AI engineering as a service
Backend engineering for teams adding LLM features without an ML team.
Each one starts with what is broken.
A thirty-minute call, no deck. If it is not a fit we will say so and point you somewhere better.
01Strategy and advisory
you
Marketing has a chatbot. Support tried a vendor. The board wants to know if the company is behind. Nobody owns the answer, so every pilot gets a little money and none gets a decision.
codehive
We work out where AI pays off in your business and put a number on each case: build or buy, which vendor, what can go wrong. Then a roadmap in the order it has to happen. Data first, then people, then the change itself.
02Application development
you
Your team answers the same questions by hand, from PDFs, Confluence and the one person who remembers. The last AI pilot was a demo nobody dared put in front of a customer.
codehive
Support agents and chatbots that cite the handbook instead of guessing. Agents that transcribe meetings, do research or find prospects. MCP servers so your tools can call each other. Built on whatever framework fits your stack, with an eval set so you know when it breaks.
03Model work
you
The API bill grows every month. Legal will not let customer data leave the EU. Nobody can say whether the newest model is better for your task, so the prompt gets another edit.
codehive
We benchmark candidate models on your own eval set, the same cases for every model. When compliance needs it, we run open models on your infrastructure. When a prompt is not enough, we curate a dataset and fine-tune: an open model with QLoRA, or a hosted frontier model through its provider.
04Production and MLOps
you
The prototype is a notebook and a demo page. No tracing, no cost cap, no login, and a prompt injection would make the news. You cannot hand it to anyone.
codehive
Terraform environments that promote dev to test to prod on their own. Bedrock or self-hosted inference. Production RAG on managed vector storage. LangFuse tracing with a cost ceiling. JWT on the front door. And a migration path for the thing you already built.
05Enablement
you
One engineer understands the pipeline. The vendor who built the pilot has moved on. Your board learns about AI from the news instead of from you.
codehive
Training at every layer, from a board briefing to hands-on sessions for engineers. During the build we pair with your team, so the knowledge stays when we leave.
06Plain backend
you
The brief says "add AI". The actual problem is a report that takes a week, a sync job that fails every Sunday, or two systems that do not talk to each other. A well-built backend would fix it and nobody would need a model.
codehive
We build backend systems: APIs, data pipelines, integrations, and the queues and databases behind them. If that is what your problem needs, that is what we build, and we will say so before you pay for a model you do not need.