AI Systems for Revenue Operations
The AI layer your CRM is missing.
Stop losing hours to pipeline admin. I design and install small, secure AI automations that keep your revenue data clean, accurate, and hands-free — inside the tools your team already uses.
No platform migration. No model training. No twelve-month roadmap. One bottleneck at a time, measured in hours returned to the people who sell.
Before & after
The manual work these systems remove
Each row is a real bottleneck I've seen inside revenue teams — and the small system that removes it. Impact varies by team; these describe the type of gain, not a guaranteed number.
| Before — the slow way | After — the system | Impact |
|---|---|---|
| Reps spend hours each week retyping discovery call notes into the CRM — and half of it never gets logged. | A call-parsing layer transcribes the call, maps the answers to your CRM fields, and updates the record before the rep closes the tab. | More selling hours, cleaner deal records |
| Inbound leads sit in a shared inbox until someone manually reads, tags, and assigns them. | A lightweight model reads each message, scores intent, tags the request type, and routes it to the right owner in seconds. | Faster speed-to-lead |
| Nobody notices a deal has gone quiet until the forecast call, when it's already too late to save it. | A nightly job scans pipeline activity and flags stalled deals, missing next steps, and blank close dates straight into your pipeline review. | Fewer silent losses |
| Contact and company data drifts — duplicate records, blank industries, inconsistent stage names, unreliable reporting. | A cleanup layer de-dupes, normalizes, and enriches records on write, so the dashboard reflects reality instead of guesswork. | Reporting you can trust |
| Proposals and invoices get checked line by line against what was actually agreed in the contract. | Document extraction pulls the line items, flags anything that doesn't match, and pushes only clean records forward. | Faster cash collection |
| Follow-up depends on whoever remembers. Some leads get five touches, some get none. | The system drafts the next follow-up from the actual conversation history and queues it for one-click send. | Consistent follow-through |
Before
Reps spend hours each week retyping discovery call notes into the CRM — and half of it never gets logged.
After
A call-parsing layer transcribes the call, maps the answers to your CRM fields, and updates the record before the rep closes the tab.
More selling hours, cleaner deal records
Before
Inbound leads sit in a shared inbox until someone manually reads, tags, and assigns them.
After
A lightweight model reads each message, scores intent, tags the request type, and routes it to the right owner in seconds.
Faster speed-to-lead
Before
Nobody notices a deal has gone quiet until the forecast call, when it's already too late to save it.
After
A nightly job scans pipeline activity and flags stalled deals, missing next steps, and blank close dates straight into your pipeline review.
Fewer silent losses
Before
Contact and company data drifts — duplicate records, blank industries, inconsistent stage names, unreliable reporting.
After
A cleanup layer de-dupes, normalizes, and enriches records on write, so the dashboard reflects reality instead of guesswork.
Reporting you can trust
Before
Proposals and invoices get checked line by line against what was actually agreed in the contract.
After
Document extraction pulls the line items, flags anything that doesn't match, and pushes only clean records forward.
Faster cash collection
Before
Follow-up depends on whoever remembers. Some leads get five touches, some get none.
After
The system drafts the next follow-up from the actual conversation history and queues it for one-click send.
Consistent follow-through
The pragmatic AI stack
Proven components, assembled carefully
Nothing here is experimental. These systems are built from established models, well-documented connectors, and your existing source of truth.
The brains
Commercial models through secure APIs — Anthropic, OpenAI, or an open-weight model when the data needs to stay in-house. No custom model training, no research project.
The connectors
Make.com, Zapier, n8n, or plain Python where the logic needs to be exact. These are the bridges between your tools, not another platform to log into.
The destination
Everything lands in your source of truth — HubSpot, Salesforce, Attio, Pipedrive, or the spreadsheet you actually run on. No new dashboard nobody opens.
Security & cost
The two questions every buyer asks first
Your data stays yours
Every integration runs through opt-out endpoints, so your CRM records are never used to train public models. Access is scoped to the specific objects the automation touches — nothing broader. If a workflow shouldn't leave your environment, it runs on an open-weight model inside it.
No infrastructure bill
These are micro-systems on consumption-based APIs. A call summarizer or a routing rule costs cents per execution, not a platform license. There's no cluster to provision and nothing to keep running when you're not using it.
It fails visibly, not silently
Every automation logs what it changed and why. Anything it isn't confident about goes to a human queue instead of writing a wrong value into your pipeline. You can turn any layer off without breaking the system underneath it.
How a build runs
One bottleneck, then the next
- 1
Map the manual work
We time the repetitive steps in your current revenue process and rank them by hours burned and error rate. Most teams find three or four worth automating and a dozen that aren't.
- 2
Pick one bottleneck
The first build is always the single highest-leverage one. One workflow, scoped tight, shipped in days — not a platform rollout.
- 3
Build it into the tools you already use
The automation lives inside your CRM and inbox. Your team's day doesn't change; the admin work just stops appearing.
- 4
Measure, then extend
We track the hours returned and the data quality before adding the next layer. If a system doesn't earn its place, it gets removed.
FAQ
Questions about AI systems
Is this an AI transformation project?+
No. These are small, single-purpose automations built into the tools you already run on. Each one targets a specific piece of manual work — call logging, lead routing, data cleanup — and can be switched off independently.
How long does one system take to build?+
A scoped micro-automation is typically live within one to two weeks. Larger workflows that touch several systems take longer, but nothing is built as a multi-month program.
Will our customer data be used to train AI models?+
No. Integrations run through opt-out API endpoints, and access is scoped to only the objects a given automation needs. Where data cannot leave your environment, the workflow runs on an open-weight model inside it.
What does it cost to run?+
These systems use consumption-based APIs, so a typical automation costs cents per execution rather than a fixed platform license. There is no infrastructure to provision or maintain.
What if the AI gets something wrong?+
Low-confidence outputs go to a human review queue instead of writing into your CRM. Every action is logged, so you can see exactly what changed and why.
Find the manual work worth automating
Send me your current stack and where your team loses time. I'll come back with the one automation worth building first — and what it would actually cost to run.
- No obligation and no sales sequence
- Reply within 48 hours, usually same business day
- You keep the recommendation either way