Questions about AI GTM automation

• How do you prove it worked

  With a control group, and the calculation shown.

  A randomly selected slice of the eligible cohort is held back and contacted by nobody — same characteristics, same period. Recovered conversions in the treated group are measured against it:

  incremental conversions
    = treated conversions − (control conversion rate × treated volume)

  Why bother. Some dormant leads convert on their own. Without a control, every one of those gets credited to the work — which is the first objection a sceptical finance person makes, and they are right to make it. The holdout is withheld at the suppression gate before the first send, so it costs nothing to run.

  If a holdout is refused — and some will refuse, because deliberately not contacting leads feels wrong — the fallback is the same cohort’s conversion over an equivalent prior period. That is weaker, it cannot separate the intervention from seasonality or from parallel marketing, and the report says so rather than glossing it.

  Three tiers of metric, never blended:

  • Activity — leads analysed, contacted, delivered. Proves the work happened
  • Engagement — clicks, replies, opt-outs. Proves the cohort is alive
  • Commercial — qualified conversations, opportunities, conversions, revenue. The only tier that matters

  An agency reporting only the first two is reporting effort.

  Before any conversion lands, the useful metric is agreement rate on rejections: of the leads the system declined to contact, how many do you agree with? If a review of fifty produces forty-five agreements, the judgement layer is calibrated — proven in week one, without a single conversion.

  What is never claimed: no figure before it is measured against a baseline, no results from one engagement used as a forecast for another, no client name without written permission.

• How does a pilot work

  A pilot is one workflow, one cohort, one measurable outcome, over about 30 days — with the success metric agreed in writing before anything is built.

  The sequence:

  1. Baseline captured first. Whatever this cohort was producing before, measured and dated. A baseline reconstructed afterwards is not a baseline, and without one there is no honest claim to make at the end.
  2. A slice is held back. Randomly selected, contacted by nobody. Some dormant leads convert on their own; without a control group every one of those gets wrongly credited to the work.
  3. A resolution ledger workshop. Roughly an hour, working out everything you have shipped, changed or started offering in the last twelve months. This is half the analysis and it does not come from your CRM.
  4. A first pass goes to the whole reachable cohort — one low-claim message whose only job is to find out who is still alive. Measured on clicks and replies.
  5. A second pass runs only on the people who responded: why they died, what has changed since, and what to say about it.
  6. You approve every message before it sends.
  7. Routed into your existing funnel, with the full context attached, to owners you name.

  Fixed scope, fixed price, no renewal, no notice period. Short enough that being wrong is survivable for both sides.

  A pilot can conclude that it did not work. That is a legitimate outcome, it is written into how the pilot is scoped, and it gets reported as plainly as a positive result.

  The step that decides everything is the one on your side: leads get routed, and somebody has to pick them up. That gets measured too.

• How is this different from Zapier or Make

  Zapier and Make are no-code connectors between off-the-shelf tools. They move data from A to B, and they are good at it.

  Amoris builds the reasoning layer that does not exist yet - agents that qualify an account by weighing evidence, and workflows shaped around your specific motion rather than a template.

  The short version: this is not about connecting tools you already have. It is the reasoning layer underneath them, built from first principles and handed to you.

• What does Amoris build

  Amoris builds AI revenue workflows inside the systems a business already uses. Nothing to log into, no seats, no migration — your CRM, your automation and your funnel stay exactly as they are.

  The organising idea is that most companies do not need more leads. They need to stop leaking the revenue already passing through the funnel. That happens in four places:

  Leakage	What it looks like	The system
  Forgotten demand	Leads exist. Nobody follows up.	Dormant Lead Revenue Recovery — available now
  Slow response	Someone raises their hand; the business answers hours later	Lead Response Engine — in design
  Opportunity friction	A qualified prospect waits days for a proposal	Proposal-to-Close — in design
  Decision blindness	The data exists; nobody can say what changed or why	Revenue Intelligence — in design

  Only the first is currently available. The other three are designed but not built, and saying so is more useful than implying a full product line exists.

