The migration stalled. The agent demo never shipped. The infrastructure was clicked together in a console and nobody wants to touch it. We're a small, senior team led by an architect with 15 years of building data platforms and running engineering teams. We work in your repos, review your pull requests, and leave things better documented than we found them.
If you've said one of these in standup lately, we should talk.
Our migration to GCP stalled halfway.
We pick it up where it stopped, finish the cutover, and leave runbooks your team can follow.
We need agents in production, not another demo.
We build them with evals, human approval steps, and a proper API layer to your systems, so they hold up on a Tuesday.
Our infrastructure was clicked together in the console.
We move it into Terraform, so every environment is reviewable and repeatable.
Security review is coming and we're not ready.
We tighten access controls, cloud configuration, and engineering process until it can take enterprise scrutiny.
The data platform works, but nobody trusts the numbers.
We rework ingestion and modeling in the warehouse, so there's one set of definitions and a clear path from source to dashboard.
We're adopting AI coding tools and getting chaos.
We set up the conventions, ticket-driven agent workflows, automated review, and CI checks that keep speed from turning into mess.
Five areas. Most engagements touch two or three.
No engineering team? See how we help, in plain English.
We design and build on Google Cloud, AWS, and Azure, with a bias toward infrastructure you can read in a pull request. Migrations, application modernization, CI/CD, and the unglamorous work that makes deploys boring. (DevOps for your product, that is. We don't do help desk or general IT support.)
Stack: GCP (Cloud Run, GKE, Pub/Sub, Cloud SQL, IAM/VPC), AWS, Azure, Terraform, Pulumi, Docker, CI/CD, serverless
We build web applications and APIs from the ground up, and integrate them with the legacy systems they have to live beside. We write tests, keep scope honest, and say so when a simpler option exists.
Stack: Node.js, React, Next.js, Python, GraphQL, PHP, PostgreSQL, MySQL, automated testing
Ingestion from many sources into a warehouse, modeled and put in front of the people who need it, including embedded in your own product. The goal is numbers people stop arguing about.
Stack: dbt, BigQuery, Snowflake, PostgreSQL, ELT, Tableau embedded analytics
Two jobs. Production agent systems: the agent, the API or MCP layer connecting it to your warehouse, CRM, and internal tools, human-in-the-loop approvals, and evals. And helping your engineers adopt AI tooling without lowering the bar: conventions, ticket-driven agent workflows, automated code review.
Stack: Gemini Enterprise Agent Platform (Vertex AI), Google ADK, Claude API, Claude Agent SDK, Claude Code, MCP
We harden the platform and the process around it: cloud configuration, access controls, and engineering practices, so the system holds up in a customer's security review.
Stack: GCP IAM/VPC, infrastructure as code, review and CI gates
We'd rather be a good teammate than a black box.
Your repo, your tickets.
We work in your version control and your tracker. No parallel universe to reconcile later.
Pull requests, reviewed.
We expect our code to be reviewed, and we'll review yours if you want us to.
CI from day one.
Tests and checks run on every change, so "works on my machine" never ships.
Docs as we go.
Decisions, runbooks, and diagrams get written while we build, not in the last week.
A clean handoff.
Your team should be able to run what we build without us. If they want to keep us around, that's their call.
Augment or own.
Join your team by the hour or on a retainer, or take a project start to finish. We'll tell you which we'd pick.
Client names stay private. We'd do the same for you.
The problem: Operational data lived in many separate systems, and every team worked from its own version of the numbers.
What we built: Multi-source ingestion, dbt models on BigQuery, and Terraform-provisioned GCP infrastructure. It now powers a business-operations and embedded-analytics platform used across every team.
Stack: dbt, BigQuery, Terraform, GCP, Tableau embedded analytics
The problem: Agent prototypes existed, but there was no safe way to connect them to the warehouse, CRM, and internal tools.
What we built: Agents on Google ADK and the Claude API, with custom API and MCP server layers to the client's systems. People approve consequential actions, and evals check behavior before and after every change.
Stack: Google ADK, Claude API, Claude Agent SDK, MCP, Vertex AI
The problem: The platform worked, but the cloud setup, access controls, and engineering process needed to withstand an enterprise customer's review.
What we built: We hardened the cloud infrastructure, tightened access controls, and formalized the engineering process.
Stack: GCP IAM/VPC, Terraform, CI
Got one of these on your board? Discovery calls are free.
Talk to an engineerA small team on purpose. You'll talk to the people doing the work.
CEO / Co-Founder
Hands-on architect. 15 years of building data platforms and leading engineering teams.
Developer
Describe it the way you'd explain it to a colleague. We'll tell you honestly whether we're a fit, and whether you need us at all.
Chicago, IL
