From Signup to Your First 50 Qualified Leads

This page walks the entire Pioneer journey once, end to end. By the time you finish it, you'll know how to go from a brand-new account to a board of qualified leads, with a named decision-maker and a verified email on the best ones, because you'll watch one founder do exactly that. Every other page in this Help Center goes deeper on one step of this journey. This page is the map.

Meet Maya, who needs customers no database can list

Maya Torres is the founder of Calyx Thermal, a seed-stage climate-tech company. Calyx makes thermal batteries: insulated boxes of very hot bricks that let a factory replace its gas boiler with cheap off-peak electricity. The product works. Her problem is distribution. She needs five pilot customers this year, and the right pilot customer is weirdly specific. A mid-size food or beverage processor in the western US. Steam-heavy production. Aging gas boilers. Ideally, a public commitment to decarbonize.

No database sells that list. LinkedIn can filter by industry and headcount, but it can't read a bond filing and notice that a tomato processor in Fresno still runs two boilers from the 1990s. Finding Maya's customers isn't a filtering problem. It's a research problem, the kind that used to take a skilled human dozens of hours.

That research problem is exactly what Pioneer does. The rest of this page follows Maya from her first login to a CSV of qualified leads, and explains why each step works the way it does.

Sign up, and check whether a pipeline is already waiting for you

Maya first heard about Pioneer through a short email that linked to something unusual: a live pipeline Pioneer had already built about Calyx's problem, with real researched leads in it. When she signs up with her work email, that pipeline is transferred into her brand-new workspace automatically, already populated.

Why Pioneer does this: the hardest claim any research tool can make is "our research is actually good." A pipeline about your own problem, full of leads you recognize as plausible, makes the claim self-evident. So when Pioneer reaches out to founders, the product itself is the demo.

If there's no pipeline waiting for you, you lose nothing. Onboarding (next step) builds the same thing in a few minutes, and every step after that is identical.

Tell Pioneer who you are and what you're trying to accomplish

Right after signup, Pioneer asks Maya three things: her name and role, optional context about her company, and the important one, her purpose: what she's trying to accomplish. She writes: "Find pilot customers for industrial thermal batteries. Food and beverage processors with big heat loads."

While she types, Pioneer is already researching her professional background and her company from the public web. But her stated purpose is weighted far above anything Pioneer infers: a person's own words about what they want beat any guess. That purpose powers recipes, which are specific, ready-to-run pipeline suggestions on the /pipelines page. The more specific your purpose, the sharper the recipes ("Series A battery-recycling startups in the EU," not "companies in energy").

Two buttons matter on each recipe: Try this pre-fills a pipeline you can still edit before creating, and Not for me dismisses it, which also teaches Pioneer, so the next batch of recipes gets closer. See Creating Your First Pipeline for the full creation flow, including starting from scratch.

Open your pipeline: it's a question Pioneer keeps answering

Maya opens her pre-built pipeline. The best way to understand what she's looking at: a pipeline is a research question she asks once, that Pioneer keeps answering. Hers is "Find mid-size food and beverage processors in the western US with steam-heavy production and aging gas boilers, that have signaled intent to decarbonize."

The pipeline is a board. On the left sits the Preferences column, the pipeline's control center, showing what it's looking for. To the right, leads flow through columns: Leads (researched leads waiting for review, best matches first), Relevant (the first custom stage, seeded for every pipeline), any custom stages you add, Done, and Rejected.

Before touching any leads, Maya reviews what the pipeline believes. That's the next step, and it's the most valuable five minutes in all of Pioneer.

Teach Pioneer what a great lead looks like

In the Preferences column, Maya clicks the pencil to open the editor and reads the pipeline's description and criteria, the rules that define a good fit. Criteria deliberately do double duty: they guide discovery (what Pioneer searches for) and they drive qualification (how every found lead is judged, with evidence). That's why teaching them pays twice.

Criteria aren't filters. Each one is a plain-language description of something Maya is looking for, and Pioneer judges every lead against it individually, researching that specific company and deciding how strongly it matches what the criterion is asking for. Direct, unambiguous evidence is a strong match; one indirect signal is a moderate one. That judgment is made per lead, from that lead's evidence, which is why two companies can both satisfy the same rule and still sit far apart on the board.

What the two tiers do is tell Pioneer which of those descriptions are non-negotiable:

• MUST criteria are deal-breakers, and the relevance gate. A lead that fails any MUST is not relevant. Mechanically, no exceptions.
• SHOULD criteria say what Maya values without requiring it. They lift a lead's position on the board, but never disqualify.

