AI integrations for teams that already have the stack and need AI inside it, not a new system around it
Most teams do not need a custom AI agent from scratch. They already have the CRM, inbox, docs, internal tools, and database. What they need is AI inside that stack, so the team stops copying context around by hand just to get one useful step done.
I connect the systems already in place and add AI where it earns its keep: drafting, synthesis, triage, research, categorization, recommendation. The source of truth stays where it already is. The AI layer helps the workflow move faster without pretending to become the product.
Based in
Los Angeles, CA
Best for
B2B founders, operators, and investor-backed teams that need the technical decisions thought through, not just executed.
Outcome
A system you can actually run in production, not a one-off demo that collapses under real usage.
- The business already runs on a stack of tools that are individually fine but together create a lot of manual glue work.
- You want AI inside an existing workflow, not a dedicated backend system or a full SaaS rebuild just to justify using a model.
- The team needs drafts, summaries, triage, research, or recommendations that still stay inside operational guardrails.
- You want the output to land back in the system of record you already trust, with human review where it actually matters.
- Integration between the systems you already use: databases, CRMs, inboxes, docs, file storage, internal tools, and external APIs.
- Server-side prompt and tool orchestration with structured output, validation, and logging.
- Review layers so people can approve, edit, or reject high-risk output before it becomes customer-visible or operationally binding.
- Spend controls, retries, alerts, and source-of-truth rules so the integration is still useful after the novelty wears off.
- The AI layer doesn't become the source of truth by accident. It writes only where the workflow and validations allow it to write.
- Prompts, tools, and schemas live server-side so the behavior is versioned and controlled like the rest of the product.
- The model has acceptance criteria. If the output is not good enough, it gets rejected, retried, or kicked to a person.
- External failures are expected. APIs change, rate limits happen, and a useful integration needs a path for the bad day too.
I treat AI integrations as product and backend work, not prompt theater. The first question is whether the current stack should stay the source of truth. If the answer is yes, I add the smallest AI layer that makes the workflow meaningfully better without turning it into a new product by accident.
Claro
Admin-side AI drafting that saves only after validation, because a usable draft is helpful and cleanup work is not.
View case study →Dotty
Reminder drafts generated from invoice context, reviewed by the operator, and never auto-sent.
View case study →FunnelScout
Multi-step analysis that runs on schedule, logs AI cost during execution, and checks billing limits before it spends.
View case study →LA Market Report Agent
A monthly intelligence workflow that turns seven sources into one delivered report instead of another dashboard.
In progressIf this sounds like the shape of the problem, send me what the workflow is doing today, where it breaks, and what has to stay true in production. I care less about the pitch and more about the constraint.