How It Works

There's no 47-slide onboarding deck. No "alignment workshops." No three-month discovery phase. Here's what actually happens:

1. We talk about your workflow

30-60 minute call. Not a demo, not a pitch — a conversation about how your business actually runs. I'll ask questions like:

• What does your team spend time on that feels repetitive or manual?
• Where do mistakes happen most often?
• What information do people search for over and over?
• What would you automate first if you could snap your fingers?

By the end of this call, we'll have identified the highest-impact place to start. Usually it's obvious once someone actually asks the right questions.

2. I scope it and give you a number

You get a clear scope document: what the AI will do, what it won't do, what it connects to, and what it costs. No ambiguity, no open-ended billing. You approve the scope before any work starts.

Most first projects land between $3K-$10K depending on complexity and integrations. I'll tell you if something is overkill for your situation or if there's a simpler approach that gets you 80% of the value at 20% of the cost.

3. I build it fast

Most v1 builds ship in under two weeks. Some in days. You'll see progress throughout — not a black box that opens three months later.

The system is built with production-grade code, not no-code tools that break when you need them to do something real. Architecture decisions are made for long-term maintainability, not demo speed.

What goes into a typical build

• System prompt engineering — The AI's personality, knowledge boundaries, tone, and behavior rules. This is where domain expertise matters most.
• Tool integrations — API connections to your existing tools. CRM, ERP, email, databases, spreadsheets, Slack — whatever you're already using.
• Memory and context — The AI remembers previous conversations, user preferences, and relevant history. Not a blank slate every time.
• Knowledge base — Your documents, procedures, specs, and FAQs indexed for fast retrieval. The AI can search and reference them in real time.
• Deployment — Hosted on secure cloud infrastructure or on-premise. Web chat, Slack, SMS, email — whatever channels make sense for your team.

4. You test it in your real workflow

Not in a sandbox. Not with test data. In your actual work environment, with your actual team, on your actual problems. This is where edge cases surface and where the AI gets tuned to handle the things that only come up in real use.

Reasonable iteration during the build phase is included in the project scope. I don't nickel-and-dime you for bug fixes or behavior tuning during the initial rollout.

5. We expand from there

Once v1 is running and proving value, we talk about what's next. Common expansions:

• Adding more integrations (new tools, new data sources)
• Deploying to more channels (SMS, Slack, email)
• Rolling out to more team members or departments
• Building additional agents for different workflows
• Adding analytics and reporting on AI usage and outcomes

The technology under the hood

We build on open-source, self-hosted AI stacks — not generic SaaS chatbots or single-vendor lock-in. We set up the open-source models and harness agents to run them, and we recommend self-hosting: today's top open-weight models are in the top 5-10% of overall intelligence and run on a $3,000-$10,000 computer, so you get near-frontier capability without per-seat fees or sending your data to a third party.

Model routing picks the right open model for each task — optimized for accuracy, speed, and cost. Complex work is decomposed into subtasks handled by specialized sub-agents, connected to the tools your team already uses.

Your data stays on infrastructure you own or control — it is not sent to a third-party provider for training. This is non-negotiable in every build.

Manufacturing AI | Telecom AI | AI Payments