Maya edits hers down to:

• MUST: Food or beverage processor that operates its own production facilities
• MUST: Mid-size, roughly 100 to 2,000 employees
• MUST: Facilities in the western US
• SHOULD: Steam-heavy processes (cooking, sterilizing, evaporation)
• SHOULD: Evidence of aging natural-gas boilers or planned boiler replacement
• SHOULD: A public decarbonization commitment

Why two tiers instead of one list? Collapse them and every preference becomes a veto, or every requirement becomes negotiable. The MUSTs are Maya's contract with Pioneer; the SHOULDs are her taste. Keeping them separate is what lets the relevance verdict be honest later. It's also why a vague description is expensive: Pioneer has nothing concrete to judge a lead against for "should be innovative," so it muddies every lead's position.

Notice what "aging gas boilers" is doing as a SHOULD. It's the kind of thing no database has a column for, and exactly the kind of thing web research can find. Write criteria around evidence that exists publicly. Three to five criteria is plenty to start; you'll refine them once you see results.

Watch your first leads arrive, already researched

With preferences saved, Pioneer gets to work. Discovery is not a keyword search. Pioneer explores the web the way a diligent human researcher would: running multiple search strategies, following promising results deeper, adjusting based on what it finds, and continuing until it's confident it has covered the space.

While that runs, an Incoming box sits directly above the Leads column showing every lead mid-research with a live progress indicator. Each candidate is checked to be the right kind of thing (a company, not a listicle about companies), resolved to its own website rather than a directory page, deduplicated against the pipeline, and then researched: content read, a summary written, and every one of Maya's criteria evaluated against evidence found on the web.

If research on a lead fails or stalls, Pioneer shows it (a warning icon and a Retry button) rather than letting the lead silently vanish. A lead that disappears without explanation would make you doubt every lead that remains, so failures stay visible and fixable.

Two things worth knowing while you watch:

• What this costs: each new lead Pioneer discovers (or you import) spends one credit. Everything after that (the deep research, criteria evaluation, properties, people) is free. You never pay to learn more about a lead you already have. See Credits & Billing.
• You can close the tab. Discovery runs in the background, and when it finishes, Pioneer emails you a summary with your leads attached as CSV files.

Open a lead and read the evidence

An hour later, Maya's Leads column holds a ranked batch. Top of the column: Copper Kettle Foods, a tomato processor in Fresno. She clicks the card.

The detail view opens with a one-paragraph summary, the TL;DR for deciding whether this lead deserves attention. Its first words are the verdict in plain terms: Copper Kettle's summary opens "Relevant because…", which means every MUST criterion passed. A "Mostly relevant" or "Partially relevant" opener means a deal-breaker failed. Below it, the criteria results: every criterion with a verdict (Met, Not Met, or Inconclusive) and the reasoning behind each one, written from what Pioneer actually found on this company rather than from a lookup. For Copper Kettle, all three MUSTs are met, and the "aging boilers" SHOULD cites a municipal bond filing that names two 1990s-era gas boilers at the Fresno plant. The decarbonization SHOULD cites the company's 2025 sustainability page and its 40% emissions-reduction target.

Here's the rule that makes the board trustworthy: a lead is relevant when, and only when, it meets every MUST criterion. The decision is mechanical. Pioneer's AI writes the summaries and gathers the evidence, but it never overrules your rules. You wrote the standard; the standard decides.

The ordering is a separate question from the verdict. A lead's position comes from adding up how strongly it matched each criterion, with a met MUST weighing substantially more than a SHOULD, enough that relevant leads always sit above ones that failed a deal-breaker, while within each group the strongest evidence rises first. Copper Kettle is top of the column not because it scraped past three MUSTs, but because it matched them convincingly and picked up two SHOULDs with hard evidence. Criteria Pioneer couldn't evaluate at all count for nothing in either direction.

Inconclusive means Pioneer couldn't find enough public evidence either way: an honest "don't know" instead of a confident guess. If one criterion comes back inconclusive on many leads, it's probably not answerable from public information; Refining Your Pipeline covers how to rewrite it. For how Pioneer keeps evidence honest, see Accuracy.

Triage the board: every card you move teaches Pioneer

Now the judgment call that only Maya can make. She works down the Leads column: Approve the ones worth pursuing (they move to the Relevant stage, the first custom stage every pipeline is seeded with), Reject the ones that aren't a fit (they move to Rejected, recoverable anytime, nothing is deleted).

She approves Copper Kettle Foods and four others. She rejects a beverage brand that outsources all its production: technically food and beverage, but it doesn't run its own plants, so there's no boiler to replace.

Then she makes the board hers with custom stages between Relevant and Done, "Reached out" and "Site visit scheduled", matching how Calyx actually sells. Pioneer doesn't impose a sales methodology; after leads land, the board is your working surface. (The Done column is always there as the finish line; rename it to "Pilot signed" if that's what done means to you.)

Here's why triage matters beyond bookkeeping: every move is teaching. Approvals and rejections are ground truth about what a good lead looks like, stronger evidence than anything the algorithm believed. A lead you approve even though it wasn't relevant tells Pioneer your criteria are too strict; a rejected relevant lead says the opposite. Pioneer uses exactly this signal in the next step. And not only there: when Maya asks for more leads later, the discovery run reads her board directly (more like what she kept, none of what she rejected on sight).

