You want an AI feature in your product — a support agent that actually resolves tickets, search that understands what customers mean, a model that runs privately on your own infrastructure. Your engineers are good, but they haven't done this before. We have, many times.
We design, build, and ship custom AI systems: from a 4-week proof of concept on your data to a production system your team can run without us.
What We Build
AI Agents & LLM Applications
Custom agents that take real actions: customer support, internal operations, domain-specific copilots. Designed with guardrails and evaluation, not just a system prompt and hope.
RAG & Knowledge Systems
Retrieval-augmented generation over your documents, wikis, and databases. Assistants that answer from your knowledge — with sources — instead of hallucinating.
LLM Fine-Tuning & Private Models
Fine-tuned open-weight models for your task: cheaper, faster, and deployable in your VPC, on-premises, or on-device. Our specialty for privacy-sensitive domains like healthcare.
Voice AI
Speech-to-text, text-to-speech, and full voice agents. We've built voice model pipelines and mobile voice interfaces — see Voice Layer, our own product.
Recommendation Systems
Product, content, and service recommendations — collaborative filtering, content-based, hybrid. We've shipped these to millions of users.
Vision & Data Engineering
Image analysis and computer vision, plus the big-data foundations (Spark pipelines, feature stores) that make every other AI system possible.
AI POC Sprint ~4 weeks · fixed scope
The fastest honest answer to "will this work?". In about four weeks you get:
- A working prototype running on your data — not a slide about one
- An evaluation report: accuracy, latency, and cost measured properly
- A production plan: architecture, effort estimate, and what it takes to ship
If the idea doesn't survive contact with your data, you find out in week four — not month twelve.
Embedded AI Team month-to-month
No data science team? Senior AI engineers join yours — your repos, your standups, your roadmap. We build alongside your people so the capability stays when we hand over. No long lock-in.
Selected Work
- Healthcare: fine-tuned a privacy-preserving LLM for a medical software company — a working POC where patient data never leaves their infrastructure
- E-commerce: AI-powered product search and recommendation chatbot for a major online retailer, in production
- On-device AI: LLMs optimized to run on phones — the research behind our Simulum lab project
How It Works
1. Intro call
30 minutes, free. Bring the idea; we'll tell you if it's buildable, roughly what it takes, and what we'd try first.
2. POC Sprint
~4 weeks to a working prototype on your data, with real evaluation numbers and a production plan.
3. Production
We ship it — evaluation pipelines, monitoring, guardrails, cost control — and hand it over to your team, trained to run it.
Not sure which AI feature to build first? Start with an AI Opportunity Scan. Want your engineers to learn while we build? Add AI Training.
FAQ
Should we fine-tune a model or use RAG?
Usually RAG first: it's cheaper, faster to ship, and easier to keep current when your knowledge changes. Fine-tuning wins when you need a specific behavior, style, or format, when latency and cost per call must be low, or when data can't leave your infrastructure. Often the right answer is both. We've built systems each way and will show you the trade-offs on your actual data, not in the abstract.
Can the model run on our own infrastructure, fully private?
Yes — this is one of our specialties. We fine-tune and deploy open-weight models that run in your VPC, on-premises, or even on-device (we've put LLMs on phones). For healthcare, legal, and other privacy-sensitive domains, sensitive data never has to leave your environment.
What exactly does the AI POC Sprint deliver?
In about 4 weeks: a working prototype running on your data, an evaluation report with honest numbers on accuracy, latency, and cost, and a concrete plan (architecture and effort estimate) for taking it to production. If the POC shows the idea doesn't work, you learn that after 4 weeks — not after 12 months.
What's the difference between a POC and production, and do you do both?
A POC proves the approach works on your data. Production adds the boring, essential parts: evaluation pipelines, monitoring, guardrails, cost control, latency budgets, failure handling. We do both — and because we build POCs with production in mind, ours graduate instead of being rewritten from scratch.
Who owns the code, models, and data?
You do. All code, fine-tuned model weights, datasets, and documentation produced in the engagement are yours. We don't lock you into a proprietary platform, and we hand over everything needed to run it without us.
What technology stack do you work with?
Whatever fits the problem: commercial APIs (Anthropic, OpenAI, Google) or open-weight models (Llama, Mistral, Qwen and others) fine-tuned with LoRA or full training; PyTorch and Hugging Face for modeling; Spark for large-scale data; standard cloud infrastructure (AWS, GCP, Azure) or your own hardware. We're vendor-neutral and pick per project.
Can you join our existing engineering team instead of working separately?
Yes — that's the Embedded AI Team option. Senior AI engineers join your team, work in your repos and rituals, and build alongside your people so the knowledge stays when we leave. Month-to-month, no long lock-in.
Have something you want built?
Book a free 30-minute call. Bring the idea — we'll tell you on the spot if it's buildable and what we'd try first.
No pitch deck, no obligation. If AI is the wrong answer for your problem, we'll tell you that too.