AI Integrations
Custom AI assistants and automations for the work that doesn't need a person doing it.
AI is most useful when it's pointed at routine work: answering FAQs, summarizing emails, drafting copy, syncing data between tools. We pick the right AI for the job, integrate it where it helps, and keep it from going off the rails.
What's included
- Discovery: where AI saves time vs. where it would just be noise
- Chatbots and AI assistants for customer support
- AI-assisted content workflows (drafts, summaries, translations)
- Document and email automation
- Custom integrations between AI providers and your existing tools
- Guardrails, monitoring, and cost controls so the AI doesn't go off-script
- Documentation so the team can adjust prompts and rules over time
How we work
- 01Intake
Map the routine work in your business: FAQ patterns, email triage, content production, internal data lookups, support tickets. Identify which would benefit from AI and which would just create noise. Pick the highest-leverage candidate to start.
- 02Build
Choose the right model for the job. Claude for nuanced reasoning, GPT for general-purpose, smaller open models for cost-sensitive workloads. Wire up the integration with your existing tools. Build in guardrails: response validation, cost caps, fallback behavior. Test edge cases before exposing to customers.
- 03Handover
Documentation that explains the prompt files, the guardrails, and how to adjust behavior. A cost-monitoring dashboard so you can see token usage. A monthly review cadence to catch drift in AI behavior or shifts in your business.
What you'll have when we're done
- A working AI integration deployed and connected to your tools
- Prompt files and configuration in your repository (you own them)
- Guardrails: rate limits, cost caps, fallback responses, escalation rules
- Cost-monitoring dashboard
- Documentation: how to adjust prompts, what to watch for, how to disable
- 30-day support window for tuning after launch
Outcomes
- Faster customer responses without growing the team
- AI deployed where it helps, absent where it doesn't
- Guardrails so AI failures don't reach customers
- Documentation and prompt files your team can iterate on
Common questions
Which AI model do you use?
Depends on the job. Claude for nuanced writing and reasoning. GPT for general-purpose work. Smaller open models (Llama, Mistral) for high-volume, cost-sensitive workloads. We don't lock you into one provider. The integration is designed so you can swap models without rebuilding everything.
How do you keep AI from saying something wrong to my customers?
Multiple layers. Prompt design that scopes the AI to your domain only. Response validation that checks output before display. Fallback behavior when confidence is low. Human-in-the-loop escalation for ambiguous cases. We don't claim AI is perfect; we make sure the failure mode isn't a customer getting bad information.
What does an AI chatbot actually cost to run?
It varies. A simple FAQ chatbot for an SMB usually costs a few thousand pesos per month in API spend. Custom integrations with high-volume workloads cost more. We design with cost controls (caps, batching, model fallback) so the bill doesn't surprise you.
Can the AI access my customer data?
Only what you configure. We design integrations with explicit data scopes. The AI sees only the records relevant to the question, not your entire database. Sensitive fields (passwords, financial data) are excluded by design. We document the data access pattern in the runbook.
What happens if the AI provider goes down?
Fallback behavior takes over. The bot says something graceful instead of breaking, support tickets queue normally, and your team gets an alert. We design for resilience because AI services have outages. That's a fact, not a hypothetical.
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