AI wired into the systems you already run

AI integration is the work of connecting models, APIs, CRMs, data sources, apps, and channels into one usable flow, then orchestrating that logic into reliable production pipelines. A MarkAge360 engagement typically includes a system map and integration brief, authenticated connectors to your stack, model and tool orchestration with request schemas and guardrails, end-to-end request QA, and a monitored deployment. You get a working AI layer wired into your real systems — not a disconnected demo.

We price by scope: the number of systems we connect, how much orchestration and guardrail logic the pipelines need, and whether we run ongoing monitoring after launch. Smaller integrations — one model wired into a CRM or app — are fixed-scope projects, while multi-system pipelines with SLAs are usually a build plus a monthly run-and-monitor retainer. We scope and quote against your integration brief before any commitment, so there are no surprise line items.

It depends on how many systems are involved and how clean their APIs and permissions are. A focused integration into one or two well-documented systems can reach production in a few weeks, while orchestrating models across a CRM, data warehouse, app, and channels takes longer through connection, orchestration, and QA. We work in the same connect → orchestrate → deploy phases you see on this page, and we give you a dated timeline once the integration brief is signed off.

A demo proves a model can do something once; an integration makes it do dependable work every time. We wire AI into the systems you already run with authenticated connectors and clean read/write contracts, compose models and tools into pipelines with retries and guardrails, and deploy behind monitoring and uptime SLAs. The difference is reliability under real traffic — a prototype breaks on edge cases, while an integration is QA'd across the full request path before users touch it.

We're model-agnostic and connect to the stack you already use — CRMs, data warehouses, internal apps, and channels like web, chat, email, and messaging. On the AI side we orchestrate leading hosted models and tool-calling APIs, and we build authenticated connectors against your existing services rather than forcing a rip-and-replace. If a system exposes an API or webhook, we can wire it into the pipeline; where one doesn't, we'll flag it early and propose a path.

We connect through authenticated, least-privilege connectors with scoped read/write contracts, so each system only exposes what the integration actually needs. Pipelines ship with guardrails, input validation, retries, and fallbacks, and we test prompts, APIs, events, and edge cases across the full request path before launch. After deployment we monitor uptime, latency, and errors against agreed SLAs, so you find out about issues from a dashboard rather than from a customer.