Outreach data systems for GTM engineering agencies and sales teams
Your campaigns stop starting from scratch.
We stop campaigns from starting over and reclaim the hours lost to list work for GTM agencies, lead generation agencies, and B2B sales teams so they can scale while stabilizing costs, without rebuilding a system or adding yet another tool.
Take on more clients without hiring more people for delivery.
One data warehouse, five layers
- 01Ingestion
- 02Identity and enrichment
- 03Qualification
- 04Activation
- 05Feedback and reporting
Works with your stack
Instantly
EmailBison
HeyReach
Clay
Zevari
ListKit
Smartlead
Apollo
HubSpot
Claude
Prospeo
Salesforce
Lemlist
PlusVibe
Findymail
FullEnrich
BetterContact
LeadMagic
Icypeas
LinkedIn Sales Navigator
Trigify
RB2B
Common Room
Unify
PhantomBuster
Aimfox
La Growth Machine
Hunter
MillionVerifier
ZeroBounce
Attio
Pipedrive
Grok
ChatGPT
n8n
Postgres
BigQuery
All 71 tools
- Instantly
- HeyReach
- Smartlead
- Lemlist
Reply.io
Salesforge
Expandi- EmailBison
- PlusVibe
- Aimfox
- La Growth Machine
- Zevari
Outreach
Salesloft
Amplemarket
Lavender- Clay
- Apollo
- LinkedIn Sales Navigator
ZoomInfo
Cognism
Lusha
LeadIQ
Seamless.ai
6sense- ListKit
- Prospeo
- Findymail
- FullEnrich
- BetterContact
- LeadMagic
- Icypeas
- Hunter
- PhantomBuster
- Trigify
- RB2B
- Common Room
- Unify
- MillionVerifier
- ZeroBounce
- HubSpot
- Salesforce
- Attio
- Pipedrive
Close
Copper
Zoho CRM
GoHighLevel
Monday.com- Postgres
- BigQuery
dbt
Airflow
Prefect
Docker- n8n
- Claude
- ChatGPT
- Grok
Gemini
Gong
Clari
Stripe
Calendly
Cal.com
Chili Piper
Gmail
Outlook
Slack
Microsoft Teams
X (Twitter)
What this actually does
Every Monday someone rebuilds the list. The same company turns up under four names. Suppression gets checked against a spreadsheet. Replies never reach the next list. Nobody is doing it wrong. Nothing holds the work between campaigns.
We install the system that holds it and run it every month. One record per company, scored on what each client converts on, with replies fed back into the scoring.
For an agency, that is a modeled 20 to 40 delivery hours a month back. Keep them as margin, or take on more clients without hiring. Internal teams get more time with buyers.
The five layers below are how it is built. You do not need every one on day one.
How it works
Five layers on one data warehouse. You buy one system. We build it in five layers and run it for you every month.
Layer 1
Ingestion
Your team stops hunting for leads across tools and spreadsheets. New business registrations, filing agents, SEC and EDGAR filings, LinkedIn, job postings, our scrapes, and your own exports. All of it lands in one data warehouse, and every record carries where it came from and when.
- New Formations and Public Filings. New business registrations, filing agents, and SEC and EDGAR filings ingested on a schedule, parsed down to the filing index, and cross-matched against your targets. Fresh companies before they are on anyone's list.
- Hiring, Funding, and Tech-Stack Signals. LinkedIn, job postings, funding rounds, new locations, and technographics pulled in on a schedule. Each signal stamped with its source and timestamp, so a lead's score is explainable.
- Bring Your Own Exports. Clay tables, Apollo and Sales Navigator exports, client spreadsheets, and old campaign lists plugged in as sources. Nothing you already paid for gets thrown away or re-keyed.
Layer 2
Identity and enrichment
Four versions of a company become one record, and only that record gets enriched. We resolve entities, dedupe, and attach firmographics and the signals that matter: hiring, funding, new locations, tech stack, intent.
- Entity Resolution and Dedupe. Fuzzy matching on name, domain, and address collapses four versions of the same company into one record. Suppression and do-not-contact run on every refresh, not once at setup.
