# Partner Intelligence Engine | Aslam C K > An 8-agent GTM intelligence and outbound system built to find branded packaged-food importers across 32 countries, a market conventional B2B databases couldn't see. Web page: https://aslamck.pages.dev/portfolio/case-studies/partner-intelligence-engine.html AI-Native GTM Intelligence & Outbound System # Partner Intelligence Engine An 8-agent research and qualification architecture built to find branded packaged-food importers and distributors across 32 countries, a fragmented, low-digital-presence market that conventional B2B databases couldn't cover. - Business: Food export, branded packaged goods - Markets: 32 countries, including the UK, Australia and the United States - Timeline: Sourcing-to-qualification progression within ~2 weeks of go-live - Role: GTM strategy, motion design and system architecture 01 ## Commercial objective Find and reach relevant importer-distributors of branded packaged food across a wide set of international markets: companies with a real, evidenced import capability for the product category, not just companies that mentioned relevant keywords online. 02 ## Operating conditions - The target market was fragmented and had low digital presence across most of the 32 countries. - Total addressable market was relatively small, but each correct account carried high commercial value. - Company information was inconsistent: categorization, language and registry formats differed by country. - Conventional B2B databases returned a lot of noise: restaurants, chefs, food-service businesses, bulk commodity importers and suppliers that were not the actual target. - Resources were limited: this had to run without a large team or an enterprise data budget. - The output needed to be usable prospects with verified import capability, not raw, unqualified leads. 03 ## The GTM decision Apollo and Clay's standard data layers did not provide sufficient coverage for this ICP: the fragmentation of the market meant no single vendor owned complete data on it. Rather than force the problem into a standard database-first workflow and accept a high false-positive rate, I designed an alternative data and research architecture built around the market itself. That meant treating company discovery and qualification as a research problem, not a lookup problem, combining directories, registries, trade and customs data, and reverse-engineering competitor distributor networks (a company already distributing a comparable product category is a far stronger signal than a keyword match). 04 ## Motion design The motion ran from signal to personalized outreach, with an explicit qualification gate before any contact was researched, and a human checkpoint before anything was sent. The system's job was to prepare research and personalization, not to replace judgment on relationship-sensitive actions. 05 ## System design Discovery and research required probabilistic reasoning (interpreting sources, classifying companies, verifying claims) that a deterministic workflow alone couldn't handle well. That's what moved the system toward an agentic architecture: eight agents with defined boundaries, inputs, outputs and handoff logic, coordinating through a shared data layer built on Supabase. The CEO used the system through its web interface: the code lives in a GitHub repo, the data in Supabase, and the interface is published by Cloudflare Pages, connected to both. 06 ## Architecture 1 #### Intent Signal Agent Detects relevant market and company-level signals (activity, category movement, commercial relevance) using a defined signal taxonomy and weighting. 2 #### Lead Discovery Agent Finds candidate companies via directories, registries, trade sources, competitor-network research and targeted web research, the sources standard databases missed. 3 #### Lead Qualification Agent Verifies existence, import capability and commercial role using evidence rather than marketing language. 4 #### Company Researcher Agent Builds a deeper profile: market role, brands, categories, geography, positioning and a reason for outreach. 5 #### Contact Scouting Agent Identifies relevant decision-makers, adapting to company size, structure, country and title conventions. 6 #### Contact Researcher Agent Researches the individual's role, authority and professional context. 7 #### Personalized Digital Prospectus Agent Builds a company-specific commercial asset ahead of outreach, giving the prospect a tangible reason to engage. 8 #### Outreach Draft Agent Prepares email and LinkedIn touchpoints built around the commercial tension behind the signal, held for human review before sending. Human-in-the-loop by design. Automation covers repetitive research; the agents handle reasoning-heavy discovery and qualification; a person stays in control of anything relationship-sensitive, including every send. 07 ## Execution & qualification Qualification was intentional attrition, not failure: each stage filtered for evidenced import capability rather than keyword relevance. Sourced 322 Scored 230 Verified 155 Advanced 41 On the contact layer, 159 decision-makers were researched, and 66 verified emails were found through a Clay → Apollo enrichment waterfall. Where a contact-level reveal failed, the system fell back to company-level targeting rather than guessing at a contact. Separately, reverse-engineering known competitors' distributor networks surfaced 81 additional companies, each one already demonstrating category import activity, which made it a stronger signal than a keyword match from a database search. 08 ## Evidence - 32 Countries mapped - 8 Specialized agents - 322 Companies sourced - 159 Decision-makers researched - 81 Competitor-network discoveries - 66 Verified emails - 41 Advanced to outreach - 29 Personalized prospectuses generated 09 ## Learnings - No single data vendor owns complete coverage for a fragmented, low-digital-presence ICP: the system has to build its own coverage. - Competitor distributor-network research is a stronger discovery signal than keyword search for categories with established comparable players. - Import capability needs to be verified through evidence, not inferred from marketing language. - Agentic research earns its complexity when the task is reasoning-heavy (interpretation, classification, verification), not as a default for every workflow. - Keeping a human checkpoint before every send preserved relationship quality without slowing down research and qualification. ## Working on a similar data or targeting problem? If a fragmented market or messy ICP is blocking your outbound motion, let's talk it through. [Book a conversation](https://cal.com/aslam-c-k/1v1) Back to work ### aslam.c.k GTM Engineer [ckaslam33@gmail.com](mailto:ckaslam33@gmail.com) © Aslam C K Availability Open to remote, worldwide - [LinkedIn](https://www.linkedin.com/in/aslam-c-k/) - [Book a conversation](https://cal.com/aslam-c-k/1v1)