Your systems
Agents work in the CRM, email, calendar and documents you already have. Nothing to rip out.
What agents do
AI agents that do the work, in your systems.
A chat answers you. An agent works for you. It uses a computer, your files and your tools to get the job done.
PromptPartner deploys AI agents in mid-sized and larger companies in Europe, inside the systems your team already uses. Claude or ChatGPT on your desktop, Claude Code and Codex are all agents. That is where we start.
Agents
do the work, not just chat
Rules set once
by Legal, IT and brand
Office hours
weekly, while we build
What is an AI agent?
A brain + hands + a notebook + an alarm clock + keys.
The brain
Understands what you ask, thinks it through and decides the next step.
Technically: the AI model (LLM)
Hands
It uses a computer the way you do: it opens programs, types and clicks.
Technically: access to the operating system, via the command line or the screen
Keys
It opens your tools: email, calendar, CRM and documents.
Technically: connections to your apps (APIs and MCP)
A notebook
It reads your files, writes new ones and remembers what it did.
Technically: access to the file system, for memory and data
An alarm clock
It can start on its own, on a schedule, even while you are away.
Technically: scheduled tasks
You probably use one already.
- Claude desktop app
- ChatGPT desktop app
- Claude Code
- Codex
Custom agents, built for one job, come later, once these run well.
Read our guide on agentic AI vs generative AI: the practical difference.
01From chat to agents
Agents work inside the tools you already use.
A chat window helps one person with one task. Agents connect to your systems, follow your rules, and do the work where it happens.
Agents
- Salesforce
- Microsoft 365
- SharePoint
- Slack
- HubSpot
- Zuora
Your rules
Legal, IT and brand set the rules once. Read-only first, with access you control.
Your team
Agents do the admin. Your people check the result and keep the work only people can do.
02Examples · Go-to-market · Often where we start
In go-to-market, the return often shows fastest.
Growth engines
- Outbound (signal + intent)
- Content engine (blog, newsletter, social)
- AI SDR assist (classify, book, route)
- Speed-to-lead (first touch in seconds)
- Lead scoring (fit + intent, explained)
Revenue ops
- Pipeline hygiene (scoring, routing, forecast)
- Deal origination (research + outreach)
- Proposal drafts (quotes and proposals)
- RFP assistant (from your knowledge base)
- Pipeline analytics (coverage, slippage, churn)
Partner + market
- Partner ops (recruit, onboard, activate)
- Market intel (M&A, funding, moves)
- Proof engine (stories to campaign assets)
- Competitive intel (live battlecards)
- Whitespace scanner (where to sell next)
Orchestration
- Meeting intel (actions, summaries, CRM sync)
- Dashboards (pipeline + engine health)
- Knowledge base (unified search)
- Voice AI (call summaries, coaching)
- Scheduled agents (daily, or when something happens)
The audit picks what goes first. Use cases go live one at a time, not all at once.
Software or SaaS company? See AI for software and SaaS companies.
03Examples · Every team
Not only go-to-market. Any team.
Finance
- Close narrative
- Invoice chase
- Forecast assist
- Reporting pack
- Spend watch
Legal + compliance
- NDA triage
- Contract review
- Compliance check (EU AI Act, GDPR)
- Vendor check
- Data requests (GDPR)
Customer + support
- Ticket triage
- Response drafting (in your voice, cited)
- KB articles
- Escalation packs
- Customer research
HR, ops + leadership
- Onboarding packs
- Policy lookup
- Process docs (SOPs + RACI)
- Exec daily brief
- Board pack assist
The audit works for any team: go-to-market, Legal, Finance, HR, R&D, Manufacturing. Every new use case follows the same rules.
04A view for every role
A dashboard for every role.
Customer-360
For the AE and SE.
Cockpit
For the SDR.
Demand board
For Marketing.
Health board
For the CSM.
Channel board
For the Partner manager.
Exec board
For Leadership.
Read-only, pulled straight from your systems. One board per role.
05Mechanics, not magic
Sixteen manual steps, one agent loop.
