Proposed Engagement

An AI ecosystem
for Win Without
Pitching

We've spent three years building an AI-powered operating system for our own agency — 35+ client accounts, 24 custom skills, six data connectors, autonomous agents monitoring around the clock. We rebuilt how we write, report, analyze, and execute.

This proposal outlines what we'd build for WWP. Not a single tool — a layered system where each layer makes the next one more powerful. You start with a configured Claude environment and end with an intelligent platform that knows your business, connects to your data, and works while you sleep.

How it's structured

Four layers, each building on the last

Each layer is independently valuable — you get real capability at every step. But each one also makes the next layer more powerful. Foundations give you a platform. The Creator Package gives you production tools. Pipedrive Intelligence gives you live data. The Intelligence Portal brings it all together.

1
Foundations
Claude Teams environment, custom INSTRUCTIONS, baseline skills,
anti-AI writing, team training, ongoing maintenance
Learn more→
Recommended
2
Creator Package
Presentation decks, dashboards & interactive tools,
report generator, deploy to web — branded production
Learn more→
Recommended
3
Pipedrive Intelligence
Custom MCP connector, Claude.ai skill, autonomous
weekly briefing agent with persistent memory
Learn more→
Recommended
4
Customer Intelligence Portal
Unified contact graph, lead scoring, web portal,
automated alerts across 5 data sources
Learn more→
Proposed project
Layer 1

Foundations

The groundwork. We configure Claude as a unified platform for your team, teach it how your business works, and build the habits that make everything else possible.

Claude environment setup

We configure a Claude Teams environment for WWP with dedicated project spaces, organized by function. Each project gets a custom INSTRUCTIONS file that grounds Claude in the right context — who the client is, how to behave, what tools are available.

This is the same pattern we use internally across 35+ client accounts. It's the difference between "using ChatGPT" and having an AI that knows your business.

What gets configured

Claude Teams workspace for WWP
Project folders organized by function
Custom INSTRUCTIONS per project (voice, context, rules)
MCP connections for tools and data
Ongoing updates as Claude evolves (new features, new models)

Custom baseline skills

We write skills that encode WWP's specific knowledge — not generic AI instructions, but your actual business context, terminology, workflows, and decision-making frameworks.

These aren't documents Claude reads once. They're active skill files that change how Claude works in every conversation. When someone asks Claude to help with positioning, it uses Blair's frameworks, not generic advice.

To build these effectively, we need your team to provide current documentation — SOPs, process descriptions, frameworks, terminology guides, sales playbooks. The more clearly your workflows are documented, the more precisely we can encode them into skills. We'll work with you to identify what matters most, but we need the raw material to come from you.

What we'd build for WWP

Business context skill — who WWP is, the products (Masterclass, Pricing Workshop, Positioning), the regions, the RTD structure, the sales process
Workflow skills — SOPs and processes we identify that Claude should follow when helping with specific tasks
Anti-AI writing skill — our production-grade content filter that catches and removes AI-sounding patterns. 50+ banned phrases, banned structures, mandatory post-draft audit.
Ongoing refinement — skills improve as we learn what works. New skills get added as we identify opportunities.

Training

We train your team on how to use Claude effectively — not generic prompt tips, but specific workflows for your actual work. How to query data. How to produce content that doesn't sound like AI. How to use Cowork mode with your existing tools.

Ongoing maintenance

Claude evolves constantly — new models, new features, new capabilities. We keep your environment updated, your skills tuned, and your team current. This isn't a one-time setup.

The platform effect

Once Foundations is in place, Claude.ai becomes the single interface where your team goes to get things done. Not a tool you switch to — the place you already are when you need to write, analyze, query, build, or decide.

The difference between "using AI" and having AI that knows your business is Foundations.

Layer 2

Creator Package

We teach Claude the WWP brand, opening the door to producing polished deliverables — presentations, HTML assets, dashboards, reports, and interactive tools — as living web elements that can be deployed, shared, and updated.

▶

Presentation decks

Interactive HTML decks built from a conversation. Navigation, build animations, responsive. Give Claude a document or walk through it — the output is a polished, browser-based presentation deployed to a live URL.

☷

HTML assets

Long-form, single-page web pages for strategy reports, case studies, executive summaries, and research write-ups. Two layouts (top-nav or side-nav), light mode default, scroll-triggered charts. Same design system as the deck. Lives at its own URL.

