01 / BUYER STRATEGY
Know what to look for.
Investigate how customers buy and which early changes create a reason to reach out.
Compare Teevia with the alternatives
Apollo, ZoomInfo, Clay, AI agents—or Teevia?
Apollo and ZoomInfo help build and enrich lists. Clay and Trayo support configurable GTM workflows; Gojiberry offers AI-led prospecting and outreach. Teevia pairs people and platform to design and run your buyer-specific intelligence operation.
Your sales intelligence team. Your AI-powered platform. One subscription.
We find who matters, why now may matter, and a credible way to connect.
THE OPERATION BEHIND THE OPPORTUNITYTEEVIA / CORE
The questionWhich companies might be preparing to buy—and what evidence would tell us before they announce it?
What Teevia engineers and operates
01 / STRATEGYUnderstand how buyers buy
02 / SOURCINGFind the early evidence
03 / RESEARCHResolve the people involved
04 / OPERATIONSValidate, monitor & refine
What the sales team getsWho. Why now. How to connect.
STRATEGY designed for your buyerWORKFLOWS managed by our teamOPPORTUNITIES with context
Compare the alternatives
From contact lists to managed opportunity intelligence.
The key difference is what you get—and who has to make it work. Select a view to compare the alternatives.
Explore the difference
Apollo ↗B2B lists & enrichment
B2B contact and company search, enrichment, and outreach sequences; also offers buying-intent filters.
Best for teams building prospect lists and finding accurate contact details.
ZoomInfo ↗B2B data & enrichment
Extensive company and contact intelligence, enrichment, org context; also offers intent tools and automation.
Best for teams that need broad data coverage and enriched CRM records.
Clay ↗GTM workflow building
Combine enrichment, research, AI, and custom signal workflows.
Strong fit for teams that want to design and maintain their own GTM processes.
Trayo ↗Agent-ready GTM workflows
Prospect discovery, research, enrichment, signals and agent-connected workflows.
Useful when your team wants GTM capabilities inside agents and custom processes.
Gojiberry ↗AI prospecting & outreach
AI agents source and score prospects using buying and social signals, enrich contacts, and run LinkedIn/email outreach.
A ready-to-use, signal-led outbound system with optional human approval and custom-plan services.
DIY AI agentsDIY AI automation
Custom research, agent-driven browsing, tool connections and workflow development.
You choose the strategy, build the stack and maintain its quality.
TeeviaPeople + Platform
A managed intelligence operation built around how your buyers make decisions.
Core sources researched leads. Outreach adds scoped, human-managed email, LinkedIn and phone campaigns.
Categories reflect each company’s primary use, not exclusive features. Apollo and ZoomInfo offer intent signals too; both are especially useful for lists and enrichment. Features vary by plan. Reviewed October 2026.
The bigger question: who decides what signals matter—and keeps the operation running?
See the example ↓Our commercial construction example makes that work visible.
One People + Platform operation, two ways to use it. Core finds researched leads. Outreach adds AI-assisted, human-managed email, LinkedIn and phone outreach in agreed channels.
See Core vs. Outreach →Real campaign · Michigan commercial construction
$2M+ in pipeline in just 2 months.
A Michigan commercial builder wanted to reach developers before projects were publicly announced. We spotted an early clue in property-related LLC registrations, then built a research operation that screens hundreds of filings weekly and turns promising signals into relevant sales conversations.
$2M+in pipeline in just 2 months
We built a custom workflow around an unusual early signal.
01
Study the buyer journey
What happens before a developer starts evaluating builders?
02
Monitor new LLCs
Identify registrations that resemble property addresses.
03
Check the property
Investigate commercial vs. residential relevance.
04
Find who's behind it
Research owners and decision-makers beyond registered agents.
05
Connect the evidence
Cross-check permits, planning information and related activity.
06
Find a way in
Explain why it matters and look for a relevant relationship path.
The buyer-journey insight came first. Our people identified the early clue. Our platform and engineering made the research repeatable. Together, they helped turn hard-to-see signals into real commercial conversations.
What does the seller actually receive?
See the opportunity output →See the buyer context, evidence and suggested next move inside a Teevia opportunity.
We tested the DIY approach
Couldn't you just ask an AI agent to do this?
We tested that question with Meta’s Muse. It found records, but turning the task into a dependable weekly workflow took much more direction.
The initial assignment
“Find newly registered Michigan LLCs.”
A straightforward objective. An agent can browse, research, and even help build tools. But a business-ready system also needs the right search criteria, complete enough coverage, contact resolution, QA, and repeatability.
