Lurnet - Concept

Status: revised product thesis, 2026-04-27. Beachhead is multi-product B2B SaaS. This document should be treated as a product-direction draft before execution planning, not as locked implementation scope.

In one line

Catalog-native GTM for multi-product B2B SaaS - turn every product in a SaaS catalog into a reviewed, measurable demand-generation motion.

Pitch

Multi-product B2B SaaS companies usually market the flagship product well and under-market the rest of the catalog. Secondary products, modules, add-ons, and vertical packages often have weak landing pages, generic positioning, stale outbound copy, and unclear routing. The products are not always weak; the GTM system is not built to give each one a real motion.

Lurnet ingests the product catalog and creates a GTM workspace for every catalog entry. For each product, it helps teams generate, review, publish, and measure ICP hypotheses, positioning, competitive angles, landing-page copy, outbound assets, lead-capture flows, and CRM routing rules. Over time, Lurnet learns which narratives work by product, audience, and channel.

The operating unit is the product catalog entry, not the campaign, account, or prospect.

Beachhead ICP

Initial target: multi-product B2B SaaS companies with 20-200 marketable products, modules, integrations, vertical packages, or add-ons.

Best first design partners likely have:

• A structured product catalog, pricing catalog, integration catalog, marketplace listing set, or internal SKU/module database.
• A small marketing team that cannot create a full GTM motion for every product.
• Existing CRM and website infrastructure, but weak product-level demand capture.
• Clear sales handoff paths by product line, region, segment, or account owner.
• Enough traffic or outbound activity to measure whether product-level narratives improve conversion.

Avoid for the first wedge:

• Single-product SaaS companies.
• Pure ecommerce merchants.
• B2C marketplaces.
• Companies that need full-funnel sales engagement before they can validate product positioning.

Product Vision

Lurnet becomes the system of record and execution layer for product-level GTM. A customer should be able to connect a catalog, approve AI-generated narratives, publish product-specific demand assets, capture intent, and route qualified leads without manually building one campaign per product.

The long-term vision can include product-level AI agents, but the first product should not depend on autonomy. The near-term product should validate a simpler claim first:

Given a product catalog, Lurnet can produce better product-level demand assets faster than a human marketing team, then show which products and narratives create qualified intent.

What This Is Not

Lurnet should avoid being positioned as a generic AI copywriter, AI SDR, cold-email tool, or landing-page builder. Those categories are crowded and easy to compare on shallow feature checklists.

The durable product is a catalog-native GTM workflow:

• Product records become GTM workspaces.
• AI drafts the first version, but humans approve customer-facing claims.
• Published assets capture intent with product context.
• Leads route to CRM with the product narrative attached.
• Outcomes update the product's GTM memory.

This is the solid wedge: product-level narrative, activation, routing, and measurement in one loop.

Investor Arc

The product matures across three compounding phases. Each phase strengthens the previous one.

1. Today - Catalog-native GTM workflow. AI generates, humans approve, customers publish and route. Lurnet combines product-level narrative, activation, routing, and outcome memory in one workflow, so every catalog entry has an ICP, positioning, asset history, capture record, and routing context.
2. Soon - Outcome-aware GTM intelligence. The workflow creates a data asset. Per-product, per-narrative, per-channel outcomes feed back into recommendations. Lurnet becomes the system of record for product-level GTM intelligence: what works, for whom, and when.
3. Later - Per-product autonomous agents. With validated narratives and proven conversion data, scoped agents can take over low-risk classes of execution such as outbound variants, ICP refinement, and experiment selection while humans gate brand and legal claims.

The investor takeaway: the assisted starting point is deliberate. Lurnet earns the right to automate by first owning the product-level GTM workflow and memory layer.

Market Wedge

Nearby categories exist, but they organize GTM around different units:

Category	Primary unit	Gap Lurnet targets
Marketing automation	Campaign, list, workflow	No native product-catalog object or per-product narrative memory
Sales engagement / AI SDR	Prospect, account, sequence	Usually assumes one product or one offer per tenant
ABM / personalization	Account, visitor, segment	Personalizes existing messaging rather than generating product-level GTM
Programmatic SEO / landing pages	Page, row, keyword	Publishes pages but usually stops before capture, routing, and learning
PIM / catalog tools	Product record	Manages product data, not GTM execution

The wedge is not that nobody uses AI for GTM. The wedge is that multi-product SaaS companies need a catalog-native GTM layer where every product has its own positioning, ICP, assets, routing, and performance history.

Core Workflow

1. Ingest catalog. Import products from CSV/API and map core fields: name, category, description, audience, pricing tier, competitors, existing URL, owner, and CRM routing hints.
2. Generate product GTM kit. For each product, generate ICP hypotheses, positioning, messaging pillars, competitive angles, landing-page copy, and outbound snippets.
3. Human review. Product marketing or growth reviews, edits, approves, or rejects each artifact.
4. Publish and activate. Publish a product landing page and/or export approved outbound assets to the chosen channel.
5. Capture and route. Capture leads, dedupe, enrich where possible, and route to CRM with product-level context.
6. Measure and learn. Track visits, form fills, replies, meetings, and CRM outcomes by product and narrative.

Product Pillars

1. Catalog Brain. Structured product records, metadata, ownership, and product-level GTM memory.
2. Narrative Engine. AI-assisted ICP, positioning, landing-page, and outbound generation with critique/refine loops.
3. Approval Workflow. Human review, edits, version history, and publish gates for customer-facing claims.
4. Activation. Publishable product pages and channel-ready outbound assets.
5. Capture & Route. Forms, dedupe, enrichment hooks, and CRM handoff with product context.
6. Learning Loop. Performance feedback by product, segment, narrative, and channel.