- Waterfall Enrichment. When one enrichment source misses, the next one tries. Firmographics, contacts, and emails filled from a documented waterfall, with a written policy for what happens when a record is bad.
Layer 3
Qualification
Scoring and filtering on what each client actually converts on. A new formation that matches a public filer is a much better lead than a new formation alone. That cross-match is the edge.
- Qualification Scoring. Scoring and filters built on what each client actually converts on, not a generic ICP. Segments stay small and sharp. A human approves the model, the system applies it.
Layer 4
Activation
Ready-to-send segments land in Instantly, HeyReach, and your CRM, with suppression checked on every sync. This is the layer clients feel, because it is the one that produces calls.
- Segment Sync to Instantly, HeyReach, and the CRM. Clean, scored segments pushed to the tools you already send from, with the right fields in the right place. No CSV uploads, no copy-paste, no Monday rebuild.
- Suppression and Compliance. Opt-outs, bounces, existing customers, and competitor domains held in one suppression layer that every sync honors. CAN-SPAM and GDPR handling built in, with a clear stance on what we will not store.
Layer 5
Feedback and reporting
Your next campaign starts from what the last one learned. Replies, meetings, and closed revenue flow back in and retrain the scoring. Without this loop we are a data vendor. With it, the system gets better every month it runs.
- Reply and Revenue Feedback Loop. Replies, meetings, and closed revenue flow back from Instantly, HeyReach, and the CRM into the scoring. Weekly reporting on what converts, so each campaign starts from the last one.
- Per-Client Environments and Monitoring. One isolated environment per client, deployed through CI so a release is never a manual event. Logging and alerting on every job, so a broken source is caught before the campaign is.
AI agents that cost less and guess less
Many GTM and lead generation agencies put one large model on every step. The token bill climbs, the wrong model does the wrong job, and the output starts to invent facts. We design the agent architecture around your workflow, then run it.
- 01
Code
Dedupe, joins, filters, and scoring rules. Deterministic, testable, and zero tokens.
Most steps
- 02
Small model
Classify, extract, and tag. Fast, cheap, and checked against a schema.
Some steps
- 03
Large model
Research and writing that need real judgment. Called last, and only there.
Fewest steps
Model choice
What we seeOne frontier model runs every step, from tagging a job title to writing the email.
What we buildEach step gets the smallest model that passes its test. A cheaper model goes in only when the evals say it holds up.
Code first
What we seeAn agent does work a SQL query can do, and gets it slightly wrong each time.
What we buildDedupe, matching, filters, and scoring stay in code. Agents handle only the steps that need judgment.
Context
What we seeWhole CRM exports and long instructions are pasted into every prompt.
What we buildEach call gets only the fields it needs from the warehouse. Shorter prompts cost less and drift less.
Grounding
What we seeThe model fills gaps with facts that sound right: a funding round, a job title, a tool the company never used.
What we buildAgents read from the warehouse and cite the record they used. Output is checked against a schema, and a claim with no source is dropped before it reaches a sequence.
Reuse
What we seeThe same company is researched from scratch for every campaign.
What we buildResults are stored once and reused. Stable instructions are cached, and work that can wait runs as cheaper batch jobs.
Cost visibility
What we seeNobody knows what one run costs until the invoice arrives.
What we buildToken use and cost are logged per job and per client, so every workflow has a number and an owner.
Same rule as the data layer: if a step does not need a model, it does not get one.
- 01
Week one: map the stack.
We walk your current stack and list every place a human re-enters data by hand. That list is the scope. Nothing else is.
- 02
Weeks two and three: ingestion and identity.
We build the ingestion and identity layers for your sources, and nothing else. One pipeline you can watch beat your spreadsheet is worth more than a slide deck.
- 03
Week four: activation and the loop.
We turn on the sync to Instantly, HeyReach, and the CRM, connect replies and meetings back into scoring, and hand over the reporting. The second install is faster than the first.
One outbound agency, modeled end to end
Illustrative example, not a client
Here is what Monday looks like at most outbound agencies. Someone exports last week's list, pastes it next to the new one, and starts deduping. The same company is in there four times under four names. A new client campaign that should take days takes two weeks, and most of that is list work.
Before
- Every Monday someone rebuilds the list by hand. The same company shows up under four names across Clay, the CRM, and three client sheets.