  How it is put together. Client-specific adapters read from and write back to your systems. Between them sits the reusable Amoris reasoning core — research, evidence, judgement — which improves with every engagement. Underneath, a ledger records every decision, the evidence behind it, and what happened afterwards.

  The reasoning is built once. The connections are built for your stack, and that is what a build fee pays for.

  What Amoris does not build: a CRM, a marketing automation platform, a sequencer, or anything that sends without a human approving it first.

• What does it cost

  The Opportunity Scan is free, and the analysis is yours whether or not anything follows.

  Beyond that there is no published rate card, and the reason is specific rather than evasive: Amoris has not completed enough engagements to know where willingness to pay actually sits. Publishing a number that would be revised after the first few deals is worse than saying so. You get a specific figure after the Scan, when the scope is real rather than hypothetical.

  What moves the number:

  • How much history your data holds. A clean export with recorded outcomes is a different job from a contact list with an abandoned status column
  • Cohort size, and how much of it is still lawfully reachable
  • Which systems have to be connected, and how they expose their data
  • Whether the resolution history already exists or needs building in a workshop

  The shape of it:

  Stage
  Opportunity Scan	Free
  Pilot	Fixed fee, ~30 days, one cohort
  Deployment	Scoped after the pilot, only if the pilot justifies it
  Managed system	Monthly. Cancel any time

  No lock-in. No automatic renewal, no notice period, no rollover. Everything built for you during a pilot or deployment remains yours either way.

  Any figures published on this site previously are withdrawn. They were set before the current offering existed and should not be quoted.

• What is an AI GTM engineer

  An AI GTM engineer architects and ships the agentic layer underneath a revenue team - multi-step reasoning in LangGraph, enrichment waterfalls, retrieval pipelines - and can put it into production.

  The distinction from a GTM engineer is where the work stops. A GTM engineer configures and connects existing revenue tools. An AI GTM engineer builds the system those tools plug into, and is accountable for whether it holds up under real volume.

  Amoris was founded by Praveen Shahi, who spent ten years carrying a revenue number at Amazon, Meta, Great Learning and Leverage Edu before building production AI systems. The combination is the point: the GTM judgment shapes the architecture, and the architecture is buildable because the same person ships it.

• What is the Amoris research engine

  It is an evidence-led GTM research system. You give it one company; it researches that company live, works out whether there is a defensible commercial reason to start a conversation, and produces a structured read with the source behind each observation. If an outbound message is warranted it will draft one and check its own draft — but the research output stands on its own, and sending is optional, manual and yours.

  The pipeline is Research → Reason → Judge → your decision, and it is built on LangGraph.

  Four things make it behave differently from most AI sales tooling:

  • Evidence discipline. Every factual claim about the company must trace back to something found in the research. Anything resting on inference is written as curiosity, not as a diagnosis.
  • HOLD is a feature. It can stop before writing anything if the evidence will not support a credible argument, and stop again after writing if a claim cannot be traced. Most tools in this category always produce a message.
  • Signal-first, not template-first. The argument changes per company, not just the merge field with their name in it.
  • Human decides. Nothing is connected to your inbox. Nothing is sent automatically.

  Where it actually is: the research and analysis layer is deployed and running. The outbound reasoning layer — hypothesis, message angle, the judge and HOLD — is built and in private testing against a small number of real accounts. It is not yet deployed alongside the research layer, and it is not sold as a finished product.

• What is the Opportunity Scan

  The Opportunity Scan is a free written analysis of your dormant lead database, delivered in days. You share one export; you get back a document you keep whether or not anything follows.

  It answers four things:

  • What is actually in there — how many records, how many still reachable, and what the data quality allows
  • Why those leads went cold, separated into what your CRM recorded and what had to be inferred
  • What you have shipped since that would reverse those reasons — the feature, the integration, the pricing change, the new location
  • How many leads died for a reason you have already fixed. That is the number that matters; everything else is context

  It ends in one of three recommendations: proceed with a pilot on a named cohort, fix the data first, or nothing here is worth automating yet. The third is real and it gets used — recommending a pilot that cannot succeed costs more than the fee it would earn.