▦

Dashboards & tools

KPI dashboards, data visualizations, calculators, intake forms, interactive reports. Built with a design system that enforces consistent output. Deployed to a live URL instantly.

☰

Report generator

Structured report production that pulls from connected tools. Reports persist — build new, load saved, update incrementally. Three modes, one workflow, branded output.

What’s enforced

One design system, every deliverable

Each format above runs through the same set of brand constraints. Branded output isn’t a finishing pass — it’s baked into how Claude generates from the first token. The same colorways carry the same meaning whether they appear in a deck, an HTML asset, a dashboard, or a report.

The result: deliverables look like they came from one team, because the rules are the same across every format.

Brand colors with encoded meaning — Each color in WWP’s palette is assigned a specific narrative role (direction, insight, context, emphasis) and used consistently across every deliverable. Colorway is a signal, not decoration.
Ten data-viz components matched to data shape — Pie for 2–3 part-of-whole splits. Bar for ranked comparisons. Sankey for multi-stage flows with conserved volume. The chart matches the question, not the preference.
User-authored text preserved verbatim — Claude doesn’t “improve” content the team wrote. Spelling and grammar fixes are flagged for review, not silently applied.
Brand gating and validation — Before Claude builds any artifact, it confirms which brand applies. Every artifact runs a structural review before it ships. No “almost right” outputs reach production.
Where assets live

Deploy to web — and build a library

Say “deploy this” and Claude pushes any artifact — deck, HTML asset, dashboard, report — to a live URL under *.data.winwithoutpitching.com. SSL automatic, password-protectable per asset.

The URL is stable. Updates re-deploy to the same address, so a link shared once stays current. Every deploy is registered in WWP’s private asset inventory.

Deploy isn’t a button you press at the end. It’s the thing that turns disposable outputs into a growing library.

Stable subdomains — Memorable URLs that match the asset name. Bookmarkable.
In-place updates — Re-deploys land at the same URL. The link Blair bookmarked stays valid as content changes.
Per-asset access control — No password, generated 8-character credentials, or custom — chosen at deploy time. Multiple auth pairs supported for separate viewers.
Growing private library — Every asset auto-registers in the Campaign & Asset Manager. WWP gets a searchable inventory of everything live, with credentials stored.

These combine in a single conversation

“Take the Q3 pipeline numbers, write the strategy review, render it as an HTML asset with the agency-comparison data as a Sankey diagram, deploy it password-protected, and send Blair the link.”

Five skills — Pipedrive query, writing, chart selection, HTML asset production, deploy — chained in one session. Output: a live URL behind a password, ready to share.

“Same review, two weeks later — pull updated Q3 numbers, refresh the charts, add a paragraph about the new LATAM closures, re-deploy to the same URL.”

Targeted edits, same URL. Once an asset is live, changes are surgical — Claude updates the parts that changed and leaves everything else untouched. The bookmark stays valid; the content stays current.

Layer 3

Pipedrive Intelligence

A custom MCP connector and Claude.ai skill that give Claude live, accurate, tailored access to your Pipedrive CRM, plus autonomous agents that act on that data on their own — built and operated by us, tuned to how WWP actually uses the system.

Custom-fit MCP libraries

Why we build connectors per client instead of using stock ones

A custom-fit MCP library is the difference between Claude having raw access to your data and Claude having useful access to your data. We build the connector around the questions your team actually asks — pre-shaping responses, resolving identifiers, encoding the relationships that matter, removing the noise that doesn’t.

The result is faster, more accurate answers at a fraction of the token cost — and a system that can be improved over time as we learn how the team uses it.

More accurate — responses are pre-shaped with stage names resolved, primary emails extracted, currencies and timestamps preserved. Claude doesn’t guess.
More token-efficient — filtered responses are roughly 60% smaller than raw API output. Common queries skip the AI layer entirely.
More powerful — complex cross-referencing questions are handled inside the connector instead of forcing Claude to loop through your CRM record by record.
Safer — enforced read-only at three layers, with security tests that verify Claude cannot modify your CRM. Most generic CRM connectors are write-enabled by default.

