Giving your agents access is the easy part.
Most GTM agent tooling stops at access — and stops at data. GrowFlex gives your agents the whole stack — prospecting, signals, CRM, outreach — plus the three things that make them safe to employ there: restraint that holds at volume, a design that spends their intelligence only on judgment, and a number on what every task cost and whether it worked.
Runs on the stack you already own.
Run your entire GTM motion through Claude.
A unified MCP + workflow engine — your agent reasons; GrowFlex is its tool to connect your entire GTM stack with optimized context and focus at every step.
- 01Build audiences
- 02Enrich data
- 03Respond to signals
- 04Qualify & segment
- 05Activate
- 06Measure
- 07Improve
LOOPS COMPOUND — LEARNINGS FEED THE NEXT BUILD
Seven critical actions, run in composable loops — no Zapier, no Make, no flowcharts to maintain.
Four ways an agent fails.Most tools fix one.
The model isn't your constraint. Today's models can qualify an account better than most SDRs. What breaks is everything around it — what it can reach, what stops it, what you're wasting it on, and whether anyone can tell afterward if it worked.
It can't reach anything
It can describe a good account. It can't go get one. Every real step needs a human to leave the chat, open a tool, and paste something back.
Nothing stops it
An agent with an API key and no ceiling will re-enrich records you already paid for and contact people who asked you not to — for a month, before anyone checks.
You're spending it on the wrong work
Filtering, deduping, matching against the CRM, moving rows — none of it needs intelligence. Route it through a model anyway and you pay in tokens, in latency, and in the memory the agent needed for the actual decision.
You can't tell if it worked
It ran. Something happened. What did it cost, what did it return, would you do it again? Most stacks cannot answer this.
GrowFlex is four layers, because there are four problems. Together they do for agents what an operating system does for software: access to the machine, permissions it cannot ignore, a scheduler that never wastes the expensive component, and an accounting of what every process cost.
We start with access so we can move on.
A data source makes an agent better informed. A stack makes it useful. Most tools give your agent a login to your data — GrowFlex gives it a job in your GTM stack.
One interface between your agents and every system they touch.
Thirteen capabilities across eight categories of GTM tooling — instead of thirteen vendor integrations with thirteen sets of quirks and thirteen ways to break.
TAM & ICP persona mapping
Define who counts once. Agents return a verdict from a controlled list — ICP tier, persona, buying role, GTM motion, TAM membership — instead of inventing a new label every run.
Prospecting & enrichment
Find companies by firmographics, find the right people inside them, resolve the full profile: verified email, phone, funding, tech stack. Every paid lookup waterfalls across multiple providers, so one vendor's coverage gap isn't your coverage gap.
Signals & targeting
Detect what's changing at a target account — funding, hiring, tech adoption, news — and let agents route on it without being told which accounts got interesting.
Triggered workflow orchestration
Multi-step sequences that run on a schedule or when a signal fires, with human approval gates exactly where you want them.
CRM & CDP, read and write
Agents read your CRM before they act and write back through a field map you control. Product usage flows in from your CDP on a schedule.
Outreach, gated
Agents stage people into lists and move them into sequences. The agent decides who. You decide how many, and who's off limits.
That's access. Every tool in this category has a version of it. Here's what they don't.
An agent you can leave running unattended.
Most agent tooling hands the model an API and hopes the prompt holds. It demos fine and fails in production, because the failure modes only appear at volume — and by then it's your credits, your domain reputation, and your CRM.
GrowFlex inverts it: the more expensive the action, the less freedom the agent has over it.
- A closed vocabulary, not a blank page. Agents compose from a fixed set of steps with typed inputs. No free-text expressions, no improvised API calls. An agent that tries something that doesn't exist fails at validation instead of failing in your CRM.
- Every spend passes one gate. Searches and enrichments clear a budget check before they execute. Results are cached, so a record you've already bought is never bought twice. You get a ceiling, not a surprise.
- Suppression is structural, not requested. Sends carry a suppression scope and a hard cap as required fields. An agent cannot construct a send that skips them — not "shouldn't." Cannot.
- Humans where humans matter. Pause and approve are real steps. High-stakes runs stop and wait for a person. Everything else keeps moving.
The difference between an agent you're monitoring and one you're employing.
The model reasons, the tools act.
Using a model for non-reasoning tasks is expensive and time consuming. In most agent tooling the model drives the loop. It holds the plan, pulls the data, and grinds through records — and by the fortieth step it's carrying four hundred things it will never need again. That's not a model problem. That's a design that put the most expensive component in the least appropriate job.
GrowFlex runs the loop and responds to the agent when there's a decision to make.
Designing the work
You describe what you want. The agent composes a valid workflow against the exact capabilities your stack supports — asked and answered in a single call, so there's no discovery, no trial and error, no guessing at what's available.
Deciding, mid-run
When a step needs judgment, the run holds and hands back exactly the records in question — nothing else, and none of the run's history. The model rules on that slice, writes a verdict validated against your fixed vocabulary, and the run picks up where it left off.
