Sales Intelligence Platform
A continuous, always-on sales intelligence platform for a European industrial manufacturer expanding sales into new markets. It discovers the right industrial buyers across dozens of public sources, mirrors current clients through global look-alike matching, tracks buying intent 24/7, and delivers a daily flow of qualified opportunities to sales and marketing.
The challenge
The client manufactures atmospheric air filtration and compressed-air treatment systems and sells into compressor manufacturers, filter dealers, and compressor sales and service companies globally. They were expanding sales into new markets and needed to find and reach the right industrial buyers without manually researching and emailing each one.
Industrial B2B buyers in this niche are hard to find in a single database. The right companies are spread across business registries, chambers of commerce, trade directories, event and trade-show listings, tender portals, and local news. The sales and marketing teams were spending most of their time on manual research (finding companies, finding decision-makers, hunting for verified emails) instead of actually selling.
The client wanted automation, but this is a regulated, relationship-driven industrial sector. A human needed to stay in control of everything consequential without slowing the whole system down. And critically, they wanted the platform to work both ways: catch fresh buying intent the moment it appeared, and continuously surface look-alikes of their best current clients so outbound stayed full even when no news had broken.
What Klevere built
A continuous sales intelligence platform, not a one-off list build. Nine modules that discover, mirror, monitor, score, and act on target companies, with a human kept in control of everything consequential.
Signals and buying-intent monitoring
News, press releases, public tenders, and expansion signals are watched around the clock. Specific triggers surface the moment they appear: a facility expansion, a new production line, a new plant announcement, an investment received, a capacity increase, a merger or acquisition. Sales and marketing get a daily flow of buyers reaching exactly the moment they need what the client sells.
Look-alike account discovery
The AI analyses the client's current customer list globally to build a portrait of what a great customer looks like: industry, size, tech stack, growth pattern, buying triggers. It then continuously discovers companies anywhere in the world that match that pattern, ranked by similarity score. Even when no fresh signal has fired, look-alikes of the best current accounts still flow forward for outreach.
Multi-source discovery
Business registries, chambers of commerce, Google Places, industrial and trade-show directories, enrichment tools.
Tender and contract-award tracking
EU TED and country-level portals for public procurement, connected to responsible decision-makers.
Scoring with evidence trail
Fit-plus-intent score with time decay and confidence. Every recommendation links back to its public source.
Verified decision-makers
General Manager, Sales Director, Product Manager identified per account with verified work emails.
Human-in-the-loop
Autopilot and Copilot modes. Consistent approval framework at every consequential action.
Multi-channel outreach
Email and LinkedIn from the client's own mailbox, auto-stops on reply, sentiment-aware escalation.
CRM sync, reactivation, and competitor radar
Qualifying replies and booked meetings sync into HubSpot as contacts and deals automatically. Closed-lost and dormant accounts stay monitored and get handed back to the original owner when a fresh signal appears. A competitor-displacement radar turns publicly visible competitor footprint (case studies, tenders, job posts, reviews) into scored, source-backed displacement targets.
The build process
Klevere built the platform in eight phases, each signed off before the next began, so the platform could be rolled out to any new market as sales expansion demands.
Discovery and ICP definition
Klevere worked with the client to define the ideal customer profile: target industries (compressor manufacturers, filter dealers, compressor sales and service companies), company sizes, target job titles, target markets, and excluded domains. This ICP became the specification the whole system was trained and scored against.
Agent training on product and market
The AI agents were trained on the client's product context (atmospheric air filtration and compressed-air treatment), value proposition, and ICP. This is what allowed the agents to recognise a good-fit company, understand what a relevant buying signal looks like in this niche, and later draft outreach that sounds like the client and speaks to industrial buyers.
Connecting the data sources
The multi-source discovery layer was integrated: business registries, chambers of commerce, Google Places, industrial and trade-show directories, a news and press-release monitoring feed watching for facility expansion, investment, and merger and acquisition keywords, and public tender and procurement sources including EU TED. This is what lets the platform find companies and moments that a single database would miss.
Look-alike engine and scoring
The look-alike engine was trained on the client's existing customer base to build a portrait of a great customer, and the fit-plus-intent scoring model was built with time decay and a confidence score. Every recommendation carries an evidence trail linking back to the public source it came from.
Decision-maker and email enrichment
Decision-maker identification and work-email discovery and verification were wired in, so every surfaced company arrived with the right named contacts and reachable, verified emails ready for outreach.
