Services

Four ways we take what you’re building to production.

For each: what it is, what actually changes hands, and how the engagement runs.

We are a small studio. The people who scope the work are the people who build it.

A workflow that an AI agent should be doing for you Agentic AI systems

A product to build: the app, the backend, the dashboard Product development

Data that cannot leave your own infrastructure Private & local AI deployment

A decision to make before anyone writes code AI consulting

Agentic AI systems

Multi-step agents that hold state, call tools, and stop for a person before anything consequential. Built on LangGraph, with MCP where the agent needs a defined way into your systems.

What's delivered

Agent graph and state model: steps, branches, what persists
Tool layer: MCP servers or API adapters, with schemas and permissions
Human checkpoints on the steps you decide are consequential
An evaluation set of real cases, with per-run traces
Docker images, deployment, runbook, and a walkthrough with your engineers

How the engagement runs

Fixed scope. Architecture first, then a working slice before the full build. Retainer optional, never assumed.

Read the detail on agentic AI systems

Private & local AI deployment

Open-weight models (Llama, Qwen, Kimi, DeepSeek) on infrastructure you control: on-prem, in your VPC, or air-gapped. The same discipline covers image and video generation, and personal assistants such as OpenClaw and Hermes.

What's delivered

Model selection, benchmarked on your data rather than a leaderboard
Deployment architecture: serving stack, hardware sizing, network boundary
Guardrails: input and output policy, rate limits, audit log
Retrieval over your own documents
Handover: runbooks, an upgrade path, your team operating it without us

How the engagement runs

Fixed scope. An evaluation on your data first, so the decision is made on evidence.

Read the detail on private AI deployment

Product development

The customer app (web or mobile), the custom backend, and the dashboard your team runs it from, built as one product. Rapid MVPs are a strength: we cut scope, never corners, and size the rest after week one.

What's delivered

Interface and interaction design, not a wrapper around a prompt
Web application build, and mobile (Flutter) where the product needs it
API layer, authentication, and the boundary between product and model
CI and containerised deployment on infrastructure you own
Source, infrastructure definitions and documentation handed over

How the engagement runs

Fixed scope for the first release. Retainer where the product keeps moving.

AI consulting

Architecture reviews, build-versus-buy decisions, technical audits. A second opinion from people who have built the thing, not a slide deck about it.

What's delivered

Architecture review with written findings and the reasoning shown
Build-versus-buy recommendation, including the case for buying
Audit of an existing agent or retrieval system: where it fails and why
A prioritised plan your own team can execute without us

How the engagement runs

Advisory. A short review or a set of sessions, never stretched into a retainer.

The stack

The tools we actually build on.

Named, so you can judge them. If yours already works, we build on it.

LangGraph: Agent orchestration: graphs, persisted state, human interrupts.
RAG pipelines: Retrieval over your documents, with an evaluation set to prove it.
MCP: One defined interface between an agent and your tools.
Local LLMs: Llama, Qwen, Kimi and DeepSeek on hardware you control.
FastAPI: The Python service layer between models and product.
Flutter: One codebase for iOS and Android.
Docker: The image we run is the image you run.
Computer vision & ML: Systems that read images and video; models trained on your data.
Data analytics: Pipelines, dashboards, and the honest caveats.
Generative media: Image and video generation, deployed privately.
Personal AI assistants: OpenClaw and Hermes on your own cloud.
Cloud & infrastructure: AWS, Azure and Google Cloud: built and managed.

Process

Want the day-by-day version? See our process.

How an engagement runs

Tell us what you're building.

A short description of the problem is enough to start. If AI is the wrong tool for it, we will say so in the first reply.

Start a project hello@visionarchitech.com