AI IMPLEMENTATION FOR REVENUE TEAMS

AI IMPLEMENTATION FOR B2B REVENUE TEAMS

AI agents that finish the work, not just answer questions.

AI agents that finish the work, not just answer questions.

We design and launch Claude powered AI agents for B2B revenue teams from qualifying inbound, writing call notes into your CRM to flagging pipeline risk. Fixed scope, fixed price, live in weeks, with human approval where it matters.

Book a discovery call

Get a free 20-minute AI workflow teardown

BUILT ON CLAUDE · HUMAN APPROVAL WHERE IT COUNTS · FIXED SCOPE, FIXED PRICE

Where are you starting from

Two kinds of teams come to us. We start with the task that costs the most time, not the one that demos best.

This is your first agent

You have seen what AI can do in a demo and want one piece of real work handed over properly.

• One workflow, working, in weeks
• Guardrails from day one
• Runs inside your existing tools

Scope your first agent

This is your first agent

You have seen what AI can do in a demo and want one piece of real work handed over properly.

• One workflow, working, in weeks
• Guardrails from day one
• Runs inside your existing tools

Scope your first agent

You tried and it stalled

A pilot worked and never made it into daily use. Almost always the same cause, and it is not the model.

• Audit of what stalled and why
• Usually the data underneath
• Rebuild only what needs it

Get your setup audited

You tried and it stalled

A pilot worked and never made it into daily use. Almost always the same cause, and it is not the model.

• Audit of what stalled and why
• Usually the data underneath
• Rebuild only what needs it

Get your setup audited

What an AI implementation actually is

An AI implementation is the work between buying a model and having it do something useful.

That means deciding which tasks are worth handing over, giving the agent access to the right systems with the right permissions, defining what it can do without asking, and putting the output somewhere a person will actually see it. Most of the work is not the model. It is the plumbing, the permissions and the place the answer lands. A pilot proves the model can do it. An implementation means it still runs on a Tuesday when nobody is watching.

What an AI implementation actually is

An AI implementation is the work between buying a model and having it do something useful.

That means deciding which tasks are worth handing over, giving the agent access to the right systems with the right permissions, defining what it can do without asking, and putting the output somewhere a person will actually see it. Most of the work is not the model. It is the plumbing, the permissions and the place the answer lands. A pilot proves the model can do it. An implementation means it still runs on a Tuesday when nobody is watching.

AGENT ANATOMY

01

Trigger

→

02

Read

→

03

Decide

→

04

Act

→

05

Write back

HUMAN APPROVES

Anything customer-facing or irreversible.

An automation would stop at Act. The agent decides what to do first, and asks when it should.

AGENT ANATOMY

01

Trigger

→

02

Read

→

03

Decide

→

04

Act

→

05

Write back

HUMAN APPROVES

Anything customer-facing or irreversible.

An automation would stop at Act. The agent decides what to do first, and asks when it should.

IMPLEMENTATION SCOPE

What we build

01

Inbound qualification

Every form submit enriched, scored and routed in seconds.

Enrichment and ICP scoring on submit

Routing to Slack and your CRM

Rep opens a lead already researched

01

Inbound qualification

Every form submit enriched, scored and routed in seconds.

Enrichment and ICP scoring on submit

Routing to Slack and your CRM

Rep opens a lead already researched

02

Call to CRM

Transcripts become structured deal records.

MEDDIC or SPICED fields completed

Next steps written

Stage tied to criteria, not memory

02

Call to CRM

Transcripts become structured deal records.

MEDDIC or SPICED fields completed

Next steps written

Stage tied to criteria, not memory

03

Pipeline risk

Deals nobody has touched, surfaced before the forecast call.

Stalled deals flagged

Champions who went quiet

Alerts to the owner, not a dashboard

03

Pipeline risk

Deals nobody has touched, surfaced before the forecast call.

Stalled deals flagged

Champions who went quiet

Alerts to the owner, not a dashboard

04

Research agents

Account and person research done before the call.

Company, people and news context

Written into the record

In the format your team reads

04

Research agents

Account and person research done before the call.

Company, people and news context

Written into the record

In the format your team reads

05

Back-office routing

Documents and requests read, classified and routed.

Extraction and classification

Correct record updated

Exceptions escalated

05

Back-office routing

Documents and requests read, classified and routed.

Extraction and classification

Correct record updated

Exceptions escalated

06

Internal copilots

Your own process and knowledge, queryable.

Grounded in your docs and CRM

Answers with sources

No invented policy

06

Internal copilots

Your own process and knowledge, queryable.

Grounded in your docs and CRM

Answers with sources

No invented policy

Automation, agent, or copilot

Most real systems use all three. Anyone selling you only agents is selling you their product, not your answer.

Type

What it does

When it is the right answer

Automation

Follows a fixed rule. When X, do Y.

The steps never vary and the data is clean. Cheapest and most reliable.

AI agent

Reads context, decides, acts, writes back.

The input is messy or the next step depends on judgment.

Copilot

Answers a person who asked.

A human is already in the loop and needs speed, not autonomy.

Three layers of trust

Three layers of trust

01 · ACCESS

Least access, always

Every agent gets the minimum permission set for its job and nothing more.

01 · ACCESS

Least access, always

Every agent gets the minimum permission set for its job and nothing more.

02 · APPROVAL

A person on anything irreversible

Anything customer facing or irreversible is reviewed before it goes out.

02 · APPROVAL

A person on anything irreversible

Anything customer facing or irreversible is reviewed before it goes out.

03 · AUDIT

Logged, and yours

Every action recorded. We never put confidential data into public models, and Anthropic does not train on business API data by default.

