Services
Each engagement below has a defined scope, a defined deliverable, and ends with something running in production. Book a call today.

Agents that survive contact with your team and customers.
We set up AI Agents that automate workflows costing your team hours every week — depending on requirements we can also perform evaluation, implement guardrails, and set up the observability needed for rock solid production deployments.

Answers from your data, not the open internet.
Private knowledge bases grounded in your company documents, tickets, and internal wikis. Your team asks questions in plain language and gets sourced answers, without your data leaving infrastructure you control.

From scoping to revenue-generating product.
A complete web application built on Next.js and/or FastAPI hosted locally or in the cloud. The same engagement that produced the SAIV tool — delivered, deployed, and handed over with documentation your team can build on.

Publish content with less manual effort.
AI Automation that turns your media into revenue. Discovery call will determine whether this service fits your revenue strategy and timeline.

Find out what is actually worth automating.
A fixed-scope engagement that maps your workflows, identifies the highest-ROI automation opportunities, and returns a prioritized roadmap with effort and impact estimates — so you spend budget on the right build.

Level up the people you already have.
Hands-on training that takes you or your team from experimenting with AI to value creation. Live sessions built around your schedule.
See it working
Walkthroughs of these systems running for real — architecture, tradeoffs, and the parts that usually break.
The full walkthrough of how we architect agents that hold up under real usage.
A content automation pipeline running end to end, from source material to published post.
How the private knowledge assistant retrieves and cites answers from internal documents.
Agents driving existing tools directly, replacing manual click-through work.
The evaluation and guardrail layer that separates a demo from a production system.
The full automated video pipeline, from script generation through final render.
A full-length session on the agent framework and observability stack we build on.
The ground-up introduction to multi-agent systems and how they get monitored.
Scaffolding a real agent project from the command line, start to finish.
Choosing the right orchestration shape for the workflow you are automating.
The local-first agent stack for teams that need models running on their own hardware.
Standing up the runtime that self-hosted agent deployments are built on.
Background on the agent frameworks shaping how these systems get designed.
An autonomous coding loop running unattended — agents doing sustained real work.
The evaluation technique behind the guardrails on every production build.
Putting models to work on the engineering overhead that slows delivery down.
How retrieval-augmented generation grounds answers in your own documents.
Wiring a vector database through a complete application, front to back.
Running retrieval entirely on infrastructure you control.
Local model hosting — the foundation for knowledge bases that never send data out.
Proof of how far private, self-hosted models can go on modest hardware.
The deployment path behind our Python-backed application builds.
The CI/CD pipeline that ships every project we hand over.
The serverless runtime we deploy client applications onto.
Scheduled work in production — the unglamorous layer that keeps systems running.
Reproducible environments, so the handover works on your team's machines too.
Wiring a model into a real web backend rather than a notebook.
One of the API layers we reach for when data modeling gets complex.
Custom data visualization work inside a production front end.
Internationalizing a React front end — one of the details that separates a shipped product from a demo.
The AI-assisted build process, shown end to end on a real site.
A complete content pipeline driven from a spreadsheet-style backend.
The fast version of the content automation build.
The building blocks — audio, video, and voice — behind automated video output.
Agents generating original audio, applied to creative production work.
A long-form look at what AI-generated media can produce at album scale.
The kind of model tradeoff analysis a readiness audit produces.
Foundational background on the framework underneath modern AI systems.
First-principles teaching from the training curriculum.