Live CRM data in Claude

Claude queries your Pipedrive data in plain English. Contacts, deals, activities, pipeline stages — all live, all current. Dedicated tools handle common queries instantly; an open-ended query engine handles anything more complex (cross-references, aggregations, trend analysis).

Trained on how WWP uses Pipedrive

The connector ships with a custom skill that teaches Claude your deal stages, your products, how to disambiguate contacts and organizations, what “stale” means in your pipeline, how to format and present results.

A generic Pipedrive integration doesn’t know that when Blair asks “how’s the pipeline?” he means open deals grouped by product, or that the LATAM RTD needs different context than the NA RTD.

Managed by Newfangled. The connector runs on Lambda in WWP-tagged AWS infrastructure. We monitor it, update it as Pipedrive’s API evolves, swap out the underlying model as Claude releases new versions, and refine the skill based on how your team actually uses it. The system gets more closely fitted to WWP over time — and you never touch the code.

claude.ai/chat/wwp-q3-pipeline-review
WWP Pipedrive / Q3 pipeline review
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How’s our Q3 pipeline looking?
Pulled pipeline summary, open deals, and Q3 activity
📌 Pipeline snapshot — Q3 2026

You have 17 open deals worth $52,400 across the pipeline. Movement has been concentrated at the early stages — 6 new deals entered Lead In this quarter, while only 2 advanced to Proposal.

📊 Pipeline by stage
Lead In ████████░░░░░░░░ 6 deals · $15,200 Discovery ███████░░░░░░░░░ 5 deals · $13,500 Proposal █████░░░░░░░░░░░ 3 deals · $11,200 Negotiation ███░░░░░░░░░░░░░ 2 deals · $8,400 Verbal ██░░░░░░░░░░░░░░ 1 deal · $4,100
🎯 Key findings
  • Lead In is up 50% vs Q2 — the Masterclass marketing push is converting ↑
  • Discovery → Proposal conversion has slowed (28% vs 45% last quarter) ↓
  • 4 deals have been in Discovery for 30+ days — possible bottleneck →
📋 Deals needing attention
Deal Value Stage Last touch ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔴 Altitude — Masterclass $4,500 Proposal 32 days ago 🔴 Webb — Pricing Workshop $2,800 Discovery 28 days ago 🟡 Criativa — Positioning $3,200 Discovery 14 days ago 🟢 Studio Park — Masterclass $4,500 Negotiation 3 days ago

Q3 = May 1 – Jul 31. Comparison vs Feb 1 – Apr 30. “Stale” = no logged calls, meetings, or emails in the last 30 days.

Show the stale Discovery-stage deals
Compare Q3 to Q2 by product
What changed for Altitude over the last 3 months?
Reply to Claude…
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Opus 4.7 Adaptive ▾

Pipedrive Agents

Alongside the connector that the team uses interactively in Claude.ai, we deploy autonomous AI agents that run on their own — on a schedule, on demand, or in response to CRM events. Each one uses the same Pipedrive connector and delivers results via email or Slack. No human prompt required.

How they work

An agent is a small autonomous program: a goal, a model, a connector, and a delivery channel. We define the job; the platform handles execution, retries, sandboxing, and delivery. Agents can run on a cron schedule, fire on CRM events, or live as conversational copilots that the team can talk to in Claude.ai.

All agents share the same managed infrastructure — same Lambda environment, same secrets store, same monitoring. Adding a new agent is configuration, not new infrastructure.

How they improve over time

Managed Agents aren’t stateless. Three mechanisms make them genuinely better with each run:

Persistent memory
Agents retain what they learn across sessions. Corrections stick; useful patterns persist. Each agent gets sharper at its specific job.
Self-grading (Outcomes)
Agents evaluate their own output against a quality rubric before delivering it. If output falls short, they take another pass — automatically.
Dreaming
Between sessions, a background process consolidates each agent’s memory — surfacing patterns no single run could see.

Potential Pipedrive Intelligence Agents

Schedule · Mondays
Weekly Pipeline Briefing

What it does. Every Monday at 9am, the agent wakes up on its own. It pulls the current pipeline summary (deal counts and values by stage), the full list of open deals, and every activity logged in the past 7 days — calls, meetings, emails, tasks. Then it cross-references the two: which deals moved stages this week, which had no touchpoints, which had calls without notes, which are sitting longer than typical for their stage.