Teevia agent experiment · 2026 · A point-in-time test, not a universal AI benchmark.
What actually happened
Progress was possible. The exceptions kept multiplying.
01 / The original source was blocked by a CAPTCHA.
The agent found another source, which required account registration and had free-account limitations.
02 / The initial output was only about 10 LLCs.
We had to correct criteria and repeatedly guide the agent to expand its search to more than 100 registrations.
03 / Finding the actual people was harder.
Its first contact pass surfaced details for roughly 5 of the 100+ records. We explained registered-agent exceptions and alternative research methods.
04 / More prompting helped—but only so much.
It located about 10 additional contacts. Other records needed enrichment capabilities that weren't available through the information it was accessing.
05 / Reliability became the next problem.
After about a week of refinement, a subsequent run encountered an IP access block, introducing another operational issue to solve.
And that was one workflow.
We hadn't yet added the full set of property checks, evidence assessment, relationship mapping, monitoring, exception handling and delivery into the customer's sales process. Nor had we solved what happens when sources and access conditions change.
Point-in-time experiment (2026), not an AI benchmark. We have not established exact test dates; newer agent versions may perform differently. Reliable operations use authorized data access, APIs and source-appropriate controls.
What a prompt cannot own
AI can build the workflow. Who runs it next week?
The value is not just the agent. It is the strategy, engineering and accountability that keep useful opportunities coming.
02 / ENGINEERING
Connect the evidence.
Build sources, enrichment, identity matching and exception handling into a repeatable system.
03 / OPERATIONS
Make it dependable.
Review quality, repair integrations and refine the research as markets and tools change.
Could you build it yourself? Absolutely.
Compare the economics →The trade-off is taking on all that ongoing work. See the illustrative cost comparison on Why Teevia.
See it applied to your business
What signals are hiding in your buyer's journey?
We'll look at your business, work backward from how your customers decide to buy, and identify the moments that may be worth watching. Start with a strategy—not another login.
Get your free growth strategy.
Share your website. We'll outline where we would look for opportunities in your market.
- How your buyers move toward a decision
- Early indicators worth investigating
- Potential data sources and research plays
- Where we'd recommend starting
Free. No credit card. No sales call required to receive it.
Straight answers
A few questions worth asking.
Why not just use Apollo or ZoomInfo?
Both are particularly useful for finding companies and contacts, enriching records and building prospect lists. They also offer buying-intent capabilities. Teevia addresses a different job: our team researches buyer-specific opportunities and keeps the intelligence operation running.
What about Clay or Trayo?
Clay offers configurable enrichment and workflows; Trayo describes signals, research and agentic activation. Teevia adds a managed team that develops the buyer-specific research strategy and owns the agreed intelligence workflow. Outreach can also execute a scoped contact campaign.
How is Teevia different from Gojiberry?
Gojiberry offers AI-led signal discovery, prospect scoring, enrichment, and automated LinkedIn/email outreach. It is a strong option when you want a ready-to-use prospecting and outreach tool. Teevia combines people and platform: our team studies your buyers, designs tailored signal research, and runs the agreed intelligence operation. Core delivers researched leads; Outreach can also manage a defined campaign. The distinction is operating model and research strategy—not a claim that Gojiberry lacks buying signals.
Could an agent in ChatGPT, Claude or Muse do all of this?
It could help build substantial parts of the operation, and these tools continue to improve. The practical questions are who develops the initial buying-signal strategy, connects and maintains authorized data sources, validates outputs, handles exceptions and remains accountable for the ongoing result. Teevia takes on that role for the agreed campaign scope.
Does Teevia handle outbound too?
Yes, with Teevia Outreach. Core runs Lead Sourcing Campaigns and delivers research-backed leads for your team to contact. Outreach adds AI-assisted, human-managed email, LinkedIn and phone outreach in agreed channels, plus response monitoring and handoff. Your team owns relationships and closing.
Do I need a large sales team to benefit?
No. The best fit is a business that has a clear customer profile and the ability to act on well-researched opportunities, even if that means a founder or one salesperson. The free growth strategy helps evaluate whether your market has useful observable signals.
About this comparison. Vendor descriptions are based on public product positioning reviewed October 18, 2026. Features, packaging and operating models may evolve. Sources: Apollo, ZoomInfo, Clay, Trayo, Gojiberry, Meta Muse. The Michigan LLC research and Muse experiment are Teevia's self-reported experience; they are not independent comparative benchmarks. Trayo also markets agentic research and outbound capabilities. No claim is made that another product cannot support a particular workflow.