Full-Stack AI Products
AI Applications
Full-stack AI application development from idea to production — NLP, computer vision, recommendations, and generative AI, wrapped in software people actually want to use.
An AI application is more than a model behind an endpoint. It's product design, data pipelines, a real user interface, and the harness around the model — evaluation, guardrails, retries, and monitoring — that makes it reliable enough to put in front of users. We build the whole thing.
Our sweet spot is taking an idea — often a rough one — and turning it into a working product: scoping what the AI genuinely can and can't do, prototyping quickly to prove value, then hardening into production software.
We're honest in both directions. If a problem doesn't need machine learning, we'll say so and build the simpler thing. If it does, we'll build it with the evaluation rigor that separates a demo from a product.
Capabilities
Generative AI products
Applications built on large language models — drafting, summarization, search, and assistants — with grounding and evaluation built in.
NLP & computer vision
Classification, extraction, semantic search, and document understanding over text — plus image classification, object detection, and visual extraction for operational workflows.
Self-hosted & open-weight models
Llama- and Mistral-family models served with vLLM or Ollama on your own GPUs, fine-tuned and quantized where it cuts inference costs and keeps data in-house. We handle the GPU provisioning, scheduling, and serving stack.
Recommendation & personalization
Content and product recommendation systems that learn from behavior while respecting privacy constraints.
Product design & full-stack build
UX design, React/Next.js front ends, robust APIs, and cloud deployment — the complete product, not just the model.
Evaluation & continuous improvement
Eval-driven development: quality benchmarks defined up front, every change measured against them, and production feedback loops so the application demonstrably improves after launch.
Typical use cases
- A customer-facing product with AI at the core of its value proposition
- Document intelligence applications for contract, claim, or form-heavy operations
- Semantic search and discovery over large content libraries
- Internal tools that put generative AI safely in employees' hands
Our approach
- 1
We prototype against real data early — the fastest way to learn whether the AI can deliver the experience the product needs.
- 2
We define quality metrics before building: what does a good answer look like, and how will we know we're shipping it?
- 3
We build the product around the model's real capabilities, designing graceful behavior for the cases where the AI is uncertain.
Every engagement follows our four-phase process — discover, design, build, and optimize. See how we work.
Technologies we commonly use
FAQ
We have an idea but aren't sure AI can do it. Can you tell us?
Yes — that's what our discovery phase is for. We prototype against your real data quickly, so you learn whether the idea holds up before committing to a full build.
Do you build custom models or use existing ones?
We default to the strongest available foundation models and fine-tune only when the problem demands it. Often the winning move is fine-tuning a smaller open-weight model on your task: it can match a frontier model on narrow work while running cheaper, faster, and inside your own infrastructure. Custom training from scratch is a cost you should incur for a reason, not by default.
Why self-host a model instead of calling an API?
Three reasons: cost at volume, since serving an open-weight model with vLLM on your own or rented GPUs avoids per-token pricing; privacy, since prompts and data never leave your infrastructure; and control over latency, versioning, and availability. We handle the GPU provisioning and serving stack, and we'll tell you honestly when a hosted API is the better fit.
What does 'production-ready' mean for an AI app?
Measured quality against defined benchmarks, monitoring and alerting, graceful handling of uncertain outputs, security review, and infrastructure your team can operate. We build to that bar, not to demo quality.
Ready to talk about ai applications?
Tell us where you are and where you want to go. We'll come back with a candid read on what's possible and a concrete path to get there.