We deploy AI agents
into your business.
Not slide decks. Not strategy memos. Working systems that do real work, embedded in your operations.
The Thesis
The AI bottleneck is no longer intelligence. It's implementation.
Models are good enough. Tools are everywhere. Value goes to the team that can get inside the business, understand the workflow, and make agents work in production.
We embed inside your organization. We don't advise from a distance.
You own everything we build. No vendor lock-in. No dependency.
Operators who build. Not analysts who advise.
AI opportunities identified
Mapped across every department of a $600M national retailer
AI roadmap board-approved
Multi-year transformation plan for a $1B research organization
AI companies founded & acquired
Automated Insights, Infinia ML, Bionic Health
Years building AI systems
From early NLP to modern agent architectures
Days from kickoff to working agents
Quick wins in 30, deployed systems by 90
Industries served
Retail, healthcare, financial services, legal, pharma, PE, manufacturing, research
Most companies don't have an AI problem.
They have a deployment problem.
This is what the bottleneck looks like inside a mid-market company. Lots of experimentation. Very little operational change.
Your team ran an AI pilot. It went nowhere.
You bought Copilot licenses. Nothing changed.
Your board keeps asking for an AI plan. You don't have one yet.
People are experimenting in silos, but no workflow actually changed.
You don't have a technical team that can deploy agents into real systems.
A competitor moved from exploration to execution while you're still comparing tools.
Three phases. Real outcomes.
We embed inside your organization and deploy AI agents workflow by workflow. Discover the work. Deploy the systems. Operate the rollout.
Discover
We interview every department, map the work, score the opportunities, and build the governance to move fast without creating chaos.
- Stakeholder interviews across the business
- 100+ AI opportunities mapped and scored
- Governance, permissions, and acceptable-use guardrails
Deploy
We pick the top workflows, build the agents, connect the systems, and test against real work until the output is usable.
- Top 2-3 workflows selected and designed
- Agents integrated into real systems and data
- Hands-on change management with the people doing the work
Operate
We stay embedded, expand quarter by quarter, measure what changes, and transfer capability into your team over time.
- Quarterly expansion into new workflows
- Metrics tied to throughput, margin, and time saved
- Reusable playbooks and knowledge transfer
Two ways to work with us.
A project to build and deploy. Or ongoing leadership to keep your AI strategy evolving. Most clients start with one and add the other.
Project-Based
AI Engineering
We embed inside your organization, identify the highest-value workflows, and deploy AI agents that do real work. Quick wins in 30 days. Production systems by 90. You own everything we build.
Workflow-specific agents — not generic chatbots
Production deployment — real users, real data, real accountability
Full IP transfer — no vendor lock-in, no dependency
Multi-model architecture — right model for each task
30–90 day engagements
65% less than Big 4
Ongoing
Embedded AI Leadership
Senior AI direction without a full-time executive hire. We stay embedded in your organization — attending leadership meetings, setting technical direction, and keeping your AI strategy evolving quarter by quarter.
Leadership meetings — we sit at the table, not on a call
Technical direction — architecture, vendor, and build decisions
Quarterly roadmap — evolving strategy as the landscape shifts
Team enablement — building internal AI capability over time
Ongoing monthly
No recruiting. No ramp-up.
We leave working systems behind.
Board-approved roadmaps. Workflow-by-workflow deployments. Production systems with real users, real data, and real accountability.
$1B Research Organization
$24M AI Transformation Roadmap, Board-Approved
Ran a three-phase engagement across the organization: interviews, readiness assessment, governance, and roadmap design. The final board package secured approval for a $24M AI transformation plan.
Roadmap approved
Delivery phases
Board approval
$600M National Retailer
100+ AI Opportunities Mapped, Agents Deploying Quarter by Quarter
Embedded inside a 310-store retailer, interviewed every department, scored more than 100 AI opportunities, and are now deploying workflow-specific agents quarter by quarter with executive sponsorship.
Opportunities identified
Store locations
Deployment cadence
PE Firm AI Enablement
Portfolio-Wide AI Training & Board-Level Strategy
Partnered with a private equity firm to build AI literacy across their portfolio. Delivered executive training sessions, board presentations, and an HR summit. The engagement became the template for rolling out AI readiness across portfolio companies and directly sourced new client referrals.
Engagement value
Training tracks
Wide rollout
Pharma IT & Executive AI Readiness
From Skepticism to 15 Prioritized Use Cases in One Day
Ran a full-day AI leadership workshop for a pharmaceutical company's IT and executive team. Used the SEAM framework to move from skepticism to a prioritized list of 15 use cases. 30% teaching, 30% interaction, 40% hands-on application with their actual business data. The moment they saw AI working on their own problems, resistance evaporated.
