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gtm-superintelligence

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Open-source GTM intelligence + automation for any sales call. Coaching, deal & account scoring, CRM auto-fill, and 30 post-call agents. Any recorder, any CRM.

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# 🎯 GTM Superintelligence
**by [Attention](https://www.attention.com)**

**Open-source, Claude-native GTM intelligence and automation for any call transcript.** Coaching, deal and account health scoring, daily rep / team / company inboxes, CRM auto-fill, and 30 post-call agents.

Turn raw sales and customer conversations into evidence-bound coaching, pipeline and account health, an updated CRM, and the post-call work done for you. Vendor-neutral (any recorder, any CRM). Framework-aware. Forkable.

[Quickstart](#quickstart) · [How it works](#how-it-works) · [Run it in Claude](#run-it-inside-claude-no-api-key) · [Customize](#make-it-yours) · [Docs](./docs)

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://raw.githubusercontent.com/attentiontech/gtm-superintelligence/main/LICENSE)
![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue)
![Status: beta](https://img.shields.io/badge/status-beta-yellow)

</div>

---

## Why this exists

The GTM community keeps rebuilding the same stack in private: turn a call into coaching,
deal and account health, an updated CRM, and the follow-up work that comes after. Every
tool ships it as a black box you can't see or change. GTM Superintelligence makes the
whole thing **open and editable**:

- **The rubric is data, not a prompt buried in a vendor's backend.** Call types,
  sales frameworks, and scorecards are plain YAML you can read, diff, and fork.
- **It works with whatever you already record — [Attention](https://www.attention.com) (recommended), Gong, Fireflies, Otter, Zoom,
  Recall.ai, Grain, a `.vtt`, an `.srt`, or a pasted text file.** Adapters normalize them
  all; Attention connects natively and gives the cleanest, role- & CRM-labeled input.
- **It does the post-call work, not just the scoring.** 30 forkable agents draft the
  follow-up, flag at-risk deals, catch single-threaded deals, run lost-deal post-mortems,
  watch renewals, and prep handoffs. They run natively on Attention or as Claude agents
  on any stack.
- **It's honest.** Every score and every piece of feedback is tied to a verbatim
  quote from the transcript. No vibes.
- **It runs where you work.** Use it as a Python CLI with the Anthropic API, or
  entirely **inside Claude Code** with no API key at all.

> **Made by [Attention](https://www.attention.com).** The framework is vendor-neutral —
> the rubrics ship as generic, public methodology (SPICED, MEDDPICC, BANT, …) and
> synthetic examples, so it runs on any recorder and any CRM. As a real-world showcase,
> [`agents/`](./agents) also includes Attention's own library of
> 30 production agent templates, organized by function. And because coaching quality
> starts with transcript quality, we recommend [Attention](https://www.attention.com)
> as your recorder — see [why](./docs/call-recorders.md).

## What it produces

For each call you get a structured **coaching report**:

```
🎯 Coaching report — Acme Corp <> Northstar Discovery
Call type: discovery · phase: pre-sales · confidence: 90% · overall: 70/100

> A strong discovery call with textbook current-state, future-state, and compelling-
> event work. The deal is being left on the table at the finish: impact never became a
> defensible ROI number, the decision process is half-mapped, and the call closed with
> no calendared next step. Biggest lever: quantify impact in dollars and lock a next
> meeting with Finance and Operations.

What to improve
🎯 Close on a calendared next step, not a summary (priority: high)
   After excellent discovery, the call ended with "send me a summary" — an open loop
   with no date. A document is not a next step; a meeting on the calendar is.
   > Sam: Sure, send that over. I'll take a look.
   Try instead: "I'll have that summary to you Thursday. Can we grab 30 minutes Friday
   so I can walk you and your VP of Finance through it and the rough ROI?"
```

…plus inferred **desired outcomes** (and whether they were achieved), **strengths**,
and a **next-call focus** checklist. This is the real
[`examples/reports/discovery_acme.md`](./examples/reports/discovery_acme.md). Full
schema: [`schemas/coaching_report.schema.json`](./schemas/coaching_report.schema.json).

