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 does Text2Report reach 92% accuracy?

A six-layer context engine combines schema, validated query patterns, business rules, self-learned corrections, conversation memory, and MCP tool access.

Can I query PostgreSQL and BigQuery in one conversation?

Yes. Both databases are queried in the same thread so operational and marketing data can be combined without manual ETL.

Analytics Team — Playbook | baachee.ai

01 / The problem

Where teams get stuck

01Ad-hoc requests from every function drown the analytics queue.

02Combining operational (PostgreSQL) and marketing (BigQuery) data needs pipelines nobody has time to build.

03Reports get stale between refreshes.

02 / The outcome

What ships instead

Stakeholders self-serve in plain language for common questions.

Cross-database queries in one conversation — no manual ETL.

92% accuracy after learning phase; corrections improve future runs.

03 / The stack

Products in this playbook

Product

Text2Report

NL-to-SQL with schema auto-discovery.

Explore →

Product

Canvas

Chain queries, LLM calls, and tools visually.

Explore →

Product

Cashflow

Financial layer for founder-visible metrics.

Explore →

04 / By the numbers

Metrics teams report

Text2Report accuracy

0% after learning

Data sources

PostgreSQL + BigQuery

Setup time

minutes (auto-discovery)

05 / Questions

Frequently asked

: A six-layer context engine combines schema, validated query patterns, business rules, self-learned corrections, conversation memory, and MCP tool access.

Next playbook