We have one RevOps person. Do you replace them or work with them?

Work with them, and make them look great. Your RevOps lead keeps owning process and priorities; we add the Salesforce admin, development, architecture, and integration capacity a single hire can't cover, with AI augmenting how the work gets delivered. Most of our best engagements are exactly this pairing: one internal operator with GAT's broader systems practice alongside them.

When should a startup move from HubSpot to Salesforce?

When deal complexity outgrows it: multi-stakeholder deals, approval steps, quoting, territory routing, or board reporting that HubSpot's object model fights you on. Often the right answer is both: Salesforce as the sales system of record with HubSpot kept for marketing, properly integrated. We'll tell you honestly if you're not there yet.

Our org was set up by a founder. Rebuild or repair?

Usually repair. Most founder-built orgs have a salvageable core buried under improvised fields and dead automation. We audit first, redesign the data model and stages in place, and reserve full rebuilds for orgs that are genuinely unsafe to extend. It's the faster and cheaper path more often than agencies like to admit.

What does AI-augmented managed services actually mean?

AI does the toil, seniors make the calls. In the security SaaS org we run today, an AI admin SOP handles documentation, monitoring, and routine changes daily, inside guardrails, with a senior consultant reviewing and owning every outcome. You get more coverage per dollar without junior staffing, and an org that stays documented as it changes.

Can AI really keep our pipeline clean?

Yes, when it's governed. We deploy agents that flag stale opportunities, chase missing next steps, draft stage-appropriate follow-ups, and propose field updates a human approves. We run the same automation on our own pipeline. It's how a deliberately small senior firm operates like a bigger one.

Scope drives price. Book a strategy briefing to talk with a senior consultant, get a fit assessment for your stack and stage, and leave with a prioritized plan whether or not we work together.

B2B SaaS · Revenue operations

Revenue systems for B2B SaaS teams.

For the RevOps leader who owns the number and the systems behind it: pipeline architecture, routing, quote-to-close, and reporting your board can read.

Book a strategy briefing See the managed-services proof ↓

Outcomes from systems we built and ran

$10K → $1M/mo

Monthly revenue grown on systems we built and ran

36×

More of the same traffic turned into customers

+50%

Half again as many leads converted, same traffic

Three ways in

Pick the door that matches your problem

Same senior practice behind all three. The door just decides where we start.

Door one

Security & infrastructure SaaS

Revenue orgs in security companies carry real compliance weight: audit trails, access discipline, and change control your buyers would recognize. We run AI-augmented managed services inside one today.

Org documentation your next hire can actually read
CI/CD and release discipline for the revenue org
AI doing the admin toil, seniors making the calls

See it in production ↓

Door two

B2B SaaS revenue operations

The classic Series A-C problem: the founder-built CRM stops scaling right as the board starts reading it. We rebuild pipeline, routing, quoting, and reporting as one system.

Funnel and stage architecture matched to how deals close
Routing and follow-up automation, speed-to-lead measured
Pipeline coverage and ARR reporting without a disclaimer slide

Explore revenue systems →

Door three

AI adoption inside your company

Claude Cowork and ChatGPT Work rollouts that change how the team works, not another unused license: governed access, real workflows, and adoption someone owns.

Workspace rollout with role-based use cases
Governed connections to Salesforce and company knowledge
Playbooks your team keeps after we leave

Explore AI adoption →

In production today

AI-augmented managed services at a security SaaS company

Not a pilot and not a demo: a revenue org we operate today, documented, release-disciplined, and worked daily by AI inside guardrails a senior consultant owns.

15,860

metadata components documented

216

flows mapped and owned

Shipped

CI/CD pipeline for the revenue org

Daily

AI admin SOP in production

They handed us the org their revenue ran through: built fast over years, by people who had left, with no documentation and nowhere safe to test. We mapped the whole thing before changing anything, resolved the conflicts the mapping surfaced (two systems fighting over deal credit, two more assigning owners to the same records), and left them a written source of truth their next hire can actually read. Now an AI admin SOP works the org daily and every change ships through a pipeline.

The full operating model, including what the AI does and what humans decide, is written up in the open.

Read the managed-services model →

Outcomes

What happened when the systems changed

Every number here comes from an engagement we delivered and ran. The story matters more than the stat.

