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Generative AI development services
Senior Brooklyn studio building generative AI on Claude and MCP. Fixed-fee discovery sprint, then a production slice in 2 to 4 weeks. Book a call.
Generative AI development services
We design and build generative AI systems for teams that want working software rather than a pilot that stalls. Rhode Labs is a small senior studio in Brooklyn. We specialize in Claude, the Claude Agent SDK, and the Model Context Protocol, and we ship a production slice in weeks instead of quarters.
Most generative AI projects fail at the same place. The demo looks great, then real data, real permissions, and real edge cases show up. We build for that second stage first. The result is a system your team can actually put in front of users.
What’s included
- A fixed-fee discovery sprint that turns a vague idea into a scoped build with a defined first slice.
- Architecture for retrieval, context, and tool access so the model answers from your data instead of guessing.
- Custom generative AI features such as assistants, internal copilots, document and data workflows, structured extraction, and drafting and review tools.
- AI agents built on the Claude Agent SDK when a workflow needs the model to act rather than only write.
- MCP integrations that connect Claude to your existing systems (databases, document stores, internal APIs, 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: which generative AI approach fits your problem
Teams often ask for fine-tuning when they need retrieval, or for a chatbot when they need an agent. This table is the first filter we use in a discovery sprint. It maps the symptom to the approach that usually fits.
| If your problem looks like this | The usual fit | Why |
|---|---|---|
| ”The model needs to answer from our docs, policies, or records” | Retrieval (RAG) with good context engineering | Keeps answers current, cites sources, no retraining when data changes |
| ”It needs to do something: update a ticket, run a query, send a message” | Agent on the Claude Agent SDK with tools via MCP | The model plans and acts through defined tools, with guardrails |
| ”We need one rigid output format across millions of calls” | Fine-tuning or a constrained schema | Cheaper per call at scale, predictable structure |
| ”Connect Claude to five internal systems without five custom builds” | MCP integrations | One protocol for tools and data, less glue code to maintain |
| ”We tried a prompt and quality is inconsistent” | Evaluation harness before more building | You cannot improve what you cannot measure |
| ”Leadership wants proof before a big commitment” | Thin production slice on real data | One real workflow, shipped, so the decision rests on evidence |
Most engagements combine retrieval, tools, and a small amount of fine-tuning at the edges. The point of the table is to start from the problem instead of from a technology someone read about.
How an engagement works
Our process has four steps, and the first useful output arrives quickly.
- Discovery sprint (fixed fee, about one week). We map the workflow, the data, the permissions, and the failure modes. You get a written architecture, a scoped first slice, and an honest read on what generative AI will and will not do here. This step stands on its own. If the project should not proceed, you will know.
- 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.
- Deploy a thin slice (2 to 4 weeks). We build one real workflow end to end, running on your real data, with evaluation in place. This is production-grade work rather than 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.
Where it pays off
Generative AI development earns its cost when the work is repetitive, language-heavy, or buried in systems people hate searching.
- 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 being 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 rather than build something you do not need.
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 generative 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 generative AI idea and want a clear read on whether it works 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.