ALPN Digital Ltd

Growth-stage companies have HubSpot, GA4, LinkedIn Ads, and Google Ads all running but leadership is pulling numbers from four different places and getting four different answers. The CEO doesn't trust the pipeline number. The VP Marketing doesn't know which channels are actually driving revenue. What we build: We connect the full stack into Databox CRM pipeline, marketing attribution, paid performance, website conversion and build a live command centre that gives the CEO, VP Sales, and VP Marketing one shared view. Automated weekly reports replace the Monday morning data scramble. Genie lets any exec ask a plain-English question and get an answer instantly without waiting on ops. Why Databox: 130+ native integrations means no custom connectors to maintain. Automated reports eliminate the manual cycle. Genie turns the dashboard from something people check into something they can interrogate.

PE firms conducting due diligence on B2B targets are flying blind on revenue operations. Management accounts tell one story; the CRM tells another. There's no structured way to see pipeline reliability, conversion rates, attribution coverage, or rep productivity before the deal closes. What we build: As part of ALPN's Revenue Intelligence Audit, we connect the target company's CRM, marketing tools, and financial data into a Databox dashboard that surfaces the actual state of the revenue engine: pipeline velocity, lead-to-close conversion by stage, marketing-sourced revenue, forecast accuracy, and churn signals. The PE firm gets a live view of what the business is actually producing vs. what the deck says. Why Databox: Fast to deploy (deals move quickly), connects directly to HubSpot, Salesforce, Pipedrive, Stripe, and GA4 without engineering overhead. Genie lets the Portfolio Operations team ask questions of the data themselves without needing an analyst on-site.

Companies that have invested in AI-native GTM systems: automated qualification, enrichment agents, AI-powered outbound often can't see what those systems are actually producing. The AI runs in the background but the output never surfaces in a way leadership can act on. What we build: We use Databox as the output layer for the entire AI revenue system - connecting n8n automation workflows, HubSpot, and custom data sources via the Databox API. Dashboards track leads qualified by AI, sequences triggered, pipeline influenced, and time saved vs. manual process. We then connect Databox to Claude via MCP so the revenue team can query their AI system's performance in plain English, directly from their existing AI tools. Why Databox: The MCP integration is the unlock here. Databox data becomes queryable by Claude, GPT, and other LLMs, meaning the AI system that generates the pipeline and the AI tool that analyses the pipeline are connected. This is the reporting layer that makes an AI-first GTM stack actually legible to leadership.

Marketing says leads are up. Sales says quality is down. Neither team is wrong, they're just looking at different data. Without a shared view of lead-to-revenue attribution, the conversation goes in circles and budget decisions get made on gut feel. What we build: We connect HubSpot Marketing, HubSpot CRM, Google Ads, LinkedIn Ads, and GA4 into a single Databox view showing the full lead-to-revenue funnel: MQL volume, MQL-to-SQL conversion rate, SQL-to-close rate, pipeline contribution by channel, and CAC by source. Both teams see the same numbers. Budget reallocation decisions become data-driven rather than political. Why Databox: Pre-built HubSpot Marketing and HubSpot CRM connectors make the attribution join fast to set up. Automated weekly reports keep both teams aligned without a weekly data meeting. Forecasts let the VP Sales model pipeline scenarios without waiting on a spreadsheet.

Revenue teams are using AI tools (Claude, GPT) for analysis and drafting, but those tools have no connection to live business data. Every analysis starts with a manual data export. Every insight is already stale. What we build: ALPN connects Databox to the client's existing Claude or LLM workflow via the Databox MCP integration. The result: the revenue team can ask Claude questions like "which deals are most at risk this week?" or "what's our pipeline coverage vs. target for Q3?" and get answers grounded in live Databox data. No exports, no manual pulls, no lag. We configure the MCP connection, standardise the underlying metrics, and document the query library so the whole team can use it from day one.