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Custom AI development
Senior Brooklyn studio for custom AI development on Claude, the Claude Agent SDK, and MCP. Fixed-fee discovery sprint, then a production slice in 2 to 4 weeks.
Custom AI development
We build custom AI systems for teams that want working software inside their own stack instead of a generic chatbot bolted on the side. Rhode Labs is a small senior studio in Brooklyn, built in Brooklyn and deployed everywhere. We specialize in Claude, the Claude Agent SDK, and the Model Context Protocol (MCP), and we ship a production slice in weeks instead of quarters.
Custom AI development means the system is shaped around your workflow, your data, and your access rules, rather than the assumptions baked into an off-the-shelf product. Most agencies in this space run a wide, model-neutral menu. Our edge is narrow and deep: we know Claude and MCP well enough to design around their real strengths and limits this quarter.
What’s included
- A fixed-fee discovery sprint that turns a vague “could we use AI for this” into a scoped build with a defined first slice.
- A build-versus-buy call before any code, so you do not pay to rebuild something a tool already does well.
- Architecture for retrieval and context so the model answers from your data instead of guessing.
- Custom AI features: internal assistants, copilots, document and data workflows, structured extraction, drafting and review tools.
- AI agents on the Claude Agent SDK when a workflow needs the model to take actions, not only write.
- MCP integrations that connect Claude to your databases, document stores, internal APIs, and tools like Slack and GitHub through one open standard instead of one-off connectors.
- An evaluation setup so you can measure quality and catch regressions instead of guessing whether a prompt change helped.
- Deployment, observability, and a handoff your engineers can maintain.
A decision table: should you build custom AI, or buy?
Most custom AI projects go wrong before any code is written, because the choice to build was never tested. This is the first filter we apply in a discovery sprint. It maps your situation to whether custom development earns its cost.
| If your situation looks like this | The usual call | Why |
|---|---|---|
| A SaaS tool already covers the workflow with minor friction | Buy, then maybe customize at the edges | Rebuilding a solved problem rarely pays back |
| The data and actions are specific to your business and systems | Build custom | No vendor will model your process for you |
| You need the model to act inside your tools, with your permissions | Build on the Claude Agent SDK, tools via MCP | The model plans and acts through defined tools, with guardrails |
| You want to connect Claude to several internal systems | MCP integrations | One protocol for tools and data, less glue code to maintain over time |
| Off-the-shelf agent assumptions keep fighting your workflow | Build custom | A custom agent has no conventions baked in by someone else |
| Quality is inconsistent and nobody can say why | Evaluation harness before more building | You cannot improve what you cannot measure |
| Leadership wants proof before a large commitment | Thin production slice on real data | One real workflow, shipped, so the decision rests on evidence |
The honest answer for some rows is “buy.” We would rather say that in week one than build something you did not need. Off-the-shelf agents are fast to start, yet they tend to assume tool formats and workflows that may not match yours, which is exactly where custom work earns back its cost.
How an engagement works
The process has four steps, and the first useful output arrives fast.
- Discovery sprint (fixed fee, about one week). We map the workflow, the data, the permissions, and the failure modes, and we make the build-versus-buy call. You get a written architecture, a scoped first slice, and a straight read on what custom AI will and will not do here. This step stands on its own.
- Architect. We design retrieval, tool interfaces, evaluation, and how the system fits your stack and security model. MCP is usually the integration layer, since it gives Claude a standard way to reach your tools. The agent loop, when there is one, runs on the Claude Agent SDK.
- Deploy a thin slice (2 to 4 weeks). We build one real workflow end to end, on your real data, with evaluation in place. This is production-grade work, not a sandbox demo.
- Iterate and expand. Once the slice proves out, we widen scope: more workflows, more integrations, and more autonomy where it earns its place. Each expansion is scoped on its own, and you can stop after any phase.
Where it pays off
Custom AI development earns its cost when the work is repetitive, language-heavy, or buried in systems people dislike searching, and when no off-the-shelf product fits the way your team actually works.
- Internal knowledge assistants that answer from your real documents and link back to the source.
- Drafting and review tools for support replies, contracts, reports, or code.
- Structured extraction that turns messy documents and emails into clean records.
- Agents that read across your tools and take routine actions, with a human in the loop where it matters.
- Team-wide Claude deployments, so the model has the right context and permissions instead of a blank chat window.
It pays off less when the task is purely deterministic, when you have no usable data, or when an existing rules engine already does the job. We will say so in discovery.
Pricing and engagement
Every engagement starts with a fixed-fee discovery sprint, so you can commit a small, known amount before any large decision. Builds are scoped per project once discovery defines the first slice and the architecture. We do not quote a custom AI build sight unseen, because the honest number depends on your data, your systems, and what “good” has to mean for your users.
Start with a discovery sprint
If you have a custom AI idea and want a clear read on whether to build it and what it takes to ship, a discovery sprint is the fastest way to find out. You leave with an architecture and a scoped first slice, whether or not you build with us.