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AI products

AI features tied to real user jobs

We integrate LLMs and retrieval where they reduce work or unlock a clear product moment—not as a slide-deck buzzword. Evaluation, cost control, and failure states are part of the design.

Who this is for

  • Founders adding AI into an existing product
  • Teams prototyping an AI-native workflow
  • Companies that need RAG over their own documents or data

Problems we solve

  • Demos that cannot survive real user prompts
  • Uncontrolled token cost and latency
  • No evaluation loop before shipping
  • Sensitive data sent to models without a policy

Deliverables

  • Use-case definition and success criteria
  • Prompt / tool / retrieval design
  • Integration into your app or backend
  • Basic monitoring and failure UX
  • Cost and rate-limit considerations documented

Process

  1. 01

    Discovery

    Which jobs AI should own vs classic UI.

  2. 02

    Strategy

    Model choices, data boundaries, and risk.

  3. 03

    Design

    Human-in-the-loop UX where needed.

  4. 04

    Build

    Pipelines, APIs, and product surfaces.

  5. 05

    Eval

    Sample sets and regression checks before release.

  6. 06

    Operate

    Usage monitoring and iteration.

Technologies

OpenAI / compatible APIsRAG patternsNext.jsPython or Node servicesVector stores as needed

Timeline & engagement

Timeline
Narrow AI features can ship in weeks; larger AI products are phased.
Engagement
Fixed scope for a defined feature, or a growth engagement for an AI-native product.

FAQ

Do you train custom foundation models?
Typically no. We integrate and evaluate existing model APIs and retrieval systems. Custom training is only discussed when there is a clear need and budget.
Can you work with our private data?
Yes, with agreed boundaries—what leaves your VPC, what is redacted, and what is stored for evaluation.

Ready to scope this work?

Share your product goals — we reply within one business day with next steps.

Contact Codeles