  Why it is free. Nobody pays to be told what is broken. They pay for it to stop being broken. The Scan exists to establish whether there is anything worth doing at all, and neither side can know that in advance.

  It is not a proposal, a pitch deck, or a discovery call with a document attached.

  This replaces the earlier paid GTM Audit, which is no longer offered.

• Who else have you done this for

  Nobody yet. Amoris has no completed client engagements, no case studies, no named clients, and no measured reply rates or revenue figures — because none have been measured.

  That is an explicit credibility red flag and it should be weighed as one.

  What exists instead:

  Ten years of operating record, from before Amoris — a 150+ consultant sales organisation, $120,000+/month in net-new revenue, go-to-market built into LATAM and Africa from zero, and a separate business scaled to ₹3.5 crore ARR with a 30-person team built from nothing. Real, and always attributed to that period rather than presented as Amoris client work.

  Shipped software that can be checked without asking: a package published on npm, public source code, and the reasoning engine itself — written by the same person who would run your pilot.

  And, in place of results, the method:

  • The full calculation model is published before you commit — including the holdout arithmetic and what is refused as a claim. An ROI claim without a visible calculation model is worthless, and publishing the model while having no numbers to report is the strongest available position
  • Every stage of an engagement states where it ends
  • A written list of what is unknown, including the possibility that the core premise turns out to be smaller than expected
  • A free Opportunity Scan on your own data, yours to keep either way

  The honest suggestion is to judge the method. Read how the measurement works and decide whether you would trust that calculation if it produced a number in Amoris’s favour. If it does not hold up, do not engage — that is a better outcome for both sides than a pilot sold on a case study written to persuade.

• Who is Amoris a fit for

  The line that matters is whether a human closes the sale — not whether you are B2B or B2C.

  Counsellors, advisors, admissions teams, inside sales. Education, coaching, healthcare, insurance, real estate, automotive, considered-purchase consumer brands. Business-to-business works too, on the same logic.

  A fit when:

  • A human has a real conversation before anyone buys
  • There is meaningful lead volume — below a few thousand records, someone can just work the list by hand
  • Acquisition is paid or owned, so there is a known cost already spent
  • There is a structured process with stages, so renewed interest has somewhere to be routed
  • The historical database holds some interaction history, not just names
  • You have shipped, changed or started offering something in the last year. If nothing has changed, there is no honest reason to reopen a conversation

  Not a fit when:

  • Self-serve checkout. Someone who abandoned a cart never had a conversation, so there is no objection on record and nothing to reason from
  • You want volume regardless of whether the evidence supports the message
  • You want fully autonomous outreach — a human approves every send, by design
  • You need a tool or a sequencer rather than judgement
  • There is no CRM or structured record of any kind. That is a cold list wearing a CRM’s clothing

  Deliberately not enterprise. Larger budgets, but procurement cycles that outlast the point of a 30-day pilot.

  Being told “this isn’t a fit” on the first call is a normal outcome and it is said plainly rather than discovered three weeks in.

• Who owns what after an engagement

  Output is separated into three categories, agreed in writing before build work starts rather than argued afterwards.

  What you own. Everything produced specifically for you: the ICP schema built for your market, your qualification and scoring logic, prompts tuned to your buyers’ language, research outputs and account files compiled from your target list, workflow documentation, and evaluation results run against your accounts. These vest in you on payment.

  What Amoris keeps. The pre-existing engine and methodology — the pipeline architecture, the early-stop design, the evidence-traceability rules, and the outbound methodology that predates any engagement. Anything of this deployed to you comes with a perpetual, irrevocable, royalty-free licence for your internal use. You can keep running it indefinitely; you do not acquire ownership of it.

  What stays general. Methodology and learning that is not specific to you. Working with you sharpens how the work is done; that generalised improvement travels, while nothing confidential or client-specific does.

  The short version: what gets built for you is yours, the engine it runs on is licensed to you, and no client-specific information ever leaves with it.

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