I build AI agents for businesses. Not slide decks about AI.
This week: an AI agent for compensation management. Slack commands, automated reconciliation, governance gates. Zero Salesforce.
Last month: AI revenue intelligence dashboards for a CIO. 24 components. Inside Salesforce.
The platform doesn't matter. The architecture does.
Landscaper who needs AI estimates from job photos? Done.
Real estate firm drowning in manual data? Done.
Enterprise with 10 disconnected tools? Done.
Same patterns. Any industry. Any scale.
https://t.co/jMz4iX8rX0
Unpopular opinion: most businesses don't need a "ChatGPT for their company."
They need one specific AI agent that does one specific job really well.
Estimate generation. Data reconciliation. Lead qualification. Appointment scheduling.
Start with one agent. Prove the ROI. Then
15 years of enterprise architecture.
Not 15 years of reading about it. 15 years of:
- CPQ implementations that processed $100M+ in quotes
- Platform migrations with zero downtime
- AI dashboards presented to CIOs
- Managed packages published to AppExchange
- MCP servers deployed
Real estate firms run on 6 disconnected tools:
MLS. CRM. Email. Transaction management. Accounting. Marketing.
An AI agent sits in the middle and:
- Pulls new listings into your CRM automatically
- Generates comp analyses from MLS data
- Drafts listing descriptions from property
Before AI agent:
- 3 people manually reconciling comp data across 2 systems
- 2 days per cycle
- Errors caught weeks later
After AI agent:
- Slack command: "reconcile Q1 promotions"
- 4 minutes
- Every mismatch flagged with context
- Full audit trail
Same team. Same systems. Ju
Clients ask: "Which AI model should we use?"
My answer: depends on the job.
- Customer support bot? Claude Sonnet. Fast, cheap, great tone.
- Financial reconciliation? GPT-5. Best at structured reasoning.
- Sensitive data? Local Llama on your hardware. Zero cloud exposure.
- Co
Enterprise AI projects fail for one reason:
They skip the architecture.
Someone buys a tool. Bolts it onto the side. Gets a demo. Declares "AI transformation."
6 months later: nobody uses it, the data is wrong, and the vendor blames adoption.
Start with the architecture. Then
AI consulting projects I've worked on in the last 90 days:
- Compensation management agent (Varicent + Slack + n8n)
- Revenue intelligence dashboards (Salesforce + Gemini)
- MCP servers on Cloudflare Workers
- 10+ production AI agents on Apple Silicon
- Managed package on AppExc
Most AI consultants won't touch small businesses. 'Not enough budget.'
A landscaping company spending 10 hours/week on estimates, scheduling, and follow-up emails is leaving $50K/year on the table.
An AI agent handles all three. Costs less than a part-time hire.
The ROI math works at every scale.
4. GOVERNANCE + ACTION
The agent doesn't just answer -- it acts. But with guardrails.
Approval gates for big decisions. Audit trails. Rollback capability. Human-in-the-loop where it matters.
This pattern works for a 2-person crew or a 10,000-person enterprise.
Book a free call: https://t.co/jMz4iX8rX0
Every AI agent I build follows the same pattern. Doesn't matter if it's for a landscaping company or a Fortune 500.
Here's the architecture that works everywhere:
3. REASONING + SYSTEM APIs
The AI model processes the request WITH your real business data.
Not generic GPT answers. Your inventory. Your pricing. Your client history. Your comp plans.
Connected via MCP servers or REST APIs.
Stop paying $200/month for AI tools that do one thing.
I set up AI that runs on YOUR computer. No subscriptions. No data leaving your machine. No vendor lock-in.
What you get:
- Your own AI assistant running 24/7
- Custom workflows for your business
- Local LLMs that cost $0/month after setup
Setup starts at $99. 20+ models deployed. 10+ agents in production.
https://t.co/jMz4iX8rX0
Einstein AI: /user/month. Plus your Salesforce license. For a 20-person team, that's ,000/year just to ask questions about your own data.
StratoForce: ,000/year. One price. Every user. Unlimited queries.
Same data. Same Salesforce. Zero implementation.
That's not a feature difference. That's a business model difference.
https://t.co/uRvr9yWFlW
The industry is obsessed with MCP right now.
Model Context Protocol. Every AI vendor racing to add it.
Here's what they won't tell you: MCP works better when your AI lives INSIDE your CRM, not when you're syncing data to external servers.
StratoForce is Salesforce-native. We don't need MCP to bridge two systems. We're already there.
Your data. Your model. Zero egress.
https://t.co/uRvr9yWFlW
Aviso warned us about 'Wrapper AI' – generic LLMs dressed up in sales clothing that sit outside your CRM.
That's exactly why StratoForce is different:
- 100% Salesforce-native – runs inside your org
- Uses your model, your data
- No external sync, no trust gap
Stop adding patches. Start building native.
https://t.co/uRvr9yWFlW
The revenue intelligence space has gone quiet.
Gong hasn't published a blog in 30 days.
Clari's blog has been dark for 90+ days post-merger.
When the biggest names stop talking, the market gets hungry.
We keep building. We keep publishing. We keep showing our work.
Because revenue intelligence shouldn't be a black box.
https://t.co/uRvr9yWFlW
Gong's CCO said layering AI on top of old workflows isn't enough.
Then they hired 100 consultants to help you use their AI.
We built the alternative: 30 minutes on the AppExchange. No implementation. No consultants. Your data never leaves Salesforce.
The cheapest AI is the one you don't need help using.
https://t.co/uRvr9yWFlW