CB’S DEVELOPMENTCB’S DEVELOPMENT SERVICES · CHASE BROWN
Senior engineering for established organizations whose critical work still runs on spreadsheets, inboxes and systems that don’t talk to each other.
Talk through your next bottleneckConnected systems, automated processes, trusted reporting and practical AI, for operations, finance, IT and engineering leaders.
01 / WHAT WE SOLVE
If one of these sounds familiar,
it’s worth a conversation.
Most established organizations don’t need another platform. They need the systems they already pay for to work together, and the processes in between to stop depending on workarounds and heroics.
Critical processes run on spreadsheets, inboxes and handoffs that only one or two people understand. We map the process and turn the repeatable parts into a documented, monitored workflow, with approvals where judgment matters.
Your ERP, CRM, finance, ticketing and line-of-business systems don’t talk, so people re-key data and reconcile by hand. We connect them so every record has one owner, bad data is caught before it spreads and someone hears about it when something fails.
Your IT and engineering teams are busy keeping the lights on. We add senior capacity that works within your standards and change-management process, and delivers the project they haven’t had time for.
Aging applications and fragile workarounds that nobody fully understands. We work out what they really do, add safeguards and monitoring, then stabilize, modernize or replace them in stages instead of one risky cutover.
A demo is easy; everyday use is not. We take AI from pilot to daily work with clear limits on what data it can see, checks against your real cases, people approving what matters and a setup your security team can sign off. I spent two years in information-security operations before moving into AI engineering, so that conversation is familiar ground.
See the seven stages ↓︎Reports are assembled by hand and out of date by the time they reach the meeting. We bring operational data into one trusted place that stays current on its own, with dashboards and alerts when something needs attention. I’ve built an agent for exactly this: it handles the data transformations and creates the dashboards.
02 / AI ADOPTION
Most teams shop for stage seven
while standing on stage one.
Adopting AI isn’t a purchase. It’s a sequence, and each stage is what makes the next one safe. We assess where you are today, then build the stage that unlocks the rest, inside your environment and under your controls. This is my day job too: as a head of engineering, I own the architecture for production AI systems spanning retrieval, agent workflows, model serving and evaluation.
FOUNDATIONS01–03
OPERATIONS04–05
ORCHESTRATION06–07
01FOUNDATIONS / Tool adoption
Teams pick up assistants and automations on their own. Each one works only while its owner is at the keyboard: close the laptop and the workflow stops; when someone goes on vacation, their automations go with them.
02FOUNDATIONS / Context layer
Every department gets a structured map of what it knows, served through a defined interface. When sales needs something from engineering, it asks through that interface instead of reaching into engineering’s data. Clear boundaries keep agents grounded in the right domain instead of guessing across departments.
03FOUNDATIONS / Harness engineering
Before picking a model, define what a good result looks like, how work gets routed and which checks it must pass. That harness is the asset you own and invest in. The model underneath should be interchangeable: if swapping it breaks everything, you built a dependency, not a platform.
I’ve built a provider-agnostic model layer that let internal products adopt or change models with a one-line update.
04OPERATIONS / Headless
Every automation moves to hosted infrastructure, where it runs on a schedule or when a condition is met, with nobody logged in. This is where most organizations stall: no one owns the move from a script on someone’s desktop to a service that runs on its own. It’s also where the earlier work starts to compound.
I’ve designed a serverless AWS ingestion pipeline that handled roughly a billion security events and several terabytes of data a day.
05OPERATIONS / Governance
Role-based access on every knowledge source from day one, and a set lifespan for every agent. A forgotten agent is still a live set of credentials. Once you run dozens or hundreds, lifecycle rules do the housekeeping: retire what hasn’t run in a quarter, and pull anything whose quality scores slip below the bar.
06ORCHESTRATION / Agent-to-agent
Hosted agents are linked along a value chain: when one finishes, its output becomes the next one’s input. A model-based quality gate reviews every handoff and stops anything that doesn’t meet the standard before it moves downstream.
07ORCHESTRATION / Orchestrated workflows
End-to-end workflows, partly or fully automated and coordinated step by step. Routine cases flow straight through; unusual ones go to the right person with the context already gathered. The business runs on agent output, and your people spend their time where judgment matters.
On my own team, the AI-assisted delivery tools I built brought 5–10x gains in commit and feature throughput, depending on the engineer, with review still in place.
A fixed-scope assessment places you on the map and names the next stage to build.
03 / HOW WE WORK
Start small.
Commit when it’s proven.
A defined look at one process or system: how it works today, where it breaks, what it costs and the options to fix it. You get a written recommendation and plan you can act on, with us or without us.
Scope, acceptance criteria and priorities are agreed up front. Work arrives in small pieces your team can see, test and approve, inside your environments and through your own approval processes.
Continued improvements and support if you want them. If not, documentation, runbooks and a walkthrough with your team mean you can run it without us.
No long statement of work before anyone understands the problem.
Each stage is agreed in writing before it starts.
04 / WHAT TO EXPECT
What you can count on,
in plain terms.
HubSpot
SAP
Google Workspace
Jira
Snowflake
Stripe
Okta
ADP
Zendesk
QuickBooks
Box
GitHub
Databricks
Shopify
Google Cloud
Zoom05 / WHO YOU’LL WORK WITH
I’m Chase Brown. I lead every engagement personally, from the first assessment to the handoff.
Today I’m a head of engineering, setting technical direction and owning the architecture for production AI. Before that I was the lead AI solutions engineer at KnowBe4, after two years on its information-security operations team, where I built a retrieval platform that reached over 95% accuracy across 3,000 internal documents.
I studied statistics at Texas A&M, hold four AWS certifications, including Solutions Architect Associate and Machine Learning Specialty, and took my first machine-learning contract in 2017.
That range matters when a problem crosses boundaries: an integration that touches finance data, an AI feature that has to satisfy your security team, an operational system that can’t go down.
START WITH THE PROBLEM
Tell me which process or system is holding things up, who it affects and what happens if it stays the way it is. If it’s a fit, the usual next step is a short call and a fixed-scope assessment.
Keep it high level: no passwords, personal data or confidential records. If the details are sensitive, say so and we can sign an NDA first.
A conversation with the engineer.
A clear, written scope.
A plan you can act on.