> proof

Proof We Build
Real Systems.

These are buyer-facing summaries of the kinds of systems we built while selling complex enterprise deals and now help other teams set up. Employer-linked work stays anonymized where it should.

This work came from long sales cycles, technical buyers, and regulated environments where generic AI demos are not enough. The pattern is the same in each one: connect the right data, add the right knowledge, build the right workflows, and put guardrails around how the team uses it.

> How to Read These

Each example below shows the business problem, the system we built, and how the team actually used it in the real sales motion.

Problem first

What was still too manual, fragmented, or risky before the system existed.

System second

What got connected: data sources, memory, workflows, plugin layers, and guardrails.

Usage and result

How the team used it in the real sales motion and why that mattered.

Anonymized flagship system

Connected AI Sales System

We built a packaged AI sales system for the enterprise team at a multi-billion-dollar SaaS company serving regulated industries. It was built for long-cycle selling into pharma and biotech and connected CRM data, regulatory data, financial filings, hiring signals, engagement data, and internal account knowledge into one working system.

Business problem

The team was selling complex enterprise deals into pharma and biotech. Important context lived across CRM records, account folders, market signals, filings, notes, and team knowledge, so too much work still depended on manual research and memory during long, technical sales cycles.

System built

We built the connected system around the model: data connectors, knowledge sources, skill-based workflows, read-only database policies, a packaged plugin, and MCP tools so approved workflows could run safely inside Claude.

Team usage

  • Evaluate accounts across CRM, FDA, financial, hiring, engagement, and web signals
  • Analyze deals with methodology-aware workflows for full-cycle enterprise selling
  • Draft emails, prep calls, build campaigns, and create conference gameplans in context
  • Give reps a safer tool layer with plugin commands, guardrails, and auditability

The result was not “use ChatGPT better.” It was working infrastructure for the sales team.

Anonymized flagship system

Account Intelligence and Signal Engine

Built a multi-source intelligence layer that ingests account signals, normalizes entities, ranks accounts, drafts outreach, and turns scattered research into usable action.

Business problem

Revenue teams usually have too many weak signals and not enough confidence in what matters now. Data sits in separate tools, naming is messy, and the team ends up with dashboards instead of decisions.

System built

We built an intelligence engine that ingests signals from multiple sources, resolves company identity, scores what changed, ranks where to focus, and prepares drafts or next-step recommendations before a human acts.

Team usage

  • Monitor timing signals across regulated-industry accounts
  • Surface account rankings and operator-ready research queues
  • Draft outreach and next actions with better provenance
  • Support recurring briefings and territory reviews

This turned scattered data into a smaller set of accounts worth acting on, with context attached. Better prioritization creates more leverage than more activity.

Public build

Regulatory Data Platform

Built a public FDA data platform and MCP server with daily-refreshed regulatory data, normalized company resolution, and production documentation.

Visit RegDataLab

Business problem

Public FDA data is useful, but the raw sources are fragmented, hard to normalize, and awkward for teams or agents to query directly.

System built

We built a public data product and MCP server that cleans up the source data, resolves companies more consistently, and exposes that information in a form operators and AI tools can actually use.

Team usage

  • Research facilities and compliance actions without digging through raw source systems
  • Feed cleaner regulatory context into account research and operator workflows
  • Give AI clients a stable interface for public regulatory data

It is public proof that RunSales can package messy, high-value data into a real product, not just an internal workflow.

> Other Systems Shipped

These are not the main story, but they show range around the core sales system work.

Executive briefings and territory monitoring

Recurring briefings that pull recent signals, account context, and next actions into one operator-ready view for the day.

Campaign and conference systems

Systems that draft campaign plans, conference guides, outreach assets, and sales content from reusable playbooks and source material.

Cadence enrollment workflows

Workflow layers that prepare personalized drafts, package outreach, and move contacts into the next approved step instead of stopping at research.

Sales asset generation

One-pagers, decks, pages, and other sales materials generated from brand rules, account context, and reusable source content.

Want This Kind of
System for Your Team?

Email the team, workflow, and tools you are working with. We will tell you what we would connect first, what we would automate second, and where a pilot would actually be worth doing.

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