Use cases prioritized
To alignment
Executive buy-in
Multi-Model Document Extraction
78% to 95%+ Accuracy, 40% Cost Reduction
Built a pipeline that routes different document types to different models. Gemini for structured tables, Claude for narrative text, OpenAI for OCR edge cases. Total processing cost dropped 40% because human review nearly disappeared.
Extraction accuracy
Cost reduction
Models orchestrated
Regional Law Firm AI Strategy
Executive Alignment in a Single Day
Facilitated a leadership session that transformed executive thinking about AI from fear and skepticism to a shared vision. Built a practical roadmap focused on document review, research automation, and client communication workflows.
Attorneys served
Priority workflows
Leadership buy-in
What happens when we show up.
“Within 60 days, we went from zero AI strategy to three pilots running in production. The speed and clarity they brought was unlike any consulting engagement we've experienced.”
COO, National Retailer
“They didn't just give us a slide deck. They sat with our teams and built the solutions alongside us. That hands-on approach made all the difference.”
VP Operations, PE Portfolio Company
“The leadership facilitation session completely changed how our executive team thinks about AI. We went from fear and skepticism to a shared vision in a single day.”
CEO, Regional Law Firm
Dispatches from the
deployment floor.
Weekly notes from real client engagements. What's working, what's breaking, and what we're learning about deploying AI agents in the wild.
The Company That Never Built Software Before
“Having a software team is very expensive, and it's a whole new thing. Now it's not as expensive.”
Spent time this week with a client that has strong product market fit, great distribution, and loyal customers, but has never had an internal software development team. They've always outsourced tech and for good reason. Their leadership asked me to help them figure out an AI strategy, and my feedback surprised them: you should actually lean into technology. That's the opposite of what I tell most companies, where founders obsess over tech when they should be selling. But this client has the reverse problem. The CEO put it well when he said one employee was spending five days a month reading emails and putting them into a spreadsheet. Having a software team is very expensive, and it's a whole new thing. Now it's not as expensive. With a small internal team of two or three people, they could start building real capability. The trick is figuring out when something should stop running on someone's laptop and start running somewhere permanent.
The CEO Who Became a Developer Overnight
“He called me the next day and said it was the best thing he'd seen in 20 years.”
Showed a CEO a coding tool two weeks ago. He called me the next day and said it was the best thing he'd seen in 20 years. Now he's building internal reports, pulling data from email platforms, and generating analysis that used to take his team days. His president told me it transported the CEO back to the level of engagement he had 15 years ago when he was hands-on building the business. Meanwhile, at a completely different client, a law firm innovation team that started with basic document review is now building custom AI workflows on their own. The pattern is the same everywhere I look: the people moving fastest aren't waiting for permission or a formal strategy. They're just building.
Office Hours Drop Off Is Completely Normal
“The peer to peer seems to go over much better when it's just like, show me how you're using it.”
Had a biweekly check in this week with a legal AI vendor deployed at a law firm client. They reported 112 monthly active users and 98 weekly active users, which they said was strong for this stage. But attendance at weekly office hours had dropped to nearly zero after just two weeks. I asked if there were techniques to stave off the drop off. The vendor confirmed it's completely normal. People stop coming because they've figured out the basics. What actually keeps momentum going is internal peer conversations. The peer to peer seems to go over much better when it's just like, show me how you're using it. We're now setting up a lunch and learn built around an AI champions model, where attorneys demo their own workflows to colleagues. I've seen this pattern across several companies now. My training has limited shelf life. A colleague showing their actual daily use case sticks much longer.
The Dangerous Middle of AI Adoption
“Her team sent out AI-generated analysis with bad numbers because nobody in the middle tier thought to question it.”
Had a conversation with a CFO this week about who's most at risk when AI gets things wrong. It's not the junior people -- they're learning new tools anyway and don't pretend to know the answers. It's not the senior people -- they have enough experience to spot when something looks off. It's the mid-career folks. They know enough to feel confident but haven't seen enough cycles to catch the subtle errors. One exec told me her team sent out AI-generated analysis with bad numbers because nobody in the middle tier thought to question it. That's the real danger zone of AI adoption right now.
Voice AI Agents Are Ready for Production
“Callers can't tell they're talking to an agent. Six months ago this wasn't possible.”