$10K → $1M/mo

Monthly revenue grown on systems we built and ran

A services business grew monthly revenue from $10K to $1M on the systems we designed and operated: leads routed to the right person immediately, follow-up that happens whether or not anyone remembers, and numbers leadership can act on.

36×

More of the same traffic turned into customers

Nothing changed about where the leads came from. We rebuilt what happened after they arrived (who picks them up, how fast, and what happens if no one does) and conversion multiplied 36 times.

+50%

Half again as many leads converted, same traffic

Follow-up stopped depending on someone remembering. The team converted 50% more leads from the same spend.

AI adoption · Claude Cowork + ChatGPT Work

Roll AI out to the whole company, not just the CRM

Workspace rollout, role-based use cases, governed access to company knowledge, and workflows people actually repeat. We run this operating model at a live SaaS client today, so the playbook comes from production, not a slide.

Explore AI adoption services Claude + Salesforce

Claude Cowork

Deep work, finished documents, and agentic execution with MCP connections to the systems your team already runs.

ChatGPT Work

Business and enterprise rollout with governed knowledge access, approved actions, and repeatable team workflows.

Claude and ChatGPT marks identify supported AI work surfaces. We are an independent consultancy; no vendor endorsement is implied.

Research before the call

How we actually run these systems

The architectures and operating models behind this page, written up for your team to inspect.

Architecture

Lead routing architecture that survives scale

The routing model behind measured speed-to-lead: territories, round-robin, PQL signals, and what breaks each of them.

Open resource →

Operating model

AI-augmented Salesforce managed services

How a senior practice uses AI to run an org: what the AI does, what humans decide, and the controls in between.

Open resource →

Hands-on guide

Claude on Salesforce admin tasks

The daily admin work Claude handles in a real org, and the guardrails that make that safe.

Open resource →

Hands-on guide

ChatGPT on Salesforce admin tasks

The same operating discipline applied through ChatGPT: approved actions, scoped access, human review.

Open resource →

RevOps questions

What RevOps and revenue leaders ask before they engage

We have one RevOps person. Do you replace them or work with them?: Work with them, and make them look great. Your RevOps lead keeps owning process and priorities; we add the Salesforce admin, development, architecture, and integration capacity a single hire can't cover, with AI augmenting how the work gets delivered. Most of our best engagements are exactly this pairing: one internal operator with GAT's broader systems practice alongside them.
When should a startup move from HubSpot to Salesforce?: When deal complexity outgrows it: multi-stakeholder deals, approval steps, quoting, territory routing, or board reporting that HubSpot's object model fights you on. Often the right answer is both: Salesforce as the sales system of record with HubSpot kept for marketing, properly integrated. We'll tell you honestly if you're not there yet.
Our org was set up by a founder. Rebuild or repair?: Usually repair. Most founder-built orgs have a salvageable core buried under improvised fields and dead automation. We audit first, redesign the data model and stages in place, and reserve full rebuilds for orgs that are genuinely unsafe to extend. It's the faster and cheaper path more often than agencies like to admit.
What does AI-augmented managed services actually mean?: AI does the toil, seniors make the calls. In the security SaaS org we run today, an AI admin SOP handles documentation, monitoring, and routine changes daily, inside guardrails, with a senior consultant reviewing and owning every outcome. You get more coverage per dollar without junior staffing, and an org that stays documented as it changes.
Can AI really keep our pipeline clean?: Yes, when it's governed. We deploy agents that flag stale opportunities, chase missing next steps, draft stage-appropriate follow-ups, and propose field updates a human approves. We run the same automation on our own pipeline. It's how a deliberately small senior firm operates like a bigger one.
What does this cost?: Scope drives price. Book a strategy briefing to talk with a senior consultant, get a fit assessment for your stack and stage, and leave with a prioritized plan whether or not we work together.

Strategy briefing

Build the revenue system your next round expects.

Book a strategy briefing See the work

Free, 30 minutes, no obligation.

You leave with
An org health read: what is solid, what is fragile, the cost
An AI-readiness map for your real workflows
A prioritized roadmap with a fixed quote for the next phase

$10K → $1M/moMonthly revenue grown on systems we built and ran