— The problem —
Most AI integrations die in production.
The typical AI integration project goes like this: a developer wires up an OpenAI API call, it works in staging, and then it fails in production because nobody thought about auth, rate limiting, cost controls, or what happens when the model returns something unexpected. The integration gets disabled, the team loses confidence, and the project dies.
We've audited enough of these to know the pattern. Production AI integrations aren't hard — they just require the surrounding infrastructure that most development shops skip. We include auth, observability, error handling, and a documented security handoff in every engagement. Not as add-ons. As baseline.
— What we integrate —
Your existing tools, augmented with AI.
We connect to your tools via API or webhook. No stack replacement required.
CRM
CRM
HubSpot, Salesforce, Pipedrive
AI-drafted responses, lead scoring, deal summary generation, follow-up automation.
Helpdesk
Helpdesk
Zendesk, Intercom, Freshdesk
Ticket triage, FAQ resolution, sentiment detection, escalation routing.
Scheduling + ops
Scheduling + ops
Calendly, Google Workspace, Notion
Meeting prep summaries, async briefing generation, internal doc Q&A.
E-commerce
E-commerce
Shopify, WooCommerce, custom
Order status agents, return handling, product description generation.
Internal tools
Internal tools
Custom dashboards, admin panels
AI-powered search, data extraction, LLM-assisted workflows embedded in your existing UI.
Document workflows
Document workflows
Google Drive, Dropbox, S3
Contract parsing, invoice extraction, document classification, automated tagging.
— What's included —
Every integration includes five non-negotiables.
01
RAG pipeline
If the integration needs to answer questions from your data, we build a retrieval layer — document chunking, embedding, vector search, re-ranking. The AI only answers from verified sources.
02
Auth + API key management
API keys stored in environment secrets, rotated on schedule, scoped to minimum permissions. Role-based access controls so the AI can only see what it needs to.
03
Security + PII handling
PII redacted before it leaves your systems. Audit logs of every AI action. No customer data sent to third-party models without explicit scoping and consent.
04
Observability
LLM call logs, input/output captures, latency and cost per call, error rates, and drift detection. Dashboards your team can read without touching code.
05
Error handling + fallbacks
Every integration has defined failure modes: rate limit handling, timeout fallbacks, graceful degradation when the model returns unexpected output. It won't silently break.
— How it works —
Four steps, fixed timeline.
01
Day 0 · 30 min
Intake
A structured questionnaire about your stack, tools, and the workflow you want to augment. We follow up over email — no discovery calls.
02
Days 1–3
Design
We map the integration: which APIs, what auth flow, what data the AI sees, what it outputs, and how failure is handled. You approve the design before build starts.
03
Weeks 1–6
Build
Integration built and tested in a staging environment. Weekly check-ins. You can see the work in progress — we don't disappear for six weeks.
04
Final week
Production
Deployment to production with the full observability stack, documentation of what the AI can access, and a handoff call covering monitoring and incident response.
— Built for —
Right for some. Not for everyone.
Built for
- $1M–$15M businesses with digital operations on SaaS tools
- Teams where a specific workflow costs 5+ hours/week in manual work
- At least one technical contact available for the integration handoff
- Ready to run AI in production, not just experiment
Not a fit
- Businesses with no existing SaaS tools or APIs to connect to
- Projects where requirements aren't defined yet — start with the audit
- Teams that need ongoing AI management after delivery
- Projects requiring on-premise deployment without cloud API access
Start with the workflow that costs the most.
The $497 AI Profit Leak Audit identifies the highest-ROI integration opportunity in your operations — the one workflow worth automating first. Most clients use the audit output as the spec for their first integration. If you already know what you want to build, book a call.
— Common questions —
Quick answers.
What does AI implementation mean for a small business?+
AI implementation means adding AI capabilities to the tools and workflows your team already uses — without replacing your CRM, helpdesk, or ERP. For most $1M–$15M businesses, this looks like: an LLM that drafts replies in HubSpot, a document parser connected to your existing storage, or an AI triage layer sitting in front of your support queue. The goal is a production system that works inside your current stack, not a demo that lives in a spreadsheet.
How long does AI integration take?+
Simple integrations (single API, one workflow) take 2–3 weeks. More complex integrations involving multiple tools, auth flows, and custom UIs are 6–8 weeks. We scope every project to a fixed timeline before work starts — you'll know the delivery date before we write a line of code.
Do I need to replace my existing software to use AI?+
No. Most AI integration work augments what you already have. We connect LLMs to your existing systems via API, webhook, or direct database access. Common targets are Salesforce, HubSpot, Zendesk, Intercom, Notion, Google Workspace, and custom internal tools. If your vendor has an API, we can wire AI into it.
What's included in the security and auth setup?+
Every integration includes API key management with rotation support, role-based access controls, audit logging of AI actions, and rate limiting. For tools that touch customer data, we add PII redaction before data leaves your systems. We don't ship integrations without a documented security handoff — your team should know exactly what the AI can and cannot access.
What does observability mean in practice?+
Observability means you can see what the AI did and why. We instrument every integration with LLM call logs, input/output captures, latency and cost tracking, and error alerting. You get a dashboard (or Slack notifications) when something breaks or drifts. Most clients use this to catch hallucinations before they reach customers.
Can I start with just one workflow?+
Yes, and that's usually the right call. We recommend starting with a single high-value, high-volume workflow — typically the one that costs your team the most hours per week. Once it's in production and you've seen how the AI performs in the real world, it's much easier to scope the next workflow confidently.
— Ready to start? —
Connect the workflow. Ship the automation.
Most integrations go from intake to production in 4–6 weeks. Start with one workflow, see how it runs, and scope the next one from a position of confidence.