You
Pull the latest report on the deal: financials, and where it sits in the workflow.
You bought the seats. The warehouse, wiki, and SaaS still don't talk.
The problem is there's no knowledge base the model can use.
- Data warehouseAnalytical truth, reporting, and metrics
- Files / wiki / decksSharePoint, Notion, presentations, SOPs
- External SaaSVendor systems that own workflows and records
- Internal toolsHomegrown apps, Excel models, scripts
The operating stack is there. The rooms still don't talk.
How do you get the warehouse and the wiki to agree today?
The files, the board, and the books.
Three Layer Operating System
Looks like you already run this. You keep a record, you move this week's work, and you report the numbers. We connect those three so a request can read them and write the answer back.
Durable
The files
Your raw data stays put. Agents organize and refine data artifacts to prepare it for delivery across your organization.
Ephemeral
The board
Your operational swimlanes. Your Agentic PM will manage this board for you and make sure work stays in flight and gets done.
Logical
The books
The output layer. Reporting and the value-adds your business runs on.
Diagram
We map your existing data warehouse, wiki/files, SaaS.
Connect
Wire an orchestrator to the layers that already work. Prefer integration over rip-and-replace.
Record
Give AI governed access, especially wiki and warehouse, so answers land in durable systems.
Sustain
Hand off cleanly and stay available. The knowledge layer should outlast the engagement.
AI comes in many generic forms. It can untangle your specific problems.
Two-way agentic workflows push and pull to your three layer operating system.
- EphemeralBoardRead request + ticket context
- DurableKnowledge baseLoad deal memo
- LogicalWarehouseQuery financials
- LogicalWorkflow SaaSRead stage + owner
- EphemeralBoardWrite status comment
- DurableKnowledge baseArchive pointer on the memo
Services
Platform Architecture & Delivery
Enterprise and investment-management platforms: requirements, configuration, integrations, UAT, go-live, and support. We take the roadmap and trade-off calls.
AI-Accelerated Engineering
You cannot delegate understanding. The board has to read a knowledge base, and the result has to write back.
Cloud & Infrastructure
Cloud migration, architecture, and infrastructure modernization. Security hardening and compliance go into the design.
Who you'll work with.
Christopher Harris
Platform Architecture · Delivery
Senior platform architect with 11+ years inside enterprise and investment-management systems: requirements through UAT, go-live, and the support that follows. Designs data architectures, stitches front- and back-office flows, and unblocks the pipeline issues that stall delivery.
0
Years in platforms
Platforms
Primary domain
Full lifecycle
Delivery span
Integrations
Core strength
Austin Harris
Distributed System Design · Architectural Engineering
A one-man engineering force with 10+ years across platform architecture, backend cloud scaling, and cross-platform apps: healthcare CRM, GPS tracking, security, startups, and R&D. When the brief is ambitious and the team is lean, he ships it.
0
Years building
Zero-to-ship
Engagement style
AI-accelerated
Delivery method
Cloud & scale
Core strength
FAQ
Who does the work, how we start, and how we sit with your team.
Seats get bought for search and coding assistants while the warehouse, wiki, SaaS, and internal tools stay siloed. We start by diagramming those layers and connecting an orchestrator so AI can read and write business context.
The principals. There is no junior bench and no mid-engagement hand-off. The people in the discovery calls are the same people writing the architecture, shipping the code, and staying through go-live.
Platform implementations, AI-accelerated engineering on a contextual knowledge layer, and cloud/infrastructure modernization. We take the work where senior judgment and end-to-end ownership matter more than headcount. Strategy and security show up inside those engagements.
The tooling an agent needs to execute: a durable knowledge base with importance pointers, then an ephemeral workflow that reads that knowledge base when someone files a feature, bug, or report. Tickets can archive into the durable knowledge base or stay in the backlog. Once that flywheel is running, AI can deliver work and unblock release.
A short consult on goals, constraints, and the systems you have today. From there we propose scope, milestones, and trade-offs, then execute alongside your team.
Yes. That's the preferred model. We embed with your people and leave systems and documentation your team can own after we step back.
Send a note. We respond within one business day.