What you receive. A briefing in your inbox or Slack before the team logs on. Pipeline snapshot up top. Then: deals that closed last week, deals that moved forward, deals that went quiet, and a short list of recommended follow-ups for the week ahead with the reason for each. Each section runs a few sentences — nothing padded, nothing missing.

How it gets sharper. The briefing remembers what was flagged in prior weeks. If a deal it flagged as stale closed two weeks later, that pattern feeds into how it interprets similar deals going forward. If you correct it (“stop flagging this one, it’s waiting on us”), the correction sticks. Over months it builds a model of which deals deserve attention vs which are normal pauses for your sales rhythm.

Event-triggered
Stale Deal Investigator

What it does. Wakes up the moment a deal trips a staleness rule — no stage movement in 14 days, or no logged activity in 21. Pulls the deal’s entire history: every stage transition with timestamps, every call/meeting/email logged against it, every note left by the deal owner, and the contact’s broader engagement record.

How it analyzes. Reads the most recent activity note for context (what was actually discussed, what was promised, what was waiting on whom). Looks at the deal’s product, stage, value, and owner. Compares against similar deals in its memory — same product, same stage — that resolved successfully vs slipped further. Identifies the most likely cause of the silence.

What you receive. A note to the deal owner via email or Slack within minutes of the trigger. It includes a one-paragraph context summary (where the deal is, what was last discussed, why it’s likely paused), a suggested re-engagement angle tailored to that history, and a rough draft of an outreach message the owner can edit and send.

How it gets sharper. Tracks which suggested plays got tried, and what happened next. If a particular re-engagement angle pulled a Pricing Workshop deal back into motion, it remembers that. If another angle got crickets, it stops suggesting that one for similar deals. Within a few months it has a working playbook of what actually moves your deals forward, organized by product and stage.

Continuous
Win/Loss Pattern Tracker

What it does. Triggers every time a deal moves to “won” or “lost.” Snapshots the entire trajectory before details fade: every stage transition with the time spent in each, every activity logged against the deal, the cadence between touchpoints, the deal value at each step, the owner, the product, and the substance of the most recent notes.

How it analyzes. Stores each closure in a growing library indexed by product, stage path, and outcome. Looks for what was different about deals that won vs deals that lost — not just one variable, but combinations. Did the won deals have a second call earlier? Did they have notes in particular stages? Did they share an owner profile? Patterns that take dozens of closures to become visible.

What you receive. A lightweight per-deal post-mortem in the moment (one paragraph, what stood out about this closure relative to the library). Then a longer monthly insight digest to Blair: the strongest patterns the library has surfaced this month, with concrete examples. “Positioning deals with a second call within 7 days closed 4× more often this year. Pricing Workshop deals lost when they sat in Proposal more than 21 days — eight examples.”

How it gets sharper. The library is the asset. The first month produces shallow patterns; by month six the agent has a richly textured model of what your sales process actually looks like at its best and worst. Patterns that contradict earlier ones get re-examined. Patterns that hold up across many closures get promoted to high-confidence findings the team can act on.

Quarterly · on-demand
Quarterly Pipeline Council

What it does. Triggered by Blair (or on a schedule) before quarter-end. A lead agent reads the brief, then dispatches four specialist agents in parallel — one per RTD region (NA, LATAM, EMEA, APAC). Each specialist gets the same instruction template but runs its own deep analysis on its region’s slice of the pipeline.

How the specialists work. Each region specialist looks at its open deals, stage distribution, time-in-stage trends compared to last quarter, win/loss outcomes for the period, deal velocity, owner workload, and contact engagement patterns. Each writes a structured report to a shared workspace where the lead agent can read it. Specialists run simultaneously, so the analysis that would take four sequential passes happens in one.

What you receive. One cohesive quarterly review document, not four separate reports. The lead agent reads all four region reports, identifies cross-region patterns (which products are strongest where, where pipeline health diverges from the trend, which RTDs face which kinds of deals), and writes the synthesized review. Pipeline health up top, regional comparison, deals at risk going into next quarter, deals worth pushing on this week, recommended discussion topics for the quarterly meeting.

How it gets sharper. Each specialist remembers its region’s prior quarters and what happened to the deals it flagged. The lead agent remembers the structure Blair finds most useful and the questions he tends to ask — subsequent reviews build on that, surfacing the things that actually warrant attention rather than the things that look statistically interesting.