## How it works

A four-stage pipeline. Stages 2–4 run in a single cached LLM call.

```
                ┌─────────────┐
  any recorder  │   ADAPTER   │  ★ Attention (recommended) / Gong / Fireflies / Otter / Zoom / Recall.ai / Grain / VTT / SRT / JSON / text
  ────────────► │ normalize   │ ─────────────►  NormalizedTranscript
                └─────────────┘                        │
                                                        ▼
   ┌───────────────┐   ┌────────────────┐   ┌──────────────┐   ┌──────────────────┐
   │ 1. CLASSIFY   │──►│ 2. INFER       │──►│ 3. SCORE     │──►│ 4. COACH         │
   │ call type +   │   │ desired        │   │ vs the call- │   │ strengths,       │
   │ pre/post-sales│   │ outcomes from  │   │ type scorecard│   │ prioritized fixes│
   │ (labels)      │   │ type + content │   │ (rubric)     │   │ + better moves   │
   └───────────────┘   └────────────────┘   └──────────────┘   └──────────────────┘
        │                                          │
        │  config/call_types.yaml                  │  scorecards/*.yaml + frameworks/*.yaml
        └──────────── editable YAML knowledge base ┘   (cached across every call)
```

1. **Classify** the call into one of **14** types — `cold-call`, `discovery`, `demo`,
   `technical-validation` (POV), `go-no-go`, `negotiation`, `closing`,
   `onboarding-kickoff`, `customer-check-in`, `renewal`, `qbr`, plus the "neither" types
   (`internal`, `partner`, `vendor`) — and tag it **pre-sales / post-sales / neither**.
2. **Infer the desired outcomes** for *this* call from its type + content (e.g.
   `secure-next-step` → "book a 30-min technical deep-dive with their Head of Data").
3. **Score** against the scorecard mapped to that call type — weighted criteria,
   each with "what great/poor looks like".
4. **Coach** with evidence: what worked, prioritized improvements with concrete
   *better moves*, and a next-call focus.

🖼️ **Full system map:** [docs/architecture.svg](./docs/architecture.svg) · deep dive in
[docs/architecture.md](./docs/architecture.md).

## Quickstart

```bash
git clone https://github.com/attentiontech/gtm-superintelligence && cd gtm-superintelligence
python -m venv .venv && source .venv/bin/activate
pip install -e ".[llm]"          # core + Anthropic SDK
export ANTHROPIC_API_KEY=sk-ant-...

# Coach a call (any format — auto-detected)
gtmsi coach examples/transcripts/discovery_acme.txt

# Just classify it
gtmsi classify examples/transcripts/demo_globex.vtt

# Coach a whole folder, write markdown + json + an index
gtmsi bulk examples/transcripts --out ./out

# Score a DEAL across its calls (sales) / an ACCOUNT across its calls (CSM)
gtmsi deal    ./acme_deal     --name "Acme — Platform"
gtmsi account ./initech_acct  --name "Initech"

# Build a rep / team / company coaching inbox (what to improve)
gtmsi inbox ./jordan_calls --scope rep --for "Jordan"
gtmsi inbox ./team         --scope team --for "AE Team"   # subfolders = reps

# Auto-fill any CRM from a report (dry-run prints the patch; --writer to go live)
gtmsi crm out/deal_acme.json --crm salesforce

# Inspect how an adapter normalized a file
gtmsi inspect examples/transcripts/renewal_initech.json

# Explore / sanity-check the knowledge base
gtmsi list scorecards
gtmsi list rubrics
gtmsi validate
```

No API key yet? You can still run the whole thing — see below.