AI Features

The AI story should be concrete and tied to customer value:

1. Product-level GTM kits. Generate ICP, positioning, landing-page copy, and outbound assets from catalog data.
2. Narrative critique and refinement. Use a multi-step pipeline to identify weak claims, vague audiences, missing proof, and risky competitive claims.
3. Competitive positioning. Compare each product against named alternatives and generate differentiated angles.
4. Prospect-message fit. Match approved product narratives to target segments and accounts.
5. Outcome-aware iteration. Feed visits, form fills, replies, meetings, and CRM outcomes back into product-level recommendations.

Do not pitch "fully autonomous agents" as the first product promise. Use it as the long-term direction after the assisted workflow proves value.

MVP Scope

Goal: validate that Lurnet can turn a B2B SaaS catalog into useful product-level demand assets, routed intent, and measurable learning faster than the customer can do manually.

In:

• CSV catalog import with schema mapping.
• Product detail workspace.
• AI-generated GTM kit per product: ICP, positioning, landing-page copy, outbound copy snippets, competitive angles.
• Human approval and version history for generated artifacts.
• Single landing-page template per product with form capture.
• HubSpot webhook first, with a generic webhook fallback.
• Basic analytics: products imported, artifacts approved, pages published, visits, form fills, routed leads.

Assisted/manual in MVP:

• Outbound copy generation and export.
• Customer-owned sending through existing sales/marketing tools.
• Manual campaign launch decisions.
• Concierge support for the first design partner's initial catalog.

Out for MVP:

• Owning SMTP/IP infrastructure.
• Fully automated outbound sequencing.
• LinkedIn automation.
• Paid ads.
• A/B testing UI.
• Multi-tenant self-serve onboarding.
• Deep CRM bidirectional sync.
• Cross-tenant learning.

Validation Gates

Before building heavier automation, Lurnet should pass discovery and concierge gates with real multi-product SaaS teams.

Discovery Gates

Run 5-10 customer discovery calls before deeper execution planning.

Gate	Target signal
Pain	Teams name under-marketed products/modules without prompting
Ownership	Product marketing, growth, or RevOps owns the problem and budget
Existing workaround	Team is using spreadsheets, ad hoc pages, agency work, SDR requests, or manual campaign queues
Routing value	Product-level context changes how leads are handled
Urgency	Team has upcoming product launches, integrations, vertical packages, or expansion targets

Concierge MVP Gates

Then run a manual test with one real catalog before automating the workflow:

Gate	Target signal
Catalog activation	Customer imports 20+ products and maps required fields without heavy support
Narrative quality	Customer approves or lightly edits 60%+ of generated GTM kits
Speed	First approved product page goes live in under one business day after import
Demand signal	Published pages or activated assets produce qualified form fills, replies, or sales conversations
Workflow fit	Customer returns weekly to review products, publish assets, or inspect results
Willingness to pay	Customer agrees catalog-entry-based pricing is plausible after seeing output

Kill or pivot if customers only value one-off copy generation, if they cannot expose usable catalog data, if product-level routing does not matter to the sales motion, or if the buyer cannot name a budget owner.

Roadmap

Phase 0 - Discovery and Concierge Validation

Interview 5-10 multi-product SaaS teams. Run a manual test on one real catalog: produce GTM kits for 10-20 products, mock or publish pages, route captured leads, and measure whether the team would use and pay for the workflow.

Phase 1 - Narrative Validation

Build the catalog import, product workspace, narrative generation, approval flow, and manual export. Prove that Lurnet can create useful product-level GTM kits at scale.

Phase 2 - Demand Capture

Add hosted product landing pages, forms, dedupe, HubSpot webhook, generic webhook, and basic product-level analytics. Prove that the generated narratives can produce and route qualified intent.

Phase 3 - Assisted Outbound

Add approved email sequence drafts, mailbox/ESP integration options, reply tracking, suppression, throttling, and deliverability guardrails. Lurnet owns sequencing logic and personalization, but does not own SMTP/IP infrastructure.

Phase 4 - Learning Loop

Use page visits, form fills, replies, meetings, and CRM outcomes to recommend narrative changes by product and audience. Add simple experiments only after baseline measurement works.

Phase 5 - Scale

Add multi-tenant onboarding, richer CRM integrations, LinkedIn assist, paid ad creative, marketplace/catalog connectors, vertical playbooks, and cross-product portfolio analytics.

Pricing Thesis

Start with platform fee + active catalog entries as a pricing hypothesis because value should scale with the number of marketable products. Keep packaging flexible until after three design partners.

Possible early packaging variants:

• Platform fee for catalog, workspace, publishing, and CRM routing.
• Usage tier by active products or published products.
• AI generation limits or overages if cost becomes material.
• Pilot package tied to one product line or a fixed number of active catalog entries.
• Enterprise package for advanced routing, SSO, governance, and support.

Do not set anchor prices until discovery and design-partner work show whether customers see Lurnet as a product-marketing platform, demand-capture platform, or GTM automation layer.

Key Risks

• Competitors move into catalog-native GTM. Counter by focusing on product-level memory, routing, and performance history, not just AI copy.
• Customers lack clean catalog data. Counter with lightweight schema mapping, enrichment, and a small required-field set.
• AI output is generic. Counter with human approval, competitive context, proof-point requirements, and outcome-driven refinement.
• Outbound deliverability dominates the product. Counter by deferring owned SMTP/IP infrastructure and validating demand assets before automation-heavy sending.
• The buyer is unclear. Counter by testing product marketing, growth, and revenue operations as economic buyers during design-partner work.