With the workflow audit. It maps where your team's hours actually go and which processes are defined enough to automate safely. Some teams discover their highest-value deployment is mundane - CRM hygiene or research briefs - and that's fine. Hours returned are hours returned.

AI Agents · GTM Systems

AI Agents

Q: What about agents making mistakes in front of customers?

That risk is why deployments are designed with human-in-the-loop controls: approval gates before anything external, override points at judgment calls, and audit trails for everything an agent does. Agents prepare and execute the mechanical steps; people stay at the decision points that carry commercial or compliance risk.

Q: Who owns an agent once it's deployed?

A named person on your team - agreed before deployment, not after. Ownership means someone reviews the agent's output, maintains its instructions as your business changes, and has the authority to switch it off. Deployments without an owner degrade silently, which is worse than no automation at all.

Agents and skills deployed into your commercial workflows - on platforms like Relevance.ai and Dust.tt, or custom-built on the Claude Agent SDK - so your GTM systems run without adding headcount.

Talk through agent deployment See all GTM systems

Built on

Relevance.ai Dust.tt Claude Agent SDK LangChain CrewAI

Sound familiar?

Your targets go up every year. Your headcount and budget don't.
Your team's week goes to research, admin and follow-up - the work that doesn't need them, at the expense of the work that does.
AI should be closing that gap. The demos are everywhere; the pipeline impact isn't.
The agent projects that do start rarely survive contact with real data, so the spend goes in and nothing compounds.

What deploying agents changes

The commercial case is simple: the same team produces more, faster, to a consistent standard - and the hours they get back go to revenue work.

Output without headcount

The research, enrichment, follow-up and reporting that fills your team's week runs in the background. Their time goes to selling, strategy, and the judgement calls that need a person.

Speed that wins deals

A buyer signal acted on in minutes beats the same signal worked next week. Agents close the gap between something happening in your market and your team responding to it.

A consistent standard, every account

Every account gets the same quality of prep, follow-up and hygiene, not whatever your busiest rep had time for that day.

Most agent projects stall before they deliver any of this. Mine are built to stay in production - owned, measured, and safe to run in a regulated business - which is the difference between hours returned and money spent.

What I deploy

Working agents and skills, shipped into the tools your team already uses. Platform-agnostic: sometimes that's Relevance.ai or Dust.tt, sometimes your existing stack can do the job without a new subscription.

Workflow audit

A map of your commercial workflows against what's worth automating.

Platform selection

No-code platforms like Relevance.ai and Dust.tt for speed. Custom builds on the Claude Agent SDK, LangChain or CrewAI where the workflow needs more control. The design and your security requirements decide, not the vendor.

Skill building

The reusable building blocks - prompts, tools, data connections - that make agents useful.

Deployment with guardrails

Agents shipped into production with override points, approval gates and audit trails.

Team handover

Your team runs it when I leave. Documentation and training included to ensure smooth handover.

Measurement

Hours returned and output shipped, tracked per agent.

Where agents earn their keep

The highest-value deployments in commercial teams are rarely the flashiest. These are the workflow categories I see returning the most hours:

Research and enrichment

Account research, contact enrichment and pre-call briefs assembled before your team asks for them.

Signal-triggered drafting

When a qualifying signal fires, the agent drafts the outreach - your rep reviews and sends.

Content repurposing

Turn one piece of content into the posts, emails and snippets each channel needs, drafted in your voice for a quick review before publishing.

CRM hygiene

Records updated, duplicates flagged, fields auto filled.

Reporting and monitoring

Pipeline movements, campaign performance and anomalies summarised and delivered each Monday.

Process orchestration

Multi-step workflows - handoffs, follow-ups, approvals - run on schedule with humans in the loop.

Who this is for

CEOs, CROs and CMOs at B2B SaaS and fintech companies with defined commercial workflows worth automating. If your processes aren't defined yet, start with the growth audit: it will tell you what to fix before you automate anything.

Design the deployment first

A 30-minute conversation will tell you which of your workflows are ready for agents, which need defining first - and whether you need a platform at all.

Talk through agent deployment Or train your team first

Frequently asked questions

Which platforms do you build on?

No-code platforms like Relevance.ai and Dust.tt when speed matters, and custom builds on the Claude Agent SDK, LangChain or CrewAI when the workflow needs more control. The platform is the last decision in the design: the workflow determines the tool, and in some builds the stack you already run covers it.

What about agents making mistakes in front of customers?

That risk is why I design human-in-the-loop controls into every deployment: approval gates before anything external, override points at judgement calls, and audit trails for everything an agent does. Agents prepare and execute the mechanical steps, while people stay at the decision points that carry commercial or compliance risk.

Who owns an agent once it's deployed?

A named person on your team, agreed before deployment. Ownership means someone reviews the agent's output, maintains its instructions as your business changes, and has the authority to switch it off. An agent without an owner drifts out of date, which is worse than no automation at all.

How does this relate to the AI Training Programme?

They compound. The Training Programme raises your team's fluency so they can work with and extend what's deployed. Agent deployment ships the systems themselves. Teams that do both end up owning their automation rather than depending on whoever built it.

Where do we start?

With the workflow audit. It maps where your team's hours go and which processes are defined enough to automate safely. From there, the first deployment is whichever gives your team the most time back.