AI integration services for the systems your business actually runs on

An AI integration service connects a large language model to the tools and data your business already runs on. Instead of buying another standalone AI app to log into, you get AI running inside the systems where work actually happens: your CRM, your inbox, your calendar, your knowledge base. A working integration usually covers five pieces. An assessment of your stack, model selection across providers, an orchestration layer through Make or n8n, data and knowledge-base connections, and ongoing monitoring for cost and accuracy.

For a 5 to 100-person business, expect $5,000 to $15,000 for an AI Readiness Audit, $10,000 to $25,000 for a single-system integration, and $25,000 to $45,000 for a multi-system build. Ongoing operate retainers run $500 to $5,000 per month. We publish these bands because hiding them wastes everybody's time. Most SMB scopes land in the middle of each band, and the audit produces a fixed-fee SoW, so you never sign a build without knowing the number.

Single-system integrations ship in 2 to 4 weeks from kickoff. Multi-system builds run 4 to 8. The AI Readiness Audit that scopes the build adds 1 to 2 weeks before construction starts. Most clients go from first discovery call to live AI in 4 to 12 weeks. The biggest single time sink is data quality, not engineering. The audit is built to find the data-hygiene problems early instead of stumbling into them mid-build.

No. We build alongside your live stack, never on top of it. Integrations sit on your accounts and your infrastructure: your HubSpot, your n8n instance, your model keys. Builds happen on staging, get tested against real data with eval suites, and only swap into production once you sign off. If something goes sideways post-launch, the workflow rolls back to the previous version, not to a manual workaround. The retainer covers the rare cases where a provider deprecates a model with no warning.

The systems we ship against month over month: HubSpot, GoHighLevel, Pipedrive, and Salesforce for CRMs; Make, n8n, and Zapier for orchestration; OpenAI, Anthropic, and Google Gemini for models; Slack, Gmail, Cal.com, and Notion for comms and knowledge; Stripe and Airtable for payments and data. Voice and telephony, including Twilio, Vapi, and ElevenLabs, run through our AI Voice Agents service. If your stack includes a tool we have not shipped against, the discovery call is where we tell you honestly whether we can build the integration cleanly or whether you should hire someone else.

Yes. The source code lives in a GitHub repository on your organization's account. Prompt templates, configs, and runbooks sit alongside it, version-controlled and documented in plain English. The integrations run on your infrastructure or your accounts. If you stop working with us, the build keeps running. You walk away with the repo, an environment-variables document, and a runbook in Notion or whatever tool you prefer, covering every workflow, every credential, and every recovery step.

You do not feel it. We build with an AI gateway, usually Vercel AI Gateway, OpenRouter, or Portkey, sitting between your workflow and the model provider. Your workflow asks for a model that meets a latency and cost target instead of naming one provider hard-coded. When pricing shifts or a model is sunset, the gateway routes to the closest live equivalent, and we swap the configured default with one config change. No rebuild, no fire drill, no rewriting prompts that have been tuned for months.

MCP, or the Model Context Protocol, is a standard interface between an AI model and a business tool. Anthropic released it in late 2024 and donated the specification to the Agentic AI Foundation, a Linux Foundation project, in late 2025. OpenAI and Google added native support. The payoff for an SMB is simple: one MCP server lets an AI agent read and write across your CRM, calendar, knowledge base, and payment system without a custom connector for each tool. We use MCP on builds where the client has 4 or more tools and wants AI to act across all of them, not just answer questions about one.

Yes. For dental, legal, vet, and any vertical handling PII, we deploy on your infrastructure with a PII redaction layer in front of every model call. Data residency choices get made up front, US or EU model hosting depending on your customer base. For HIPAA-adjacent workflows, we use providers with signed BAAs and configure logs to exclude protected information. The audit phase is where we sort out which data flows need redaction, which need self-hosting, and which can run on standard cloud providers.

Only if you want us to. The Operate Retainer runs $500 to $5,000 per month and covers prompt tuning, model swaps, cost monitoring, accuracy and drift checks, and a monthly performance report. Most clients keep it because AI builds are alive. Providers ship new models, your data shifts, and prompts that were tuned in May read differently in November. Clients who want to operate it themselves get a runbook and 30 days of post-launch support included in the build fee.

Sometimes, yes. ChatGPT Team is the right answer when the use case is general assistance for individuals on your team. Lindy or a SaaS chatbot fits when the workflow is generic enough to live inside their template. Custom integration is the right answer when the AI has to read and write to the systems you actually run, when the workflow is specific to your business, or when you have data inside your CRM, knowledge base, or scheduler that a SaaS tool cannot reach. The discovery call is where you get an honest read on which side you are on. We tell people to buy off the shelf when that is the right call.

We define success metrics during the AI Readiness Audit, before any code gets written. The usual targets are hours saved per week, leads enriched per month, support tickets resolved without a human, response time on inbound queries, and revenue captured from work that was previously falling through the cracks. The monthly performance report tracks those numbers against the baseline we measured at kickoff. The rule of thumb we work to: an integration should pay for itself within 6 to 12 months on a single-system build, and faster on multi-system work that touches revenue directly.