Interpreting, after
Results come back as something to reason about — what the run produced, what it cost, what it means, what to do next — rather than as a firehose the model had to swim through to get there.
Thirteen step types. None of them call a model.
Filtering, branching, mapping, deduplication, cross-referencing, fan-out, collection, set-refill, provider calls, holds and sends — all of it deterministic code or a call to your tools. The model composes the run and rules on what needs ruling on. It doesn't personally move the rows.
Two hundred accounts move through ten steps. The model is consulted once — and when it is, it sees the accounts awaiting a decision and nothing else.
This is what it means for an agent to orchestrate GTM: it designed the whole motion — five systems, ten steps — in one call, ruled on the one decision that needed judgment, and never personally moved a row.
What that means in practice
- Deterministic steps run as code. Faster, effectively free, and correct every time instead of almost every time. Deduplicating four hundred records has one right answer; a model gets it right most of the time, and "most" is a defect on a step like that.
- Your criteria live in versioned rubrics, not in the prompt. Scoring means the same thing on every run, and you change it in one place.
- Configuration is referenced, not carried. Enrichment policy, suppression rules, CRM field map, persona targeting — stored once, passed as a pointer. Change it and every future run picks it up. No re-prompting.
- Answers come back as verdicts, not essays. An account is
tier_1. A contact is adecision_maker. Three tokens instead of three paragraphs, meaning the same thing to every agent on every run. - Reads are narrow by default. Ask the CRM for the properties you need, paged — not the whole object graph dumped into context because that was the only endpoint on offer.
- Long runs move to a durable worker automatically. A job spanning thousands of records doesn't die halfway through because a conversation ended.
The model gets a clean desk and one decision at a time. That's why it behaves the same way on its thousandth call as its first — and costs a fraction as much to get there.
Every task reports what it cost and what it returned.
"It's running" is not a result. If you can't put a number on a task, you can't decide whether to run it again, run it wider, or kill it.
- Cost and latency, per capability. Every step tracks what it actually costs and how long it actually takes, from real runs on your data — not vendor list pricing.
- You're billed on usable results. Paid lookups meter on what came back usable.
- Spend is bounded before it happens, not reconciled after. Every paid action clears a budget check first. The number you set is the number you spend.
- Runs are inspectable. Any job or workflow run can be pulled up after the fact — what ran, what it produced, where it stopped.
- Config is versioned. Policies, field maps and rubrics carry version history, so when output changes you can see what changed and when.
You can answer "is this working?" with a number. Most teams running AI in GTM cannot.
Bring your tools. Or don't.
Every capability has two paths — and most teams end up using both.
Connect what you already pay for. GrowFlex routes through your accounts, your data, your contracts.
Missing a slot? We select, create, and configure it in your name — you own the account, we run it. You skip procurement; you don't skip ownership.
Most teams land on both: connect what's already working, have us stand up what's missing. Either way it's your account — you're not locked in, and you don't decide up front.
We don't hand you an API keyand wish you luck.
The tool layer is the product. Getting it wired into your stack, your definition of an ICP, and your idea of what "qualified" means — that's the work, and we do it with you.
Stack review
We map what you own, what you're paying for twice, and where the handoffs break. Usually worth the call on its own.
Wire and define
We connect your CRM, CDP and outreach, and encode your ICP, personas and qualification criteria as versioned definitions your agents apply the same way every time — instead of prose in a doc everyone reads differently.
Run and hand over
We build the first workflows against real volume, tune on what the data says, and train your team to compose their own. You own the system, not a retainer.
The person who designs it builds it. There's no handoff to an implementation team, because there isn't one.
Built for a specific moment.
A good fit if
- Sales is still founder-led and you're missing opportunities because you simply don't have enough time
- You've made your first GTM hire and there's no system under them — spreadsheets and a CRM nobody trusts
- Real motion, real tooling, and it's brittle — data in six places, none of it talking
- You want AI scaling your GTM, even if you don't know what that looks like yet — just not a full-time job monitoring it
- You want agents doing the work, not agents writing reports about the work
Not a fit if
- You don't have a product in market yet — there's no offer for a motion to run
- You don't want AI in your GTM yet — we're not here to convince you
- You want one all-in-one vendor and don't care what's underneath
- You're looking for a strategy deck
Currently in select alpha.Limited beta coming soon.
GrowFlex runs in production today with a small number of design partners. We're keeping alpha deliberately narrow — every engagement is hands-on, and that doesn't scale past a handful at a time.
Alpha partners get the system shaped around their stack, and their edge cases become the roadmap.
Alpha
A few seats, chosen for stack fit. Starts with a stack review.
Beta
Opening soon. We'll tell you when, and nothing else.
Find out what your stack could actually run.
A 30-minute stack review. We'll map what you own, where it breaks, and what your agents could be doing with tools you already pay for. If GrowFlex isn't the answer, we'll tell you what is.
No deck. No discovery sequence. One call with the person who'd build it.