Deployed an inbound call handling agent for a retail client this week. Latency is under 500ms. Callers can't tell they're talking to an agent. Six months ago this wasn't possible at production quality. Now it's table stakes. The companies that figure out voice-first AI interactions will have a massive advantage in customer-facing workflows.
The Meeting Recording Goldmine
“If you're not recording your meetings, you're leaving the most valuable data your business produces on the floor.”
Did 14 stakeholder interviews in a single day for an AI strategy engagement. Recorded every one. Then used Claude to answer questions about what specific people said across all 14 conversations. Transcripts are the sawdust of business. If you're not recording your meetings yet, you're leaving the most valuable data your business produces on the floor.
Trends and insights from
inside the companies doing the work.
We're inside mid-market companies every week deploying AI agents. These are the patterns we're seeing, the lessons we're learning, and the shifts that matter most right now.
Testing: The New Software Bottleneck
Code production has been transformed by AI and automation—but human validation hasn't kept pace. As one consultant discovered, the real constraint isn't building features faster; it's the client's ability to review, test, and confirm they work as intended.
The AI Infrastructure Problem Nobody Talks About
Building AI automations is one thing—but where do you actually run them? This post explores the critical infrastructure bottleneck that stalls most enterprise AI deployments, from laptop solutions to orchestration platforms.
The Spec Problem in AI-Driven Development
As AI accelerates code generation from weeks to hours, a new bottleneck emerges: writing detailed, unambiguous specifications. Unlike human engineers who clarify requirements, AI builds exactly what you describe—exposing gaps in product thinking that traditional development masked.
The New AI Development Bottlenecks
For 25 years, developer capacity was the constraint—specs took weeks to estimate and months to build. In 2026, AI has fundamentally changed that equation: prototypes now take hours instead of weeks. But removing the old bottleneck just revealed new ones that enterprise teams need to understand.
AI Adoption Requires Ambition, Not Just Focus
While startups traditionally obsess over focus, AI changes the equation—companies should think bigger about next-order problems rather than shrinking teams. The real missed opportunity isn't efficiency gains from AI; it's the lack of ambition to reimagine what your workforce can accomplish with these tools.
AI Security: The Flip Side of Productivity
An experiment with Claude Code revealed how easily AI can map vulnerabilities in home networks—without special tools or expertise. As AI makes businesses 10x more productive, it's doing the same for bad actors, and enterprise leaders need to think seriously about AI security alongside adoption.
Why AI Training Fails (And How to Fix It)
AI adoption isn't a technology problem—it's a habit problem. Companies seeing real ROI treat AI deployment like a fitness program: picking specific workflows, building daily triggers, and measuring actual usage rather than just conducting training sessions.
$1,000 in AI Tools. $50,000 in Value.
I spent over $1,000 on AI tools last month. Caught myself grumbling about it for about three seconds. Then I tallied up what we actually got for that money. This is the cheapest it's ever going to feel relative to what it delivers.
The Executive AI Operating System
Andrej Karpathy just described building something I've been working on with executives for the past year. The knowledge base is the foundation. The agents are where it gets interesting. Executives go from skeptical to dependent in less than two weeks.
Operators who build.
Not analysts who advise.
After 20+ years building AI companies and 170+ startup investments, we kept seeing the same gap: companies know AI matters, but they cannot get it to work inside the business. That's the gap ACG fills.
3 AI companies founded and acquired
20+ years building AI systems before the current hype cycle
Senior operators only. No junior consultant bench.
Everything is built in your stack, under your accounts.
Founder
Managing Director
Founded Automated Insights, the first generative AI company, before co-founding Infinia ML and Bionic Health. Two Masters from MIT. Cisco's youngest Distinguished Engineer. 8 AI patents. 170+ startup investments.
The Team
Common questions
No. We bring the technical capability. Your team brings the domain knowledge. We build the systems, deploy them in your infrastructure, and train your people to operate them. If you later want to bring it in-house, you own everything.
You do. Everything runs in your infrastructure, under your accounts. No vendor lock-in. No dependency. When we leave, you keep running.
Quick wins ship in the first 30 days. Working production systems by day 60-90. Some clients extend into ongoing advisory. Most don't need to.
Mid-market companies doing $50M-$500M in revenue. Large enough to have real workflows worth automating. Small enough that a senior team can move the needle fast.
They send junior analysts to write slide decks. We send senior operators to build working systems. Our engagements cost 65% less and produce deployed AI agents, not PowerPoints.
Stop exploring.
Start deploying.
If your company has real workflows, real bottlenecks, and real pressure to act on AI, this is the model built for you.