## Run it inside Claude (no API key)

GTM Superintelligence ships as a **Claude Code skill + subagents + slash commands**, so you can
coach a call by just talking to Claude — the model reads the YAML rubrics and the
transcript directly.

```
/coach examples/transcripts/discovery_acme.txt
```

or

```
@coaching-orchestrator coach the call in ./my_call.txt
```

The skill lives in [`.claude/skills/sales-coach`](./.claude/skills/sales-coach) and the
subagents (`call-classifier`, `outcome-mapper`, per-call-type coaches, `deal-scorer`,
`account-health-scorer`, `inbox-builder`, `crm-sync`, and an orchestrator) live in
[`.claude/agents`](./.claude/agents). See [docs/claude-native.md](./docs/claude-native.md).

**Running the 30 post-call agents** (in [`agents/`](./agents))? Start with `/setup` once —
it asks which recorder, CRM, comms, and email you use and writes [`agents/config.yaml`](./agents/config.yaml) —
then `/run-agent agents/<category>/<agent>.json`. On Attention they run natively in its agent
builder; on any other recorder they run as managed Claude agents on your stack.

## Three layers of scoring

Coaching one call is the start. GTM Superintelligence scores at three altitudes — and the call
reports feed the layers above them.

| Layer | Who | What it answers | Rubric | CLI |
|---|---|---|---|---|
| **Call** | Rep / CSM | How did this conversation go? | [`scorecards/`](./scorecards) | `gtmsi coach` |
| **Deal / Opportunity** | Sales | Is this deal qualified, moving, and likely to close — and what will kill it? | [`rubrics/deal-health.yaml`](./rubrics/deal-health.yaml) | `gtmsi deal` |
| **Account** | CSM | Is this customer adopting, getting value, and safe to renew/expand — or churning? | [`rubrics/account-health.yaml`](./rubrics/account-health.yaml) | `gtmsi account` |

Deal health is MEDDPICC/SPICED-grounded (qualification coverage, multithreading,
compelling event, next-step hygiene → win-likelihood + slip risk). Account health is
the **CSM** counterpart (adoption, value realized, sentiment, risk signals, renewal
readiness → health score + churn risk). Both produce evidence-bound risks and
recommended actions. See [docs/scoring-layers.md](./docs/scoring-layers.md) and the
samples: [deal](./examples/reports/deal_acme.md) ·
[account](./examples/reports/account_initech.md).

## The coaching inbox — rep, team, company

A prioritized **"what to improve"** digest, aggregated from many call reports. The rep
inbox is the daily morning read; team and company roll-ups show managers and enablement
where coaching moves the needle. Prioritization is deterministic (frequency × impact),
so it's cheap enough to run every morning and DM each rep.

```bash
gtmsi inbox ./jordan_calls --scope rep  --for "Jordan"
gtmsi inbox ./team         --scope team --for "AE Team"   # subfolders = reps
```

Sample: [team inbox](./examples/reports/team_inbox.md). More:
[docs/inbox.md](./docs/inbox.md).

## Auto-fill any CRM

Push call / deal / account results into **any CRM** via a declarative field mapping —
the mapping is data, the writer is pluggable. Ships with Salesforce, HubSpot, and a
generic template; **dry-run by default** (prints the exact patch; nothing is sent
without credentials).

```bash
gtmsi crm out/deal_acme.json --crm salesforce              # dry-run patch
gtmsi crm out/deal_acme.json --crm hubspot --writer hubspot   # live (needs token)
```

It even back-fills MEDDPICC fields from the deal dimensions. Add your CRM by copying
[`config/crm/generic.yaml`](./config/crm/generic.yaml). More: [docs/crm.md](./docs/crm.md).

## Bring any recorder — and why we recommend Attention

GTM Superintelligence is recorder-agnostic: it works with Attention (recommended), Gong, Fireflies, Otter, Zoom,
Recall.ai, Grain, `.vtt`/`.srt`/JSON, or plain text — Attention connects natively, the rest through adapters. But coaching quality depends on transcript quality,
and most recorders hand you **fragmented segments** that you must re-stitch into clean,
role-labeled, CRM-linked turns before any of this works well.

