AI INTEGRATION

Build useful AI features into your app.

We integrate on-device machine learning, language models, and computer vision into mobile products. We test the feature against real data and account for speed, privacy, reliability, and running costs.

Start a projectSee our work
pk@developer: ~/ai-ml

$ ls ./deliverables

AI and machine learning: scope of work.

We agree on the deliverables for your project. These are the areas we can help with.

On-device ML

We integrate Core ML and TensorFlow Lite models that process data on the device without sending it to a server for inference.

LLM features

We build assistants, search, summarization, and agent features with evaluations, safeguards, and cost controls.

Computer vision

We implement detection, classification, text recognition, and tracking, and test them in the conditions the app needs to handle.

Model preparation and deployment

We prepare models for deployment through training, format conversion, and quantization, then integrate them into the app.

Privacy & safety

We consider on-device processing first, limit the data collected, and add safeguards appropriate to the feature.

Evaluation and monitoring

We evaluate model quality in production and monitor changes in its performance over time.

$ ./build --steps

How we approach the work.

We plan the scope with you and review progress together as the work develops.

  1. Use-case & feasibility

    We define what the feature needs to achieve and assess whether the available models can do it.

  2. Model prototype

    We test a model with real project data before committing to production integration.

  3. On-device / cloud

    We decide which processing belongs on the device and which needs a server, then connect the model to the app.

  4. Quality & guardrails

    We build evaluations and safeguards, and check the cost of language-model usage.

  5. Deploy & monitor

    We release the feature, monitor its behavior, and use the findings to improve it.

$ cat stack.json

The tools behind the work.

  • Core ML
  • TensorFlow Lite
  • PyTorch
  • ONNX
  • Vision
  • Create ML
  • OpenAI API
  • RAG

$ pk --outcomes

What we focus on.

pk@developer: ~ $ pk --outcomes ai-ml

On-device

local processing with lower network latency

Evaluated

model quality checked in production

Usage costs

monitored with controls for LLM usage

$ man ai-ml

Questions, answered.

We consider on-device AI when privacy, offline access, or response time matters. Cloud processing can fit models that are too large for the device or need frequent updates. We can combine the two approaches where the product needs both.

We build evaluation sets to check the feature’s output, add guardrails and fallback behavior, and monitor it in use. These checks help us find failures and decide what needs improvement.

We use caching, smaller models for routine tasks, and usage limits. We also optimize prompts and retrieval, then check the cost of running the feature before scaling it.

We start with on-device processing and minimizing the data the feature needs. We explain what data, if any, leaves the device and why, so you can understand how the proposed design handles it.

$ ls ../related

iOS and Android apps

Wearable app development

IoT companion apps

$ ./start_project --service ai

Talk to us about
AI and machine learning.

Tell us what you need help with. We will discuss the scope, timing, and cost.

Discuss your projectAll services