What AI tools do you use?

We build on top of the tools you already have - HubSpot, Salesforce, n8n, Make - and layer in AI from OpenAI, Anthropic, and others where it makes sense. No proprietary platform, no new vendor to manage.

Is this actual AI or just rule-based automation?

Both, depending on the job. Simple routing and alerts are rule-based - that's the right tool for that. Scoring, summarization, intent detection, and research use real LLMs. We match the approach to the problem.

Do I need to replace my CRM?

No. We build on top of what you have. Whether that's HubSpot, Salesforce, or something else, we connect to it - we don't replace it.

Do I need a technical team to manage this?

No. The automations run on their own. We build it, hand it off, and your team uses the outputs - alerts, scores, summaries - without touching the infrastructure.

How do you handle our data?

Your data stays in your stack. We don't store it, sell it, or route it through proprietary systems. The pipelines we build connect your existing tools directly.

What does onboarding actually look like?

A 30-minute discovery call, then we scope the work and send a proposal. Once you're in, we go heads-down - no lengthy kickoffs or status meetings. You see working software fast.

What if the AI gets it wrong?

It will sometimes, especially early. That's why we build human review steps where it matters and tune based on your data. Accuracy improves over time - it's not set-and-forget.

How is this different from HubSpot's or Salesforce's native AI?

Native AI works within the walls of that one tool. We connect your entire stack and build logic that runs across it - enrichment, scoring, alerts, and summaries that pull from multiple sources and trigger actions anywhere.

Can AI Skills Replace Your Sales Automation Platform?

Claude Skills are reusable, structured instructions that Claude executes on demand or on a trigger - against your actual data, inside your actual tools. They’re not saved prompts. They’re not templates. They’re operations that run the same way every time, with the context and judgment of an AI model behind them.

For sales teams, Skills turn Claude from something reps open when they’re stuck into something that runs your operational playbook automatically.

What is a Claude Skill and how is it different from a prompt?

A prompt is a one-off question. You type it, get an answer, close the tab. The context is gone.

A Skill is a defined operation with a specific input, a set of instructions, and a structured output. It persists. It can be shared across a team. It can be triggered by events - a new deal entering the pipeline, a calendar invite, a Slack command. And because it connects to your tools through MCPs, it operates on live data instead of whatever you remembered to paste in.

The difference: a prompt asks Claude to help you think. A Skill tells Claude to do something specific, the same way, every time, on real data.

What sales workflows should you build as Skills?

Deal summary Skill. Input: a deal name or HubSpot deal ID. The Skill pulls the full deal record - stage, value, close date, all contacts, activity timeline, last call transcript summary, open tasks - and produces a one-page brief. Your manager uses this before every 1:1 instead of clicking through HubSpot for 10 minutes.

Competitive brief Skill. Input: a prospect company name. The Skill pulls every mention of competitors from that deal’s call transcripts and emails, synthesizes the positioning landscape, identifies which competitor was mentioned most and in what context, and outputs a brief the AE can review before their next call. The intel was already in your transcripts. Nobody had time to compile it.

Objection handling Skill. Input: a specific objection (e.g., “we’re already using Salesforce”). The Skill searches your closed-won deal transcripts for every instance where a rep successfully handled that objection, extracts the approaches that worked, and produces a response framework. Your best reps’ techniques, extracted and available to the whole team.

Weekly forecast digest Skill. Trigger: every Monday at 8am. The Skill scans every deal in the pipeline, compares stage and close date to last week, identifies what moved forward, what slipped, what’s new, and what closed. It delivers a formatted Slack message to the sales manager and CRO. Two minutes to read. Replaces the report someone used to build manually on Sunday night.

Post-call action Skill. Trigger: new call transcript available. The Skill reads the transcript, extracts action items the rep committed to, identifies any qualification data mentioned (MEDDIC fields, budget signals, timeline updates), and creates tasks in HubSpot for follow-ups. The rep’s admin work after a call drops from 15 minutes to a 30-second review.

How do Skills work with MCPs?

Skills are the logic. MCPs are the connections.

A Skill that generates a deal summary needs to read from HubSpot (CRM data), Gong (call transcripts), and possibly Gmail (email threads). Each of those is an MCP connection. The Skill defines what to do with the data. The MCPs provide access to it.

Without MCPs, a Skill can only work with what you manually provide. With MCPs, a Skill operates on your full data stack - live, current, and complete.