- A new client campaign takes two weeks to go live, and most of that is list work, dedupe, and suppression checks.
- Replies and meetings live in Instantly and HeyReach. Nobody feeds them back, so next month's targeting is the same guess as last month's.
After we install the system
- Filings, registrations, LinkedIn, job postings, and the client's own exports land in one place. Every record is stamped with where it came from and when.
- One record per company, filled in by waterfall enrichment, scored on what that client converts on, and synced as a clean segment to Instantly, HeyReach, and the CRM.
- Replies, meetings, and closed revenue flow back in and retrain the scoring. Each campaign starts from the last one, not from scratch.
- Modeled: 20 to 40 delivery hours a month come back from list work. Keep them as margin, or take on the next client without hiring for delivery. The list lives in the system instead of in one person's head, so a delivery lead out for a week no longer delays a launch.
Modeled targets, not results
Days
to take a new client campaign live, not weeks
One
record per company, with provenance, in every client environment
20 to 40
delivery hours a month back from list work, dedupe, and re-entry
100%
of replies, meetings, and closed revenue fed back into scoring
These are modeled targets, not promises. On the call we map them to your sources, volume, and client count and show you the math before you commit. On the first install we pick the one number you care about, hours back or days to launch, and measure it from day one.
Every campaign that starts from scratch pays for the last one twice.
Who this is for, and who it is not
Read both lists before you book. If the first one sounds like you, we would rather tell you now than on the call.
Who this is not for
- You send a few hundred emails a month and a spreadsheet still keeps up.
- You want someone to write copy or run campaigns. We build and run the data layer; you run outbound.
- You want to pay per lead or per meeting.
- You have no CRM or sending tool yet and are still looking for your first offer.
- You want a one-off list pull, not a warehouse and pipelines that run every month.
- You need it live next week. A clean install takes 30 days.
Who this is for
- GTM and outbound agencies, five to thirty people, running ten or more client campaigns.
- B2B sales teams whose reps rebuild lists by hand before every push.
- Solo and fractional GTM engineers who need infrastructure, not advice.
- Teams on Instantly, HeyReach, Smartlead, or a CRM who want one clean source under all of them.
- Teams that want a data warehouse of record they own, in Postgres or BigQuery.
- Teams whose AI agent workflows cost more every month and still get facts wrong.
- Teams willing to feed replies and closed revenue back, so the scoring keeps learning.
Somewhere in between?Fill in the intake and we will tell you straight
Custom builds
No packages. Every build is scoped to your sources, your clients, and your volume, then run for you every month.
Custom
Scoped to you
Boutique GTM infrastructure as a service.
For teams that want the data warehouse, the pipelines, the agents, and the models built around how they sell, then run for them every month. Tell us about your business in the intake, then pick a time to talk.
In
- The five-layer system on your stack: ingestion, identity, qualification, activation, and the feedback loop.
- A dedicated data warehouse in your cloud or ours, modeled around your accounts, contacts, and signals.
- AI agent architecture: each step on the cheapest model that passes its test, with token use logged per job.
- A proprietary source we do not already ingest: a niche registry, a paid dataset, or a scrape only you run.
- Scoring models trained on your closed-won history, including the filings cross-match.
- A migration out of a warehouse, a CRM, or a sending tool you are leaving, with the history intact.
- One isolated environment per client, with suppression, opt-outs, and provenance on every row.
- Jobs run, monitored, and improved every month, with one point of contact.
Setup fee plus monthly, kept separate and quoted after the intake and the call. We never price per lead or per meeting, because conversion depends on your copy and your offer. We commit to install and go-live dates, never to pipeline numbers.
Free resources
Two PDFs, one form. The field kit gives you seven working tools for your outbound stack. The guide shows the numbers a discovery call should capture.
Free field kit
The GTM Engineering Field Kit
Twelve pages, seven working tools for GTM agencies and startups that run outbound. Print it, fill it in, or copy the tables into your own docs.
- The One-Loop Audit: find the list, dedupe, or upload loop that stops the week you stop touching it.
- A stack map, an ICP and signal sheet, and the data hygiene and deliverability checklists to run on every refresh.
