Consulting — ABOS Architecture
Your AI agents work in demos.
They fail in production.
I find where unreliable agent behavior puts customers, margin, or delivery at risk, then install the system that makes failures visible before they become customer-facing incidents. AI is the fulfillment engine, not the product.
Not theory. I run an autonomous business operating system — a public registry of 16 interconnected projects spanning marketing, intelligence, publishing, and identity — built solo with AI in 27 months, starting from zero coding knowledge. Every claim on this page links to something you can inspect.
Inspect the proof first
Don't take the pitch — take the evidence. All of it is public.
Eight live systems — open any of them
Production sites, a member app, an events platform with a public API, and the factory they come out of. Every card is a URL that answered today, plus the shared rails underneath. No outcome claims — capability you can check yourself in a minute.
Live GitHub Metrics
Public proof snapshot: 16 registry projects, 10 marked deployed, and 16,091 commits as of July 13, 2026.
Case Studies
The AI Orchestra Method and Teneo systems, documented with architecture, tradeoffs, and verification evidence.
The Projects
16 registry projects, 10 marked deployed, and 16,091 commits as of July 13, 2026.
Independent Codebase Audit
March 2026: a Claude instance audited the Teneo production repo — 155+ Lambda functions, 7 domain stacks. Verdict: "needs hardening, not rebuilding."
What an ABOS Architect does
Most AI consulting teaches prompts. The actual bottleneck is whether an agent can fail without taking a customer, a deadline, or a team's trust with it. I diagnose where the business is exposed, decide what must stay under human control, and make the next failure visible early enough to fix.
I've built these safeguards across my own ecosystem: clear approval boundaries, reliable access to the information agents need, and early warning when behavior changes. Your agents guess because they cannot find what they need. We fix retrieval, which cuts both error rate and token spend. The first question I'll ask you is the one that matters: can you show me how you measure whether any of this actually works?
Engagements
ABOS Audit
Find the costly failure before a customer does: identify where agent behavior goes wrong, what it puts at risk, and the clearest path to fixing it.
- ·Find where failures reach customers instead of your team
- ·Measurement so failures surface before users do
- ·A concrete, prioritized fix plan
Agent Infrastructure Sprint
Turn an unreliable agent workflow into one your team can operate with confidence—without letting one bad run become a customer-facing incident.
- ·A shared operating plan your team can follow
- ·Approval boundaries that bound the blast radius: a retry, not a customer
- ·Measurement that catches regressions before users do
Strategic Advisory
For founders who want a clear view of where AI helps, where it adds risk, and what to fix next.
- ·Async-first: documents and working sessions, not status meetings
- ·Architecture reviews on real code, not slideware
- ·AI Orchestra Method training for you or your team
Good fit / bad fit
Worth a call if
- · You've raised and your agent pilots aren't becoming production systems
- · You need regressions to surface before users do, not another demo
- · You want your team faster with AI, without losing control of quality
- · You can handle direct communication and ship-first iteration
Not a fit if
- · You want someone to build your whole product for you
- · You value credentials over inspectable work
- · You're building surveillance or exploitation systems
- · You want a generic "AI strategy" deck
Request a discovery call
Tell me what you're building and where it breaks. You'll get a direct reply within 24 hours — from me, not a funnel.
Prefer email? travis@traviseric.com