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AI products

AI App Development Company

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

What AI app development looks like in practice

Most valuable AI features are focused: a support assistant that answers from your own help content, a tool that drafts documents or messages, search that understands meaning, or a workflow that classifies and routes incoming requests. We start from the user's job and ask where AI genuinely saves time or improves quality — and where a normal interface is simpler.

From there we build the full feature, not just the model call: the interface, loading and error states, guardrails, usage limits, logging, and a way for users to correct the AI when it gets something wrong.

Using your own data with retrieval

Generic AI answers are rarely good enough for a business product. Retrieval-augmented generation (RAG) lets the model answer from your own documents, products, or records, so responses are specific and grounded. We set up the ingestion, embeddings, vector search, and access controls so each user only retrieves what they are allowed to see.

Where your product already runs on Postgres, vector search can live in the same database through pgvector, which keeps the architecture simple.

Quality, safety, and cost control

AI features need testing like any other feature, but with sample sets instead of fixed outputs. Before release we evaluate answers against realistic examples, add guardrails for off-topic or unsafe requests, and set limits so one heavy user cannot run up your bill.

We also design for model changes. Providers update and replace models regularly, so prompts, evaluation sets, and provider settings are kept in one place where they can be updated and re-tested.

AI products we can build

Typical builds include AI assistants inside web and mobile apps, document and knowledge-base Q&A, content generation tools, AI-powered search, and vertical AI products such as our SmartLaw AI case study. For an early-stage idea, an AI MVP usually falls in the advanced MVP band; the app cost calculator includes AI as a product type and a feature.

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.

Related work

Full SmartLaw AI web product — marketing site, user assistant dashboard, and Super Admin Portal for users, roles, analytics, monitoring, and financials.

SmartLaw AI case study →

FAQ

Which AI models do you work with?
We work with the major commercial AI APIs and choose per feature based on quality, speed, cost, and data requirements. The architecture keeps the provider swappable so you are not locked to one model.
Can you add AI to our existing app?
Yes. We often add an AI feature to a product that is already live — an assistant, smart search, or automation — without rebuilding the rest of the app.
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.

Have an AI product idea?

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

Contact Codeles