A studio for building custom AI agents. We design multi-agent systems tailored to your workflow, so you own the solution instead of renting one. Small team, senior operators, applied AI plus growth engineering under one roof.

Why a studio, not a subscription?

Subscriptions bill you every month whether you use them or not, and they lock your data into someone else's product. We build once, ship a system that fits your team, and charge for the work. You keep the code, the data, and the control.

Who owns the data and the code?

You do. Every build runs in your environment, on your keys, against your data. We ship the system and step out. No hidden telemetry, no vendor lock-in, no forced migrations if your priorities change.

How does multi-agent design work?

We compose small, focused agents with narrow jobs, explicit tools, and clear handoffs. That beats one giant prompt: cheaper to run, easier to debug, and safer to change one piece without breaking the rest. Each agent is testable on its own.

How do you price the build?

Like an expert plumber. You pay for the work when you need it, fixed scope, fixed price, agreed up front. No surprise fees, no per-seat inflation, no auto-renew you forgot to cancel. When your needs change, we scope the next piece the same way.

What about ongoing AI costs?

Two options, both agreed up front. If you'd like us to run the AI ourselves, we quote a fixed monthly under an agreement. Or bring your own keys (OpenAI, Anthropic, Google) and pay providers directly while we charge only for the build. No surprise fees either way.

How is low-confidence content handled?

Rows below a configurable confidence threshold enter a human review queue; approved corrections feed back into the memory layer for future runs.

Marketplace Content Ops — Playbook | baachee.ai

01 / The problem

Where teams get stuck

01Every seller writes titles and categories differently.

02Manual moderation queues slow onboarding to a crawl.

03Analytics on catalog quality never happen because SQL is a blocker.

02 / The outcome

What ships instead

Automatic normalization with confidence-based review queues.

Consistent taxonomy across seller cohorts.

Plain-language catalog analytics via Text2Report.

03 / The stack

Products in this playbook

Product

Text Management

Classifies and normalizes seller text.

Explore →

Product

Text2Report

Ad-hoc catalog analytics in plain English.

Explore →

Product

Canvas

Custom moderation workflows per marketplace.

Explore →

04 / By the numbers

Metrics teams report

Onboarding throughput

thousands/hour

Manual review

confidence-gated

Analytics

self-serve NL

05 / Questions

Frequently asked

: Rows below a configurable confidence threshold enter a human review queue; approved corrections feed back into the memory layer for future runs.

Next playbook