MaxtDesignAI Studios

AI Studios · Deployments

Custom AI tools, built right the first time.

Production-grade RAG, agents, and copilots, with evaluation harnesses so you can prove the system works, runbooks so your team can operate it, and a clean exit so you\'re not paying us to maintain something we built. We design ourselves out of the dependency from day one.

What we build

Four categories cover most of the work

Most engagements live in one of these four shapes. If your use case doesn\'t fit, talk to us anyway. Edge cases are where the interesting work lives.

RAG systems

Retrieval-augmented question answering over your documentation, knowledge base, contracts, or code. With evaluation harnesses so accuracy is provable, not asserted.

Agent workflows

Multi-step agents that read, decide, and act across your tools. Every step is logged and reviewable; every action goes through a permission policy you control.

Internal copilots

Copilots calibrated to your team's specific work: code review, contract analysis, customer-support drafting, internal knowledge retrieval. Voice and tone tuned.

Evaluation pipelines

Automated test suites for AI features. Regression detection on prompt changes, model upgrades, and data drift. The boring infrastructure that keeps quality from sliding silently.

How it works

Five phases, two-week sprints

The engagement shape is fixed. Duration scales with scope. A focused RAG system might land in six weeks; a multi-agent workflow with a custom eval suite might run twelve.

  1. 01

    Discovery

    A short scoping engagement: which use case, which data, which tools, which constraints. We don't start writing code until we're aligned on what success looks like.

  2. 02

    Architecture

    Reference architecture for the build, including model choice, retrieval design (if any), tool surface, evaluation strategy, and deployment target. Reviewed before implementation.

  3. 03

    Implementation

    Iterative builds in two-week sprints. You see the system working at the end of every sprint. Real data, not toy data, from sprint two onward.

  4. 04

    Evaluation

    Before handoff, we ship a test suite that runs against your live system on every model change. You'll know, by metric, when the system is regressing.

  5. 05

    Handoff

    Documentation, runbooks, and a knowledge-transfer session with the team that will own it. Optional retainer for the first 90 days; no lock-in beyond that.

The stack we reach for

Pragmatic, model-agnostic, eval-first

We pick tools per-engagement based on data sensitivity, latency budget, and your existing infrastructure. No religious commitment to any single vendor.

Anthropic Claude (Sonnet, Haiku, Opus)
OpenAI GPT-4-class models
Open-source models (Llama, Mistral) for on-prem
MCP for model-context plumbing
Vercel + Next.js for deployment
PostgreSQL + pgvector for retrieval
Anthropic Managed Agents for stateful workflows
Custom eval harnesses (LangSmith, Promptfoo, in-house)

Have a use case in mind?

A 30-minute scoping call confirms whether what you want to build is actually the right thing to build. We\'re happy to talk you out of an engagement that wouldn\'t serve you.