This is why the combination matters. MCPs alone give Claude access to data. Skills alone give Claude instructions. Together, they create operations that run on real data with consistent execution - which is what an ops team actually needs.

What if you’re a RevOps team of one right now?

You’re probably doing all of this manually. Building the forecast summary yourself. Compiling deal context before manager 1:1s. Chasing reps for call notes. Auditing CRM fields that should have been filled in last week.

Five Skills won’t replace you. But they’ll replace the 15 hours per week you spend on work that doesn’t require your judgment - the data pulling, the formatting, the compiling, the chasing.

That’s 15 hours you get back for the work that actually needs a human: designing the sales process, coaching reps on deal strategy, building the systems that make the team better.

Skills don’t automate your job. They automate the parts of your job that shouldn’t be your job.

How to build your first Skill

A Skill is a markdown file with a specific structure. It defines: who’s using it, what data they’ll provide, what Claude should do with that data, and what the output should look like.

Here’s the basic anatomy of a deal summary Skill:

The header names it and sets context: “You are a sales operations assistant. When given a HubSpot deal ID, you produce a concise brief that a sales manager would need before a rep 1:1.”

The input section specifies what will be provided: “Input: a HubSpot deal ID. You will read the deal record, contact records, and activity history from HubSpot MCP.”

The output section specifies the format exactly: “Output: a structured brief with five sections - Deal Status (stage, value, close date), Activity Summary (last 3 interactions), Risk Flags (anything that warrants attention), Contact Overview (key stakeholders and their roles), and Recommended Focus (one question or action for this 1:1).”

The more specific the output format, the more consistent the Skill runs. Vague instructions produce variable results. Exact specifications produce the same output structure every time, which is what a team actually needs.

Build the Skill as a file in Claude’s project folder. Test it manually 5-10 times with different inputs. Then decide whether to run it on demand or wire it to a trigger via your automation platform.

What makes a Skill reliable vs. unpredictable

The biggest failure mode in Skills is insufficient specificity about what “good output” looks like.

A Skill that says “summarize the deal” produces different output every time depending on what Claude decides to emphasize. A Skill that says “produce exactly five sections in this order, with each section being 2-3 bullet points maximum, using only information from the HubSpot record provided - do not speculate or add information not in the source data” produces consistent output that a team can rely on.

Three things make Skills reliable:

Explicit output format. Define the sections, the length, the structure. Don’t leave it to Claude’s judgment.

Bounded data inputs. Tell the Skill exactly what data it’s allowed to use and what it should ignore. If the Skill is summarizing a deal from CRM data, it shouldn’t be drawing on general knowledge about the prospect’s industry unless you explicitly want that.

Failure instructions. Tell the Skill what to do when data is missing. “If the last call transcript is more than 60 days old, flag this explicitly at the top of the output.” Skills that handle missing data gracefully are production-ready. Skills that silently produce incomplete output are not.

How to roll Skills out to a team

Don’t roll them all out at once. Pick one Skill, run it yourself for a week, tune it until the output is consistently useful, then share it with one other person and get their feedback.

The first person to use a new Skill will find the edge cases you didn’t think of. Their feedback is more valuable than another week of solo testing. After two users, you’ll have a reliable-enough Skill to share broadly.

Documentation matters. A Skill without usage instructions gets used wrong. Write a one-paragraph note for each Skill: what it’s for, what input to provide, what the output means, and what to do if the output looks wrong. Keep it in a shared doc or the Skill’s own README.

Adoption follows usefulness. If the deal summary Skill saves managers 10 minutes per 1:1, they’ll use it every time. If the output is generic or unreliable, they’ll revert to clicking through HubSpot. The quality of the Skill is the adoption strategy.

What to build after your first five Skills

Once the core Skills are running and trusted, the next layer is Skills that talk to each other.

A rep runs the deal summary Skill before a 1:1. The manager uses the output to run the objection handling Skill against the specific objections mentioned. The post-call action Skill fires automatically after the meeting ends. Each Skill is useful individually. Together, they form a workflow that covers a full deal review cycle without manual steps.

This is where AI operations compounds. You’re not just automating individual tasks - you’re building a system where the output of one operation becomes the input of the next. The deal summary feeds the coaching conversation. The coaching conversation surfaces the objection pattern. The objection pattern improves the competitive brief. Each Skill makes the others more useful.

Start simple. Build five Skills that each save a few minutes. Then connect them.

The teams that treat Claude as a set of defined operations - not a chat window - are the ones getting compound returns from AI. Skills are how you make that shift.