Human-in-the-loop guardrails
A consistent approval framework was designed rather than deciding case by case. A human is required at points that carry cost, legal exposure, irreversible customer-facing commitments, negative or sensitive replies, and any low-confidence AI output. Autopilot and Copilot modes let the client tune autonomy without a code change.
Outreach engine and CRM sync
The client's own mailbox was connected with sending limits and health monitoring. AI-drafted email sequences run inside approved windows, with reply detection and sentiment classification escalating anything negative or ambiguous to a person. Qualifying replies and booked meetings sync into HubSpot as contacts and deals.
Dashboards, decision logs, and rollout
The full picture came into a single real-time dashboard covering pipeline, meetings, activity, and spend across every market and campaign. A decision log records every automated and human decision with its rationale. Isolated workspaces per market let each region run its own campaigns from one control panel, so the platform can be extended to any new market as sales expansion demands.
See the intelligence layer in action
A working example of the always-on sales intelligence layer. Shows how the modules feel to use and what the daily opportunity flow looks like end to end.
View live example →What made it different
It works both ways. Signal-driven when news breaks, and outbound-driven through look-alike matching against the client's best customers when no news has broken. Sales and marketing never wait for a fresh signal to have something to work.
Every recommendation carries source-backed evidence. The sales team sees why a company was surfaced, which is what makes the output trusted and acted on rather than second-guessed.
A human stays in control of everything consequential, with tunable autonomy through Autopilot and Copilot modes. In a compliance-sensitive, relationship-driven industrial sector this is not optional; it is what makes the platform deployable at all.
The platform finds buyers and moments beyond a single database, which is the core problem in niche industrial B2B. Sales and marketing get a daily flow of opportunities without hiring a full research team per market.
“What made it work was not the outreach. It was that every account that landed in front of us came with the reason and the source. My team stopped second-guessing the list and started actually selling into it.”
Sales Director, European industrial manufacturer
Want continuous sales intelligence for your business?
The template generalises well beyond manufacturing to any niche B2B vertical where the right buyers are not in a single database. The free AI audit gets you a written opinion on what a Klevere sales intelligence platform would look like for your specific market and ICP.
Frequently asked questions
How does the look-alike account discovery work?
+
The AI analyses the client's current customer list globally to build a portrait of a great customer: industry, size, tech stack, growth pattern, and buying triggers. It then continuously discovers companies anywhere in the world that match that pattern, ranked by similarity score. This is what makes the platform a proper outbound engine as well as a signal-driven one. When a fresh news signal has not fired, look-alikes of the client's best current accounts still flow into the daily opportunity feed for sales and marketing to work.
How is this different from a standard outbound tool or a lead database?
+
A lead database gives you a static list. A standard outbound tool sends messages. This is a continuous intelligence layer: it discovers and researches companies across sources a single database would miss, mirrors your best clients through look-alike matching, monitors accounts for buying intent over time, and only surfaces accounts with source-backed evidence. Sales and marketing get a daily flow of opportunities as they emerge, not a batch of stale contacts.
What kind of buying-intent signals does the platform actually watch for?
+
News articles, press releases, public procurement, and industry updates around specific triggers: a facility expansion, a new production line or plant announcement, a new investment received, a capacity increase, a merger or acquisition, or a hiring signal that implies growth. The moment a signal like that appears against a target company, the account surfaces to the sales team with the source and the reason.
How is a human kept in control in a regulated, relationship-driven sector?
+
A consistent approval framework, not case by case. A human is required at every point of cost, legal exposure, irreversible customer-facing commitment, negative or sensitive reply, and low-confidence AI output. Everything low-risk and reversible runs fully automated. Autopilot and Copilot modes let the client dial autonomy up or down without changing the code.
How does the platform prove why a company was surfaced?
+
Every recommendation carries an evidence trail linking back to the public source it came from: the tender document, the news article, the press release, the trade-show listing, the registry entry, or the look-alike similarity score against a named existing customer. The sales team sees exactly why a company appeared and why now.
Could Klevere build something similar for our business?
+
Yes. The template of a continuous, source-backed sales intelligence platform with look-alike discovery, human-in-the-loop guardrails, and CRM-native pipeline sync generalises well across industrial B2B, professional services, and other niche B2B verticals where the right buyers are not in a single database. The specific data sources, scoring model, and outreach patterns get scoped per client during the free AI audit.
Is there a working example we can look at?
+
Yes. A live example of the intelligence layer is available at sales-intel-ai.vercel.app. It shows how the modules feel to use and what the daily opportunity flow looks like end to end.