agree how an empty search helps someone refine the request and how an unavailable search service is explained. If the next step belongs to another application system, define the supported handoff without assuming that system accepts every field. AI accelerates familiar interface and indexing work; engineers review record relationships, filter behavior and recovery. Recruitment owners approve the posting content and the purpose of the discovery feature.

Review an opportunity search against current published records

For a new engagement, bring representative postings, approved search examples and the intended structured-filter rules. Proposed acceptance examples: a result opens the intended published opportunity; explicit filters constrain results under the agreed policy; an expired posting follows the approved destination rule; and a search failure is distinguishable from no matching records. Include a revised requirement and an ambiguous request. These are proposed discovery checks, not claims about Appster's historical recruitment delivery, applicant assessment or job-match outcomes.

A product partner as the opportunity catalogue evolves

Search examples, posting corrections and visitor questions show where the catalogue needs clearer fields or a more useful refinement path. We collaborate through a shared backlog and reviewed releases so those findings improve the product. Monthly AI-accelerated development starts from $2,000, with capacity, data ownership and supported integrations agreed in writing. Curiosive LLC works remotely with Durham teams through agreed Eastern-time review windows, with engineering led from Ankara. The partnership continues beyond a first semantic-search demonstration.

Delivered work in detail

Appster: AI app discovery with a full stack content platform

Natural-language discovery across a catalogue of more than 2,700 apps, with semantic vector search, administration and programmatic SEO pages.

Read the case study →
AI-accelerated development
from$2,000
per month

Senior engineers building your product with AI coding agents: web, mobile, SaaS or browser extension, reviewed before anything ships.

  • Web, mobile, SaaS and browser extensions
  • AI-assisted delivery under senior engineer review
  • Scope and capacity agreed in writing, renews monthly

AI-accelerated development, billed monthly. We confirm scope, capacity and start date in writing before checkout.

Questions

The things people ask before starting.

Can you develop recruitment catalogue discovery for a Durham team?

Yes. Bring representative postings, approved query examples and the intended filter rules so we can scope a focused opportunity-search product.

How is Appster relevant to recruitment discovery?

Its Mexico-recorded case demonstrates semantic search within a maintained catalogue and public page system. Recruitment schemas and publication policies would be new requirements.

Can search results reflect expired or revised postings?

That can be an agreed requirement. Define the source of publication state, update timing and the destination for closed opportunities before implementation.

Can you keep improving discovery after launch?

Yes. A continuing engagement can prioritize query examples, catalogue corrections and visitor feedback through a shared backlog and reviewed releases.

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How it starts

What happens after you write to us.

  1. 01

    You describe the product

    One paragraph on what you are building, roughly when you need it, and who it is for. No brief, no form with fifteen fields.

  2. 02

    You get a reply within a day

    From the engineer who would do the work, not a sales team. If we are not the right fit we say so, and where possible we point you somewhere better.

  3. 03

    You get a scoped plan

    Scope, price and timeline in writing before anything is committed. You decide with the numbers in front of you.

No long contract to begin, no lock-in to our tooling, and the code is yours from the first commit.

Start a conversation
Contact

Looking for an engineering partner for the long haul?

A short note about the product, the timeline and who it is for is enough to start. You will hear back from the engineer who would do the work, not a sales team.

hello@curiosive.comSee what we have shipped

Ankara / Türkiye · Working across European and US time zones