This is what AI automation looks like in practice: sixteen manual steps become one loop. From the first signal to a signed deal. Today your team does each step by hand:
- 01Source account
- 02Enrich contacts
- 03Verify emails
- 04Score fit
- 05Pull signals
- 06Match playbook
- 07Generate copy
- 08Personalize
- 09Schedule send
- 10Send + track
- 11Classify reply
- 12Book meeting
- 13Update CRM
- 14Alert rep
- 15Log outcome
- 16Retrain loop
One agent loop, with your rules at every step. Step 16 feeds back into step 01. Your team starts the day with warm leads, not a cold list.
149 signal types
Every account watched for buying signals.
75+ data points
Per account, before a single message goes out.
Measured
Every stage measured. Every lost deal feeds back into the loop.
Every hand-off logged, every number traceable.
Read our guide on AI automation for mid-sized companies: where to start.
06Around sales: approvals
One front door for approvals, in Teams or Slack.
A request comes in. An agent sorts it, drafts an answer and sends it to the right person. A person decides, within an agreed time. Later, small requests under a set limit can be approved on their own. No more chasing sign-offs in chat.
- 1
Request in
Teams or Slack
- 2
An agent sorts it
Drafts and routes it, with context
- 3
A person decides
Within an agreed time, logged
07Example dashboards
What an agent looks like when it’s running.
Each agent works inside the systems you already use and reports into one view. Pick one to see the shape of the work. These three dashboards are examples. Every number in them is sample data, not a client result. A real dashboard is built for each client.
AI Content EngineExample data, not client results
Example
128
Assets shipped / mo
Example
14
In review
Example
3.2h
Avg time to publish
Example
+41%
Engagement lift
Output — last 8 weeks Example
Recent assets Example
| Asset | Channel | Status |
|---|---|---|
| Q3 launch narrative | Blog | Live |
| Comparison vs legacy | Web | Live |
| Webinar follow-up series | Review | |
| Founder POV thread | Draft |
AI Outbound EngineExample data, not client results
Example
3,410
Leads enriched
Example
12
Active sequences
Example
9.4%
Reply rate
Example
47
Meetings booked
Outbound funnel — this month Example
Sent3,410
Opened1,740
Replied321
Booked47
Active sequences Example
| Sequence | Leads | Stage |
|---|---|---|
| SaaS FR – Series B | 420 | Replied |
| DACH ISV expansion | 610 | Meeting |
| PE portfolio RevOps | 280 | Sequenced |
| Enterprise reactivation | 350 | Opened |
AI RevOps EngineExample data, not client results
Example
18,200
Records standardized
Example
€4.6M
Pipeline value
Example
92%
Forecast accuracy
Example
96%
Data-quality score
Pipeline by stage Example
Data-hygiene alerts Example
| Deal | Stage | Flag |
|---|---|---|
| Northwind – renewal | Negotiation | Clear |
| Acme GmbH – upsell | Proposal | Missing ICP |
| Vertex – new logo | Qualified | Dup contact |
| Orbit Ltd – expansion | Discovery | Clear |
Runs on the leading AI models
08Questions
Questions about agents.
What is an AI agent?
A chat answers you. An agent works for you. It uses a computer, your files and your tools to get the job done: a brain, plus hands, a notebook, an alarm clock and keys to your tools.
Is ChatGPT an AI agent?
It can be. Claude or ChatGPT on your desktop, Claude Code and Codex all work as agents when they use your files and tools. That is where we start. Custom agents, built for one job, come later, once these run well.
What can an AI agent do for my business?
The work nobody should do by hand: research, follow-ups, reports, data clean-up, approvals. In go-to-market the return often shows fastest. We use the same approach for Finance, Legal, HR and support.
Do agents replace people?
No. Agents do the admin. Your people check the result and keep the work only people can do. Superpowers, not replacements.
Are agents safe to run in our systems?
Only with rules. A hacked website shows you spam. A hijacked agent shows your mailbox and your CRM. So Legal, IT and your brand team set the rules once, agents start read-only, and a person approves the important steps.
Which use case should we start with?
The audit decides. It scores every use case on impact and difficulty, and the quick wins go first. Use cases go live one at a time, not all at once.
09To start
See it working before you decide.
No pitch. You’ll leave with at least one recommendation you can use — whether or not we ever work together.