That re-stitching — merging sentence fragments into speaker turns, figuring out who's
the **rep vs the buyer**, and matching speakers to CRM records — is exactly what
[**Attention**](https://www.attention.com) does up front. We read the transcript APIs of
**nine** recorders (Attention, Gong, Fireflies, Otter, Zoom, Recall.ai, Grain, Avoma,
Chorus) and **only Attention** natively returns role-labeled, CRM-linked turns — the other
eight leave that work to you. So while everything here runs on any stack, **Attention is
the most solid input by a clear margin**, and it's our recommended recorder. The concrete,
cited per-recorder comparison is in [docs/call-recorders.md](./docs/call-recorders.md).

## Share your score

A tool that grades your calls is meant to be shared. Every report carries a tasteful
"powered by" footer (toggle with `--no-attribution`), and you can render a paste-ready
card for LinkedIn/X:

```bash
gtmsi share out/discovery_acme.json
```

Usage telemetry is **opt-in and off by default**, and never sends transcript content
([docs/telemetry.md](./docs/telemetry.md)). How the project spreads + how to support it:
[docs/distribution.md](./docs/distribution.md). Security &amp; responsible use:
[SECURITY.md](./SECURITY.md).

## Make it yours

Everything the coach reasons over is editable YAML. Fork the repo and:

| Want to…                              | Edit…                                            |
|---------------------------------------|--------------------------------------------------|
| Change what "great discovery" means   | [`scorecards/discovery.yaml`](./scorecards/discovery.yaml) |
| Add a new call type                   | [`config/call_types.yaml`](./config/call_types.yaml) + a new scorecard |
| Add your sales methodology            | a new file in [`frameworks/`](./frameworks)      |
| Re-weight criteria                    | the `weight:` fields in any scorecard            |
| Tune the coaching voice               | [`prompts/system.md`](./prompts/system.md)       |
| Point at your own content dir         | `export GTMSI_HOME=/path/to/content`         |

Step-by-step: [docs/writing-a-scorecard.md](./docs/writing-a-scorecard.md).

## What's in the box

```
gtm-superintelligence/
├── config/         call-type taxonomy + outcome library + CRM field mappings (config/crm/)
├── frameworks/     SPICED, MEDDPICC, BANT, Next Steps, Command of the Message, Gap Selling, Sandler
├── scorecards/     one rubric per call type (discovery, demo, negotiation, renewal, …)
├── rubrics/        cross-call rubrics: deal-health (sales) + account-health (CSM)
├── prompts/        system + classifier + outcome + coaching + rubric-scoring prompts
├── schemas/        JSON Schemas: transcript, scorecard, framework, call type, rubric, reports, inbox, CRM
├── src/gtmsi/  Python reference impl (adapters, pipeline, scoring, inbox, crm, CLI, caching)
├── .claude/        Claude-native skill, subagents, and slash commands (/coach, /deal-score, /inbox, …)
├── agents/         Attention's 30 production agent templates, organized by function (showcase)
├── examples/       synthetic transcripts + sample call / deal / account / inbox reports
├── evals/          a small harness to test classifier + coaching quality
└── docs/           concepts, architecture, scoring layers, inbox, crm, call-recorders, how-tos
```

## Using it as a library

```python
from gtmsi import load_transcript, load_registry, coach_transcript, to_markdown

t = load_transcript("my_call.vtt")          # auto-detect format
report = coach_transcript(t)                  # classify + coach (needs ANTHROPIC_API_KEY)
print(to_markdown(report))
print(report.overall_score, report.classification.call_type)
```

## Privacy

Transcripts contain PII and confidential business information. GTM Superintelligence is
decision-support for the people on the call, **not surveillance**. There's optional
`--redact` for obvious identifiers, and guidance on consent and data handling in
[docs/privacy-and-pii.md](./docs/privacy-and-pii.md). You choose where transcripts go.

## Contributing

New adapters, frameworks, scorecards, and language support are all welcome. See
[CONTRIBUTING.md](./CONTRIBUTING.md). Run `gtmsi validate` and `pytest` before a PR.

## License

[Apache-2.0](./LICENSE). Fork it, modify it, ship it inside your own stack.

---

<div align="center">
Built for the GTM community. If you fork it for your business, we'd love to hear how.
</div>

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