03 · AUDIT

Logged, and yours

Every action recorded. We never put confidential data into public models, and Anthropic does not train on business API data by default.

How we work

Every engagement starts with a discovery call. Thirty minutes. You show us where the manual work is and what it costs. We tell you which task is worth handing to an agent, what it should never do unasked, and what the fixed scope would be.

01 · DISCOVER

Map the workflow

The manual work, the systems it touches, and the fixed scope before anything is built.

The manual work, the systems it touches, and the fixed scope before anything is built.

02 · DEFINE

Define what the agent may do unasked

Guardrails, permissions, approval points, and where the output lands are signed off first.

Guardrails, permissions, approval points, and where the output lands are signed off first.

03 · PROVE

Prove it on a slice of real data

Ten records before ten thousand. The output is reviewed before anything writes at volume.

Ten records before ten thousand. The output is reviewed before anything writes at volume.

04 · LAUNCH

Train, launch, monitor

Your team trained, documentation yours to keep, and monitoring that catches what real usage reveals.

Your team trained, documentation yours to keep, and monitoring that catches what real usage reveals.

Book a discovery call

What it costs

We price the workflow, the systems it touches, and the permissions it needs. The scope and the number are fixed before anything is built.

AGENT BUILD

From $6,000

Typical fixed-scope build, live in weeks. Audits start lower. Multi-agent systems sit above. No hourly billing, no scope creep.

AGENT BUILD

From $6,000

Typical fixed-scope build, live in weeks. Audits start lower. Multi-agent systems sit above. No hourly billing, no scope creep.

THE REAL RETURN

Hours nobody notices

Work that used to need a person, handled around the clock, with a record of every decision.

THE REAL RETURN

Hours nobody notices

Work that used to need a person, handled around the clock, with a record of every decision.

Who we work with

Seed to Series B B2B teams and VC firms. Roughly ten to a hundred and fifty people. Founders still running sales themselves, heads of sales inheriting a CRM nobody maintains, and first RevOps hires who know what they want built and need the hands.

When this is worth it

The manual work is measured in days a month, not hours. The task involves reading something messy. Your CRM is good enough that an agent can trust it, or you are willing to fix that first.

When this is worth it

The manual work is measured in days a month, not hours. The task involves reading something messy. Your CRM is good enough that an agent can trust it, or you are willing to fix that first.

When it is not

If you want a website chatbot, buy one, they are good and cheap now. Under ten people with two hours a week of manual work, do it manually. If your CRM is a spreadsheet, an agent on a spreadsheet is a worse spreadsheet, and we should talk about the layer underneath.

When it is not

If you want a website chatbot, buy one, they are good and cheap now. Under ten people with two hours a week of manual work, do it manually. If your CRM is a spreadsheet, an agent on a spreadsheet is a worse spreadsheet, and we should talk about the layer underneath.

Guides for the next decision.

We write up what we learn. Start with the question closest to the work you are trying to hand over.

GTM engineering →

CRM implementation playbook →

AI implementation consultant →

FAQ

""

What is the difference between an AI agent and an automation?

An automation follows fixed rules. When X happens, do Y. An agent reads context, makes a judgment and picks the next step, like qualifying a lead from an email thread or pulling structure out of a messy document. Most real systems use both. Automations for the predictable steps, agents for the decisions.

""

What is the difference between an AI agent and an automation?

An automation follows fixed rules. When X happens, do Y. An agent reads context, makes a judgment and picks the next step, like qualifying a lead from an email thread or pulling structure out of a messy document. Most real systems use both. Automations for the predictable steps, agents for the decisions.

""

Can Claude connect to our CRM?

Yes. We connect Claude to Attio, HubSpot and Salesforce so it can read records, update fields and route work, with scoped permissions and a person approving anything that matters. On Attio that runs through MCP.

""

Can Claude connect to our CRM?

Yes. We connect Claude to Attio, HubSpot and Salesforce so it can read records, update fields and route work, with scoped permissions and a person approving anything that matters. On Attio that runs through MCP.

""

What does an AI implementation cost?

Scoped as a fixed price after a free discovery call. What moves the number is how many workflows go live and how deep the system integration goes. No hourly billing and no change orders.

""

What does an AI implementation cost?

Scoped as a fixed price after a free discovery call. What moves the number is how many workflows go live and how deep the system integration goes. No hourly billing and no change orders.

""

How long before something is actually running?

Days to a few weeks, not months. You see a working version on your own data before you commit to the full build.

""

How long before something is actually running?

Days to a few weeks, not months. You see a working version on your own data before you commit to the full build.

""

Is our data safe with AI agents?

Agents get the minimum access they need, important outputs get human review, and we never use your confidential data to train public models. We build on Claude, and Anthropic does not train on business API data by default.

""

Is our data safe with AI agents?

Agents get the minimum access they need, important outputs get human review, and we never use your confidential data to train public models. We build on Claude, and Anthropic does not train on business API data by default.

""

What happens when the agent gets something wrong?

It will, occasionally. That is why irreversible actions need approval, why everything is logged, and why we test on a small slice before anything runs at volume. The question is not whether it makes mistakes. It is whether a mistake can do damage before a person sees it.

""

What happens when the agent gets something wrong?

It will, occasionally. That is why irreversible actions need approval, why everything is logged, and why we test on a small slice before anything runs at volume. The question is not whether it makes mistakes. It is whether a mistake can do damage before a person sees it.

Start with the layer that is costing you most.

Thirty minutes. You tell us where the system is breaking. We tell you which layer to fix first and what it would take.

Book a discovery call →