Comparison
This is the objection we hear most often, and it deserves a straight answer rather than a defensive one: if an AI assistant can research a company, find the right person and write the email, why pay for a product that does the same thing? Much of the premise is correct. This page is about the part that is not, which turns out to be less about intelligence and more about who maintains the state.
8 dimensions comparedLast reviewed Written by Alfa, so read the concessions first
The short answer
You are working a handful of accounts you have already chosen, and you want one tool that does everything rather than a specialist. A frontier assistant costs a fraction of any GTM product, and the same subscription that helps you prepare for a meeting also writes your board update and reviews your code. If you close ten large deals a year, you likely do not need a prospecting system at all — you need a good assistant and a list you keep in your head.
You need over two hundred accounts kept current without you prompting anything. The work that breaks the do-it-yourself approach is not the research on any single company — an assistant does that well — it is maintaining state across a book: which accounts have been surfaced, which were dismissed and why, what changed at each one this week, who has already been contacted, and which are newly worth a look. That is a systems problem rather than an intelligence problem, and it is the whole of what Alfa is.
We are not really on opposite sides. Alfa is built on large language models routed through OpenRouter, so depending on the task it may well be calling Claude to do exactly the reasoning described here. The product is the system around the model, not a substitute for one.
At a glance
Head to head
On the 8 dimensions below we give 5 to Alfa, 2 to Claude and 1 even. We publish the scoreline because a comparison in which the author sweeps every category is not a comparison.
The public web at the moment you ask. Excellent at reading what is published, and bounded by whatever a browsing agent can reach in that session.
A proprietary database built up over time, including historical job-ad data that is not sitting on the open web for an agent to fetch. That history is what makes a timing signal legible in the first place.
A consumer subscription costs less per month than most lunches, and the API is usage-priced. For an individual doing modest volume this is very hard to argue with.
Meaningfully more per month, because you are paying for the data layer, the continuous refresh and the state, not for the reasoning.
It is a general assistant. The same subscription writes your board update, debugs a script and summarises a contract, which is real value a single-purpose tool cannot offer.
Does one job, and that specialisation is the point — high-quality outbound is the priority, not breadth. If prospecting is not currently your bottleneck, that is a narrow return on the money.
It will answer the question well if you ask it. But it answers when prompted and does not hold a view on your market that persists and improves between conversations.
The standing job. Alfa maintains a live view of which companies and champions are in the market for what you sell and reorders it as the market moves, without being asked.
Every conversation starts largely fresh. Keeping two hundred accounts current means you re-running the same prompts on a schedule and holding the results somewhere yourself.
The state is the product. Surfaced, dismissed, contacted and newly-timely are tracked per account, and the stream refills as things change.
No verified contact layer. It can tell you which role to target and often name the person, but it is not a source of verified work emails and will sometimes be confidently wrong about who holds a job.
Verified champion records including the LinkedIn profile and a verified work email, with the reasoning attached — a data problem solved with data rather than inference.
None of it. Drafting is where an assistant stops — sending, sequencing, reply handling and CRM sync are yours to do in other tools.
Focus mode sends over email or LinkedIn, tracks replies, and syncs to HubSpot, Attio or Salesforce on the Growth plan.
You do, with excellent help. Every cycle needs a person to initiate it, which is fine at ten accounts and the binding constraint starting at over two hundred.
The stream runs whether or not you open it, so the queue is full when you sit down rather than after an hour of prompting.
Credit where it is due
Switching
There is nothing to migrate, and the honest test is a fortnight of your own time. Work your accounts the way you do now, with an assistant helping on research and drafting, and count the hours spent deciding who to contact rather than contacting them. If that number is small, you do not need us. If it is a day a week and rising as the book grows, that is the cost Alfa removes — and you can keep using an assistant for everything else.
Method
This is the least like-for-like page on the site, and we have tried to write it without the usual trick of comparing a general tool to a specific one and declaring the specific one better. A general assistant is not trying to be a prospecting system, and judging it as a failed one would be dishonest.
So the dimensions here are the ones a person actually weighs when deciding whether to buy a product or do it themselves: depth, cost, generality, continuity and maintenance. Two of the eight go to the assistant, which is roughly what we would expect given that most people asking this question are right to ask it.
One disclosure, since it bears on the page’s credibility: Alfa is built on large language models routed through OpenRouter, and may use Claude among others. We are not comparing ourselves to a rival technology. We are arguing that the system around the model is the part worth paying for, and if you do not need that system, you should not buy it.
How it works
Not a filter set, not a saved search. Alfa reads the product, the problem it solves and who it is for, then works out which companies are in the market for it right now.
A stream refills as companies raise, hire, ship or change motion. There is no list to rebuild each quarter, because the list was never a snapshot.
The signal that triggered the match, the person inside the account most likely to care, and a first message written from both. You edit and send.
FAQ
For a small book, you probably can, and we would rather say so than pretend otherwise. The approach breaks on continuity: an assistant answers when prompted and starts each conversation largely fresh, so maintaining over two hundred accounts means you re-running prompts on a schedule and holding the state yourself. That maintenance is the job Alfa does.
Alfa routes through OpenRouter and uses different models for different tasks, so depending on the job it may be calling Claude. We think that is the right way round: the model is a component, and the product is the data layer, the continuous refresh and the state tracking built around it.
A capable person with tool access could build a decent version, and some have. The realistic questions are whether you will keep it running once the person who built it is busy, whether it will have verified contact data, and whether the time it takes is better spent selling. Those are judgement calls about your team, not about the technology.
Nothing about reasoning. The differences are all systems: verified contact records, a persistent view of which accounts have been surfaced, dismissed or contacted, continuous refresh as companies hire and raise, deliverability, and CRM sync. Those are engineering and data problems that sit around a model rather than inside one.
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