Products

Products

AI GTM automation for B2B revenue teams

Amoris is a founder-led AI GTM agency run by Praveen Shahi. Below is what exists today, with its actual status stated rather than implied - what is deployed, what is in testing, and what is published where you can check it yourself.

Core service

AI GTM automation

The whole go-to-market motion as one orchestrated system.

I map the motion, design the architecture against your actual workflow, and deploy it on your infrastructure. Enrichment, qualification, routing and CRM handoff stop being separate tools held together by manual work. Engagements start with a free Opportunity Scan.

  • LangGraph agents for multi-step qualification, not one-shot prompts
  • Clay waterfall enrichment with an LLM pass for intent and personalisation
  • n8n for event-driven orchestration across your existing stack
  • PostgreSQL for state and memory, so decisions are auditable
  • LangGraph
  • Clay
  • n8n
  • PostgreSQL
Book 30 minutes
The wedge

Amoris research engine

Evidence-led account research, and a system that knows when to stop.

Give it one company. It researches the account live, works out whether there is a defensible commercial reason to start a conversation, and returns a structured read with the source behind each observation. If outreach is warranted it drafts a message and checks its own draft against a fixed set of quality checks. The research is worth having either way, and sending stays manual.

Status: Research layer running. Outbound reasoning layer built and in private testing against a small number of real accounts - not yet deployed alongside it, and not sold as a finished product.

  • Evidence discipline - claims about the company must trace back to the research
  • HOLD stops the pipeline: before writing if the evidence is too thin, and after writing if a claim cannot be traced
  • Signal-first, so the argument changes per company and not just the name
  • Nothing is connected to your inbox and nothing is sent automatically
  • LangGraph
  • Tavily
  • OpenAI / Anthropic
  • FastAPI
How it works, in detailAsk about a live run
Published, open

intel-echo

Audits an AI conversation for where the reasoning exceeded its mandate.

Built out of running agent systems in production, where the failure mode that actually hurts is not a wrong answer but an agent quietly deciding something it was never asked to decide. Published on npm - install it and read the source.

  • Live on the npm registry, MIT licensed
  • Came directly out of production agent work, not a side experiment
  • Runnable demo hosted on this domain
  • Node.js
  • npm
  • Reasoning observability
View on npmLive demo
Applied tooling

Intel Echo GTM

The messaging loop, instrumented.

The go-to-market companion to intel-echo: a running view of how messaging behaves once it meets real buyers, so positioning changes are driven by evidence rather than opinion.

  • Runs on this domain
  • Built alongside live outbound work
  • Messaging analysis
  • GTM instrumentation
Open Intel Echo GTM

How an engagement runs

Every engagement starts with a free Opportunity Scan, which establishes whether there is anything worth doing at all. What follows is scoped from what it finds.

Pilot

One workflow, one cohort, one measured outcome

About 30 days. The success metric is agreed in writing before anything is built, a baseline is captured before anything sends, and a slice of the cohort is held back untouched as a control. You approve every message. Fixed scope, fixed price, no renewal.

  • Measured against a baseline
  • You approve every send
  • Can conclude it did not work
What is included

Deployment and managed system

Only if the pilot justifies it

Full data scope, hardened integrations, documentation and handover — running in your environment and working without me present. After that, an optional monthly cycle that tests something, measures it, and keeps what works.

  • Runs on your infrastructure
  • Documented for handover
  • Cancel any time
What is included