An agentic pipeline that monitors target companies for real-time buying signals, runs each signal through a 7-field cognitive reasoning chain, and generates personalized outreach that leads with the specific event — not a generic pitch.
Industry-agnostic by design. Swap the signal definitions, value proposition, and buyer persona — same architecture, same nodes. It runs for any B2B motion.
Generic. Untethered from anything happening at the company right now. The recipient knows it's a template before they finish the first sentence.
Anchored to a specific event. Demonstrates you did the homework. Opens a conversation, not a pitch.
Four stages, from raw news to human-ready campaign. Each stage is a discrete n8n node — the pipeline can be inspected, modified, or extended at any point.
The pipeline starts with a filtered list of top accounts from your CRM — the companies your team has already identified as high-priority targets. For each one, it builds a search query and hits real-time AI search APIs, scanning for configurable signal types. Results are structured as JSON with signal type, date, confidence score, and source text.
Signal taxonomy is deployment-specific. The same detection node can be configured to monitor for acquisitions, technology changes, leadership moves, product launches, job postings, funding rounds, partnerships, earnings reports, market expansion, conference appearances, and more — whatever signals indicate buying intent for your ICP. Different keyword config, same architecture.
Each signal receives a weighted base score by type. Signals that cluster within a 14-day window get a multiplier — two signals in two weeks means the company is in active evaluation, not just browsing.
A local LLM then serves as the qualification gate — it evaluates whether the signal creates genuine buying intent or is just noise. This step eliminates false positives before any content is generated. Without it, you're generating emails for every press release — and burning sender reputation on noise.
This is the differentiation layer. For each qualified signal, the pipeline generates a structured reasoning chain — not a template fill, but a derived analysis specific to the event. Seven fields, each building on the last:
The cognitive chain forces the model to think about why this matters to their business before it writes a single word of outreach. This is what separates signal-aware campaigns from "AI wrote an email about a news article."
The pipeline produces a structured brief designed to equip the sales team — not just an email. Each output includes an email body (under 120 words, signal-first, single-ask CTA), three subject line variants, a cold call outline built from the same signal, and a rep alert summary for Slack or CRM notifications. Everything is keyed to the triggering event so the rep walks into any channel with context.
The same LLM that generates the output also reviews its own work against a quality rubric — signal relevance, hook specificity, CTA clarity, word count, buzzword density. Each brief gets a self-assigned quality score before it reaches a rep.
This creates a feedback loop. As reps flag what landed meetings versus what didn't, the rubric calibrates. The pipeline builds a reference library of high-performing outputs, and the self-review scores become predictive. Once the benchmark is dialed in, the pipeline can send autonomously — because it can prove quality before the email leaves the system.
Real emails generated by the pipeline across three different industries. Each example below came from a live deployment — the pipeline detected a real-world event, ran it through the cognitive chain, and produced the outreach. Same architecture. Different signal taxonomies, value propositions, and buyer personas.
Premium insulated water bottles. $200M+ brand. Sells DTC on Shopify and wholesale via Amazon. Their growth depends on cultural relevance — partnerships with music festivals, outdoor events, and lifestyle brands drive discovery.
Hydro Flask's global partnership with Universal Music Group puts your products in high-influence studios and songwriting camps.
The challenge with these high-profile placements is converting passive visibility into trackable sales across Shopify and Amazon. Without a performance layer, this remains a branding play rather than a revenue engine.
Our platform turns that organic influence into a structured affiliate program, incentivizing artists and producers to drive external traffic that directly improves your Amazon BSR and conversion rates.
I have some data on how similar brands turn corporate partnerships into performance-based revenue. Would you like to see it?
Digital experience platform (Drupal). 500–1,000 employees. Uses Gainsight CS and PX. Public case study shows 12-point renewal rate increase — they measure CS ROI. A new VP CS hire signals platform reassessment.
A new VP of Customer Success at Acquia typically means platform reassessment within the first 90 days. The incoming leader needs to demonstrate visibility into accounts that current tooling can't surface — accounts showing early disengagement, product usage decline, or support ticket spikes that haven't yet hit a health score threshold.
Most CS platforms track lagging indicators. We surface the leading indicators — the behavioral signals that predict churn 6–9 months before a renewal conversation starts.
I'd be happy to share how peer organizations use this to protect NRR during leadership transitions.
Largest restaurant franchise operator in the US. 2,800+ units across Applebee's, Panera, Arby's, Taco Bell, Pizza Hut, and Wendy's. Rapid acquisition pace means constant technology integration across brands.
Flynn Group opening 15 new locations across three states means standardizing your POS, payments, and kitchen display systems across units — and doing it before the first new store goes live.
Multi-unit operators often discover that legacy systems don't scale cleanly past a certain footprint, and retrofitting after opening costs 3–4x what it costs to standardize upfront.
We help franchise groups deploy a unified restaurant platform that handles POS, online ordering, labor management, and kitchen operations from a single system — so your 15th new location runs the same stack as your first.
Worth a conversation about what that rollout looks like?
The pipeline architecture is constant. What changes per deployment is a handful of prompts and configuration values — making it reusable across any company, industry, or GTM motion.
| Layer | What Stays Universal |
|---|---|
| Company Input | Filtered CRM account list → batch processing pattern |
| Signal Search | Query construction + multi-API search logic |
| Signal Classification | Weighted scoring model + clustering multiplier |
| Qualification Gate | LLM-powered relevance evaluation |
| Cognitive Analysis | 7-field cognitive primitives framework |
| Email Assembly | Under-120-words rule, signal-first rule, no-buzzwords rule |
| Subject Lines | Under-8-words rule, no-clickbait rule, 3-angle generation |
| Output | Structured campaign brief format |
The same signal-to-campaign workflow can be built in Clay — but at a recurring monthly cost. Running it as a self-hosted n8n pipeline with a local LLM flips the economics from subscription to API credit spend only.
| Cost Factor | Traditional (Clay + APIs) | Pipeline (n8n + Local LLM) |
|---|---|---|
| Platform subscription | $800/mo (Clay Pro) | $0 (self-hosted n8n) |
| LLM inference | Included in Clay credits | $0 (local LLM) |
| Signal discovery API | Included in Clay credits (credit burn varies by volume) | ~$12–24/1,000 accounts (AI search API credits) |
| Cost per account | ~$0.10–0.18 (Clay credits) | ~$0.04–0.08 |
| Monthly total (4 runs × 1,000) | ~$1,200–1,520/mo | ~$192–320/mo |
Every email leads with what changed at their company — not what your product does. The recipient sees you did the homework before they see a pitch. This is the difference between "spray and pray" and actual relevance.
The 7-field reasoning chain means the model understands why the signal matters before writing. Most "AI outreach" tools skip this step and produce templated emails with the company name swapped in.
Built once, reconfigured with prompts. Self-hosted on n8n means no per-credit pricing, no vendor lock-in. Deploy for a new industry by editing prompts — not rebuilding the pipeline. Three industries, same nodes.
Signal-to-campaign automation isn't a product — it's a skill set. The ability to detect signal, interpret context, and generate action at scale, using tools that cost almost nothing to run. The architecture is the asset. The prompts are the configuration.