Conversational
Deal Coach

What it does. Lives in Claude.ai as a conversational copilot. Any RTD can talk to it. They paste a deal ID (or just describe the deal) and ask things like “what should I do next?”, “why is this stuck?”, or “is this worth chasing?”

How it analyzes. Pulls the deal’s full history — stages, activities, notes, contact engagement. Then searches for the three most similar past deals using product, deal value, stage path, and contact profile as match criteria. Reads what worked and didn’t in those analogues. Drafts a recommended next action grounded in that comparison, not in generic sales advice.

Quality control. Before the RTD ever sees it, the recommendation is scored against a rubric: is it specific to this deal’s actual situation? Does it cite the comparable deals it’s drawing from? Does it name a concrete action with timing (not “reach out soon”)? Is the action feasible within the next week? If the recommendation falls short, the agent retries automatically. The RTD only sees output that passes.

How it gets sharper. Remembers each RTD’s history with each deal across conversations. If you asked about a deal last week and tried the play it suggested, it knows what happened next time you ask — and adjusts. Over time it builds a working model of each RTD’s style and which kinds of recommendations land for them.

Schedule · monthly
Resurrection Hunter

What it does. Once a month, scans the contact base for people who were active 6–12 months ago and have gone completely quiet — specifically those who had open deals that slipped to “lost” or simply stopped moving. Pulls each one’s deal history to understand what was on the table and why it stalled.

How it analyzes. For each candidate, looks at the products they were interested in, the stage the deal reached, the substance of the last few notes, and any signals that conditions might have changed (their org appearing in the CRM through a different person, related contacts re-engaging, etc.). Ranks the list by likelihood of a productive re-engagement, not just dormancy.

What you receive. A ranked re-engagement list at the start of each month. Each entry includes: the contact, the last meaningful touchpoint, what was on the table when they went quiet, the most likely reason the deal stalled at the time, and a suggested re-engagement angle — something specific to that history, not a template.

How it gets sharper. Tracks which contacts it has surfaced before, what plays were tried, and whether anything came of them. A contact who got three suggestions and never re-engaged drops down the list. A contact whose colleague responded gets a different angle next time. Over months it builds a working sense of which dormant relationships are worth pursuing vs which are genuinely closed.

Layers 1–3 give your team a connected, intelligent workspace. Layer 4 builds on that to create something new.

Layer 4 — Proposed Project

Customer Intelligence Portal

The most ambitious layer. A dedicated application that goes beyond Pipedrive — aggregating contact data from five sources (Pipedrive, Kit, Circle, Fathom, Clay) into a unified contact graph with lead scoring, a web portal, and automated alerts.

This is a separate project that builds on everything in Layers 1–3. The same Pipedrive connector, the same agent infrastructure, the same Claude.ai skills. It adds a scoring engine, additional data connectors, a dedicated web portal, and intelligent automation that surfaces your highest-value leads to the right person with a recommended next step.

◉

Unified profiles

One view of every contact across five sources. Email engagement, Academy activity, call summaries, deal status, LinkedIn data — merged into a single profile matched by email.

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Lead scoring

A configurable scoring engine that weighs engagement signals, applies recency decay, and ranks every contact. Tuned by your team based on what actually predicts conversions.

⚡

Automated alerts

When a lead crosses the engagement threshold, an AI agent reviews their full profile and sends the right RTD a contextual recommendation — not a template.

The intelligence portal has its own detailed product specification.

View the full spec
How it's operated

You own Claude. We run the rest.

WWP owns the Claude environment directly — your subscription, your users, your spend limits. We advise on the configuration as part of Foundations (workspace structure, member roles, budget controls), but the account stays in your name and under your control.

Everything else — the connectors, the agents, the deployed tools, the infrastructure that makes all of this work — we take care of.

Isolated
Dedicated network and compute. Your data never mixes with another client's.
Encrypted
All API keys in Secrets Manager. All connections over HTTPS. No anonymous access.
Monitored
Automatic alerts on failures. Cost tracking per client. Health dashboard.
Maintained
We handle updates, deployments, and scaling. You don't touch infrastructure.
Prepared by Newfangled · May 2026