Sector coverage: building a balanced training mix

An agent trained on the internal records of a hundred software startups will be very good at working like a software startup. Put it inside a freight broker, a dental group or a construction firm and it will carry assumptions that do not hold. If the goal is agents that handle real work across the economy, the mix of companies behind the data matters as much as the amount.

Left to itself, the supply of company data does not arrive balanced. The companies most likely to know that their records have value, and to come forward on their own, tend to be close to the technology industry. They run modern tools, they follow AI news, and their founders often know people at labs.

That produces a predictable tilt. Software, developer tools and venture-backed startups end up over-represented. Sectors where operations are just as complex, such as logistics, healthcare administration, professional services, manufacturing and financial operations, are under-represented, not because their data is less useful but because nobody asked. We look at the mechanics of this in why inbound data marketplaces have coverage gaps.

A dataset built only from what arrives will inherit that skew. A dataset built to a plan can correct it.

It is worth being specific about what an agent actually misses when a sector is absent. The differences go beyond vocabulary.

• Workflow shape. A software team's work flows from ticket to pull request. A logistics company's flows from quote to dispatch to proof of delivery to invoice dispute. The steps, handoffs and failure modes are different.
• Tool stack. Some sectors run on chat, Jira and GitHub. Others run on email, shared drives, a CRM and an industry-specific system. An agent needs to see work spread across the tools a sector really uses.
• Pace and horizon. Some industries resolve most tasks in hours. Others run projects over months with long gaps, approvals and external parties.
• Compliance and caution. Regulated sectors show how teams handle approvals, documentation and audit trails, which is exactly the behavior enterprise customers will expect from agents.
• Who the customer is. Selling to consumers, small businesses or large enterprises produces very different support and sales records.

None of these can be inferred well from public text. They are visible in the operational record, and only for the sectors that are in the dataset. For the case for depth within a single vertical, see why vertical AI agents need vertical company history.

Sector is the most visible dimension, but a balanced mix also accounts for several others. Each one changes what the data teaches.

Headcount shapes how much coordination is visible. A 15-person company has short chains of communication. A 300-person company has teams, managers, cross-functional projects and formal processes. Both are useful, and a mix of sizes avoids training agents that only understand one.

Years of history affect how many complete projects, cycles and long-horizon tasks the record contains. Region affects language, business norms and regulatory context. Whether a company is still operating or winding down affects what the archive looks like at the end, since a closed company's record has a natural endpoint.

Imagine a lab that wants to train agents for back-office operations. A balanced plan might pair software companies with distribution, staffing, accounting and property management firms, span a range of headcounts, include both North American and European companies, and favor archives with several years of email, CRM and finance workflows. That plan is a sourcing brief.

A sourcing brief is how a lab tells us what it needs. It typically specifies sector, headcount, tool stack, years of history, regions, and whether to target operating companies, companies winding down, or both. The more precise the brief, the more precisely the resulting mix matches what the lab is trying to build. Our post on how to write a good sourcing brief goes into detail.

From the brief, Martal Group builds a target list using company and technographic data. Martal has spent 17 years in B2B outbound, with more than 200 onshore sales reps across the US, Canada, the EU and LATAM, and experience prospecting across 24 industries. Its reps reach the people who can sign by cold calling with a power dialer and signal-driven prospecting, along with email and LinkedIn. That outbound reach is what makes it possible to source from sectors that would never show up on their own.

Signed companies are then evaluated, anonymized and delivered on a rolling basis. A rolling cadence also lets a lab watch the mix as it builds and adjust the brief if one segment is filling faster than another.

Balance only helps if the data is held to the same standard across every sector. Every company's data goes through the same anonymization: every person, company, address, email, phone and account becomes a consistent token across tools, and credentials, API keys, card numbers and government IDs are removed outright. HR, payroll, medical and legally privileged folders are excluded by default, which matters in sectors like healthcare and staffing where those folders can be large.

Each seller sets scope and exclusions before any data moves, and reviews an anonymized sample before delivery. The licence covers training and evaluation only, and buyers may never market to, profile, contact or re-identify anyone in the data. Consistent terms across the mix make it simpler for a lab's own provenance and compliance review.

If your team is planning its next training or evaluation mix and wants sector coverage that does not depend on who happens to come forward, send us a sourcing brief.