AI Engineering & Consulting
We build AI-native features and workflows for iOS, web, and internal tools — with a focus on local LLMs, Apple Intelligence, privacy-first integrations, and production-ready systems that solve real problems, not demos.
What we do
Concrete AI engineering services designed to ship production features, not prototypes. We work end-to-end: from data prep and model selection to UI integration and deployment.
AI-Native iOS Apps
Integrate Apple Intelligence, Foundation Models, and on-device ML into SwiftUI apps. From natural language interfaces to contextual suggestions, we build features that feel native and respect user privacy.
Local & Private LLM Integration
Run language models on-device or on private infrastructure. We help you choose the right model, optimize for your hardware constraints, and build interfaces that never send data to the cloud unless you want them to.
Data Prep & Classic ML
Clean datasets, feature engineering, baseline models, evaluation, and iteration. We work in Python (pandas, scikit-learn, Jupyter) and deliver explainable models with clear metrics and documentation.
Command Translation & Assistant UX
Natural language interfaces for internal tools, dashboards, and workflows. We build systems that translate user intent to actions safely, with preview and rollback capabilities built in.
Web-Based AI Features
Integrate AI into Next.js/TypeScript web apps: smart search, content generation, personalized recommendations, and chat interfaces. We handle both frontend UX and backend API design.
Strategy & Architecture
Not sure where to start? We help scope AI projects, choose appropriate models, estimate costs, and design architectures that balance performance, privacy, and maintainability.
Where this fits
AI engineering isn't one-size-fits-all. Here's where our approach works best.
SaaS Founders Adding AI Features
You have a working product and want to add AI without rewriting everything or locking into expensive third-party APIs. We integrate models that fit your stack, optimize for cost and latency, and make sure the UX feels natural rather than bolted-on.
Examples: Smart search, content suggestions, auto-tagging, personalized recommendations, natural language queries.
Internal Tools for Companies
Your team uses spreadsheets, CLIs, and manual workflows that could be streamlined with AI. We build internal-facing tools: command translators, data validators, document analyzers, and assistant-style interfaces that speed up operations without exposing data externally.
Examples: SQL query generators, report summarizers, document classifiers, workflow automation assistants.
Privacy-Focused AI Workflows
Your users care about privacy, or your industry has strict compliance requirements. We prioritize local-first and on-device AI wherever possible, designing systems where data never leaves the user's control unless explicitly required.
Examples: Healthcare apps, financial tools, personal productivity apps, on-premise enterprise solutions.
Mobile-First AI Experiences
You're building for iOS and want to leverage Apple Intelligence, Foundation Models, or custom CoreML integrations. We design AI features that feel native to the platform: fast, offline-capable, and respectful of battery life and user experience.
Examples: Smart photo organization, voice command systems, contextual suggestions, health data analysis.
Tech stack
We work across the Apple ecosystem, modern web frameworks, and standard ML tooling. The goal is always production-grade systems, not academic exercises.
iOS & Apple Ecosystem
- •SwiftUI, SwiftData, Core Data + CloudKit
- •Apple Intelligence, Foundation Models (on-device LLMs)
- •Core ML, Create ML, Natural Language framework
- •HealthKit, Vision, Speech frameworks
- •Combine, async/await for reactive data flows
Web & Frontend
- •Next.js 14+, React, TypeScript
- •Tailwind CSS for UI, Vercel for deployment
- •Server Actions, Edge Functions, API routes
- •Streaming responses, real-time updates
- •OpenAI API, Anthropic Claude, local LLM endpoints
Python & ML
- •Python 3.x, Jupyter Notebooks for exploration
- •pandas, NumPy for data manipulation
- •scikit-learn for classic ML (classification, regression, clustering)
- •Flask, FastAPI for model serving and APIs
- •Evaluation frameworks: precision, recall, F1, ROC curves
Tools & Infrastructure
- •Git, GitHub, CI/CD pipelines
- •Docker for reproducible environments
- •PostgreSQL, SQLite, Redis for data storage
- •Monitoring, logging, error tracking
- •Documentation-first approach for handoffs
How we work
We keep engagements focused, collaborative, and transparent—no surprises, no scope creep.
Scope
30–45 minute free call to understand your problem, stack, constraints, and goals. We clarify what’s possible, realistic, and worth building.
Build
Implement a focused feature or integration using your existing stack. Clear milestones, regular updates, and working code—not vaporware.
Iterate
Refine, measure, document, and hand off with clear next steps. You get working systems and the knowledge to maintain them.
Have a real problem, not a demo? That's where we're useful.
We work with SaaS founders, product teams, and companies looking to integrate AI into real workflows. Whether you need a single feature shipped or ongoing consulting, let's scope it out.
Typical engagement:
We start with a scoping call (free, 30–45 minutes) to understand your problem, tech stack, and constraints. From there, we propose a fixed-scope project or ongoing consulting arrangement. Clear communication, no surprises, and systems built to last.