- An agent and token budget: route each AI step to code, a small model, or a large one, and price it before the bill arrives.
Free alpha
The Quantified Discovery Call
One 54-minute call, broken down to the dollar figures, the decision unit, and the dates a GTM team should capture every time.
- The three-figure cost stack the prospect named, and the four friction numbers behind it.
- The BANT table as the prospect stated it: budget, decision structure, timeline, buying process.
- Five moves to steal, and where the numbers should live so the next campaign starts from them.
FAQ
Straight answers on ingestion, entity resolution, scoring, activation, AI agents and token cost, pricing, and how we keep each client's data apart.
What is an outreach data system?
It is the layer your copy and sequences run on. Five layers: ingestion from filings, registrations, LinkedIn, job postings, and your own exports; identity and enrichment, so one company is one record; qualification scoring against what each client converts on; activation, which syncs clean segments to Instantly, HeyReach, and the CRM; and a feedback loop that pulls replies, meetings, and closed revenue back in to retrain the scoring. We install it on your stack and run it monthly.
Do you write the copy or run the campaigns?
No. Your copy, your offer, your sequences, your client relationships. We own the layer underneath: clean, scored, deduplicated segments delivered to the tools you already send from, and the results fed back so the next campaign starts from the last one. If you want an agency to run outbound for you, we are the wrong call and we say so.
Is this just a data warehouse?
A warehouse stores. This ingests, resolves, scores, activates, and learns. The warehouse is one piece, Postgres or BigQuery inside your cloud, and nobody on your team has to touch it. If your real question is what a warehouse is for, the honest answer is that you are not the buyer yet, and we would rather tell you that before the call than after.
What sources and tools does it connect to?
Sources: new business registrations, filing agents, SEC and EDGAR filings, LinkedIn, job postings, our own scrapes, and whatever exports you already have from Clay, Apollo, Sales Navigator, or a spreadsheet. Targets: Instantly, HeyReach, Smartlead, lemlist, and your CRM, including HubSpot, Salesforce, Attio, Pipedrive, and Close. Underneath: Python, SQL, dbt-style transforms, Airflow or Prefect, Postgres or BigQuery, Docker, and a cloud account you control.
Will you build something custom for one client?
Every engagement is a custom build. We scope it to your sources, your clients, and your volume from the intake and the call. The five layers underneath stay the same from install to install, which is why a clean install takes 30 days and not six months. A proprietary source, a bespoke scoring model, or a migration is part of that scope, priced up front, never a surprise later.
How do you build AI agents into the workflow, and how do you keep token costs down?
We treat the agent architecture as part of the system, not a layer on top. Deterministic work, such as dedupe, matching, filtering, and scoring rules, stays in code and spends no tokens. Small models classify and extract. A large model only does the research and writing that need judgment. Every agent reads from the warehouse, gets only the fields it needs, and cites the record it used, and its output is checked against a schema before anything is sent. Results are stored and reused, stable prompts are cached, and work that can wait runs in batch. We log token use and cost per job and per client, and a step moves to a cheaper model only when it passes the same evals.
How is each client's data kept separate, and what about compliance?
One environment per client, so one client's data never touches another's. Every record carries where it came from and when. Suppression lists, opt-outs, and CAN-SPAM and GDPR handling are part of the build, with a written policy for what we will not store. Access to your CRM and sending tools is scoped and least-privilege, and everything is encrypted in transit and at rest inside infrastructure you control.
How do you price it, and what is the return?
A setup fee plus a monthly, kept separate. The setup pays for the install and the first cleanse. The monthly holds the system, runs the jobs, and includes support and iteration. We anchor the monthly to what it replaces: if it gives an agency back twenty to forty delivery hours a month, the retainer is a third to a half of that, and we say the comparison out loud. We never price per lead or per meeting, because conversion depends on your copy and your offer, not on us.
Once the data is clean, why keep paying?
Because clean is not the product. The loop is. Replies, meetings, and closed revenue keep flowing back into the scoring, new sources keep landing, and dedupe and suppression keep running on every refresh. The month you turn it off is the month the Monday rebuild comes back. Something should be visibly getting better every month, and if it is not, you should cancel.