Steer the next batch in plain language

Maya's first batch had a pattern: too many beverage brands, not enough actual processors. She doesn't need to hand-edit criteria to fix that. In the Preferences column:

• Recommended actions: Pioneer analyzes the pipeline (your criteria, your properties, and how leads actually scored) and suggests up to three concrete improvements. Clicking one shows the exact change for review; nothing is ever applied silently.
• Steering: the text box at the bottom of the column takes plain-language instructions. Maya types: "fewer beverage brands, more processors that run their own plants." Pioneer turns that into a specific criteria change and shows it to her before anything is applied.

Review-before-apply is deliberate everywhere here: the criteria are your contract with Pioneer, and Pioneer never rewrites the contract behind your back. The full refinement loop (review, spot the pattern, refine, run again) is covered in Refining Your Pipeline.

Ask for more leads, or bring your own

With sharper criteria, Maya wants a bigger batch. The control lives where leads land: the more leads button in the Leads column header. It offers two paths:

• We find them for you: pick a count and click Find leads. Discovery runs again with everything the pipeline has learned.
• Upload your own list: paste anything, whether rows copied from a spreadsheet, company names, emails, or links. Maya pastes six processors from a conference attendee list. Each line becomes a lead that gets the exact same research and qualification as discovered ones; origin never changes rigor. See Importing Your Own Leads.

A pipeline runs one lead-adding job at a time, a search or an import, never both at once, so progress stays legible and nothing is double-spent. Repeat the loop (generate, triage, steer, generate) and fifty qualified leads is a matter of a few passes, not a few weeks.

Find the person you'd actually email

A qualified company is half an answer. Maya's real question about Copper Kettle is operational: who owns the boiler decision?

Properties are custom research questions Pioneer answers for every lead in the pipeline. From the Preferences column editor, Maya adds three: "Primary process fuel" (text), "Number of facilities" (number), and the important one, "VP of Operations" with the type set to Person. A Person property doesn't return a text snippet: Pioneer identifies the actual human in that role at each company and researches them.

On the Copper Kettle card, the property resolves to Dan Whitfield, who appears as a clickable chip. His contact card shows a short About (Maya's personalization material: he spoke on an industrial-electrification panel in March), his email with a verification badge showing it's deliverable, his LinkedIn, and his other channels.

Every detail on that card was found, never guessed. Emails are never pattern-guessed from name@company conventions; each channel comes from a real page Pioneer actually read, and emails are verified for deliverability before you spend a send on them. If a channel couldn't be found, the card says so: an honest blank beats a confident mistake, because one wrong email costs more than a hundred empty fields. The full story of champions and contact cards is in Finding the Right People.

Find a warm path to the person you want to reach

Maya has a name, a profile, and a verified email. But before she cold-emails, it's worth checking whether someone she knows can introduce her. On any person card, click Find warm connection: Pioneer searches your network for people who know this person, and suggests a short opening. A warm introduction converts far better than a cold one, and Pioneer does the matching work.

Read more about My Network.

Export your board and go get the meeting

Time to act on all of it. Maya clicks Export in the header of the Relevant stage. Pioneer downloads a single CSV: every analyzed lead with its name, website, its relevance tier, its one-line summary, a link back to its full research, and one column for every property. Dan Whitfield doesn't export as an ID or a blob: person properties are split into separate columns for name, LinkedIn, email, and other contact details, ready for a CRM or an outreach tool with zero cleanup.

She also has the results email from each discovery run: summary on top, two CSVs attached, her relevant leads, and the leads that weren't relevant. The second file is worth a skim: seeing what got filtered out is the fastest way to check your criteria are drawing the line where you meant to. Details in Exporting Your Leads.

Her co-founder wants the board itself rather than the spreadsheet, and he has no Pioneer account, so Maya clicks Share in the pipeline header and emails him his own read-only link: Sharing a Pipeline.

Maya's first week with Pioneer ends with a board of qualified processors, a named decision-maker with a verified email on each approved lead, and a spreadsheet her outreach runs on. That's the whole journey, and every pass through the loop makes the next one better.

Where to go deeper

Each step of this walkthrough has a page that goes further:

• Creating Your First Pipeline: the creation flow, step by step
• Criteria: writing MUST and SHOULD criteria that actually work
• How Discovery Works: what Pioneer does differently from a database
• Refining Your Pipeline: the review, steer, regenerate loop
• Finding the Right People: champions, contact cards, and verified channels
• Importing Your Own Leads and Exporting Your Leads: getting leads in and out
• Sharing a Pipeline: a read-only view of the board for people outside Pioneer
• My Network: warm paths and asks from the people you already know
• Getting High-Quality Leads: habits of teams whose boards stay good

Need help?

If you have questions, reach out to us at support@pioneerclimate.com