Our methodology / Research systems
For the people evaluating how the work gets done. A research system has to identify the right business, investigate the people behind it, preserve the evidence and keep the result current enough to act on.
AI helps do the work. The engineering is in making that work repeatable, inspectable and specific to your market.
The architecture
This is our approach to designing a research engagement. The sources, automation, review coverage and refresh schedule are agreed for each project.
Fit criteria, exclusions, required fields and the decision the research must support.
Businesses, people and locations, with the relationships between them.
Source records, observation dates, conflicting claims and unanswered questions.
What fits, what changed and what the evidence does—or does not—establish.
Research records and briefs in the agreed format, ready for your team to use.
For ongoing work, new evidence feeds back into the existing records. Refreshing a source and changing a recommendation are separate decisions.
Research depth
A brand, its parent company, a franchise operator and an individual site can have different owners and buying responsibilities. A person’s title belongs to a relationship with a business; that relationship can change.
Our research starts by separating those entities. It then tests the account against your criteria and investigates the evidence behind a potential reason to reach out. A missing answer stays an open question.
Consider a brief targeting manufacturers with $30–50 million in revenue. A directory estimate is a starting point. The research needs to establish which entity it describes, its date and basis, and whether the company actually makes the product you care about. Unverified revenue should not silently become a confirmed match.
Depth is defined in the brief. Each in-scope account is assessed against the agreed questions; accessible evidence and unresolved gaps determine the result.
See a public research exampleMulti-agent orchestration
A multi-agent workflow breaks research into bounded tasks. Each task needs a defined input, an expected output and a way to decide whether the result is acceptable.
Company identification, source discovery, role research and commercial assessment answer different questions. Their outputs should connect to the same account record. A coordinating process tracks task status, dependencies and review outcomes so partial work can be identified and resumed.
Accounts can be researched independently. Dependent work waits for its inputs: researching a person against the wrong operating company only compounds the error. Source access limits and the research budget constrain concurrency.
An unavailable source, disputed match or missing field needs an explicit outcome. In implementation, we define retry limits and escalation rules so incomplete jobs cannot be mistaken for completed research.
A second agent agreeing with the first is not independent confirmation. Checks need to return to supporting sources. Human review focuses on ambiguity and consequential judgments, with coverage agreed in the engagement.
The workflow is a means to a deliverable. The number of agents is not a measure of research quality.
Quality and maintenance
Before commissioning a larger dataset, review a sample against the same criteria the full research will use. Agree how errors will be found and what happens when coverage is insufficient.
Build or buy
A capable internal team can build this. The choice is whether you want to own the research operation, commission a defined piece of it, or combine the two.
These approaches can work together. Existing tools and internal knowledge can remain part of the research plan.
Your data
The aim is to help you build your own working database: company, person and location records, the evidence behind them and assessments tied to your criteria. Agree the field definitions, identifiers and destination before collection begins.
The handoff can be an agreed set of working files. Database delivery, CRM mapping or a maintained pipeline requires its own scope and acceptance checks. We do not assume your team needs a new platform.
Your private inputs must be distinguished from public evidence and licensed sources. Conclusions derived from private information need the same care as the inputs. An AI-generated summary does not create new rights to share the underlying data.
Ownership, export rights, permitted uses, retention and deletion belong in the engagement agreement. Third-party licenses can restrict what can be stored or handed over; we assess those constraints before proposing a source.
Deployment architecture, security controls and service levels are agreed for the implementation. This page describes our engineering approach, rather than a preconfigured software product.
Start with the evidence
GTM intelligence strategy defines the customer profile, source options and a small feasibility sample before a larger implementation commitment.
Explore the strategy