LATYNEX
Services

AI as a feature inside a real product — not the whole product

Search, document processing, classification, recommendations and copilots — with human review and auditability built in.

Direct LATYNEX delivery

How this differs from Custom AI Agent Development

Custom AI Agent Development is a standalone agent or conversational layer — a distinct executor. This page is about AI embedded as one feature inside a fuller web/mobile product (Web Application Development, Mobile App Development) — search, extraction, scoring — alongside the product's other, non-AI functionality.

The feature types we build

  • AI search and knowledge-base search over your own content
  • Document processing and structured extraction
  • Classification and scoring — the same pattern behind Freelance Hunter AI and the Tender Broker Agent internally (see [Portfolio](/ai/portfolio/))
  • Recommendations
  • Summarisation of long content into something scannable
  • Copilots — a bounded, task-specific assistant inside the product's own UI
  • Workflow decisions — the AI proposes, a human or a rule confirms

Confidence thresholds and human review

Every AI feature that takes or proposes an action has a defined confidence threshold below which it defers to a human, rather than guessing silently — the same discipline described in Common AI Lead Qualification Mistakes and How to Handle AI Agent Failures and Retries, applied inside a product rather than a standalone agent.

Auditability, permissions and cost controls

AI decisions inside a product need to be traceable (what was decided, on what input, when) for accountability — and usage needs cost controls (rate limits, caching, model-tier selection) so an AI feature's running cost stays predictable rather than open-ended.

Fallback logic

Every AI feature has a defined fallback for when the underlying model call fails or times out — the rest of the product keeps working; the AI feature degrades gracefully rather than breaking the whole application. See What Happens When an AI Automation Fails for the general principle.

Questions

Is this the same as adding a chatbot to our site?+

No — a chatbot is one specific interface pattern; this covers AI features (search, extraction, scoring, copilots) embedded inside a working product's actual workflow.

Can this be added to an application you didn't originally build?+

Yes, in principle — depends on the existing codebase and data access, assessed during scoping.

What happens if the AI feature is wrong?+

A defined confidence threshold and human-review path — the same discipline as our standalone AI agents, adapted to sit inside a broader product.

See Custom AI Agent Development
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