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What's the smartest first step to launch a new AI product with a small budget?

GPT-6 Astra · Auto

Validate demand before you build more. With a small budget, the cheapest signal is a real conversation:

Pick one painful use case and write a one-page landing page for it.
Open a waitlist and personally reach out to 50 people who have that problem.
Run 15–20 short calls and ship only what repeats across them.

Spend on ads only once people are asking when they can start.

Now turn that strategy into a short launch email.

Claude Opus 5.5 · Auto

Subject: We built this for the problem you told us about

Hi — a few weeks ago you shared how much time goes into this every week. We listened, and we built a first version around exactly that.

We're opening it to a small group first. Reply "in" and I'll send your invite today — and I'd love 15 minutes of your honest feedback.

Ask Council whether this launch strategy is actually the best approach.

Rileva Council · 3 models

Council · 3 models consulted

GPT-6 Astra6.2s
Claude Opus 5.57.4s
Gemini 3.1 Pro5.8s

The short answer: it's a strong start, but not complete on its own. Keep the validate-first approach and add one channel that reaches beyond your existing network.

Where they agree

Talking to real users before spending on ads is the right first move, and a personal, problem-led email beats a generic announcement.

Where they differ

GPT-6 Astra favours depth: fewer, longer calls, because early learning matters more than reach.
Claude Opus 5.5 would sharpen the message around a single pain point before sending anything.
Gemini 3.1 Pro warns warm outreach alone can't prove wider demand, so it adds one community launch.

What it depends on

If you already have 50+ warm contacts, start there. If not, lead with the community launch.

The models agreed on the core approach and differed on reach versus depth.

GPT-6 Astra
6.2s
Yes—with a tighter definition of success.
The validate-first strategy is the right use of a small budget because it buys evidence before reach. A focused waitlist and 15–20 interviews should reveal whether the problem is urgent, who feels it most, and which objections repeatedly block adoption.
Treat the launch as a learning system rather than a one-day event. Track qualified replies, interview acceptance, and how many people ask to try the product—not impressions or raw sign-ups.
Recommended next move
Set a two-week target: 50 direct conversations, 20 interviews, and five users willing to test or pay. If those signals appear, expand into one public channel and document what changes in a public changelog.
Claude Opus 5.5
7.4s
Mostly—but the message needs to become more specific.
The personal email is a strong starting point, yet it currently leads with the fact that you built something. Prospective users care first about the costly, frustrating moment the product removes. Name one audience, one recurring problem, and one credible outcome.
Keep the invitation small and conversational. Asking for a reply is lower-friction than sending people straight to a generic sign-up page, and it creates the qualitative feedback this stage needs.
Recommended next move
Write three versions around the three strongest pains from earlier interviews. Send each to a small matched group, compare reply quality, then use the winning language on the landing page and in the product itself.
Gemini 3.1 Pro
5.8s
Partly—the plan needs one test beyond your existing network.
Warm outreach is efficient, but it can overstate demand because recipients already know or trust you. That makes it useful for refining the offer, not sufficient for proving that the market will discover and choose the product independently.
Add one concentrated community test where the target audience already discusses this problem. Use the same promise and call to action so the two channels produce comparable evidence.
Recommended next move
Choose one relevant forum, newsletter, or launch community—not several. Compare qualified conversations and activation from warm outreach versus that audience before spending on paid acquisition.

Rileva can make mistakes. Check important information.

OpenAI
Claude
Gemini
Grok
Mistral
Perplexity

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The best-suited model replies, and the answer always says who wrote it.

Research the biggest competitors in the AI-router market.

Perplexity Fastchosen by Auto

I’ll compare the current multi-model products by positioning, model access, workflow, and pricing, then separate direct competitors from adjacent tools.

SourcesMarket mapPricing compared

Same conversation · context retained

Now build the TypeScript architecture for this.

Claude Haiku 5.5chosen by Auto

Start with one routing contract. Keep provider adapters isolated, stream through a shared event shape, and make retries idempotent.

interface Router {
  decide(req: ChatRequest): Promise<RouteDecision>;
  execute(decision: RouteDecision): AsyncIterable<Token>;
}

Same conversation. Different task. Different model.

02 — Council

Ask three models at once. Get one synthesized answer.

Council puts the same question to several leading models, then writes one clear synthesis: where they agree, where they differ, and Rileva’s conclusion. The full perspectives remain available to read.

GPT-6 Luna29.5s

The challenge verdict: don’t commit to the original recommendation as written. It overstates the shared evidence and treats several distinct risks as one decision.

The strongest challenge

Demand matters before scale. Test the premise with a small, reversible commitment.

Claude Haiku 5.525.5s

A challenge round makes the hidden assumption visible. One perspective supports the direction; another shows where it could fail under pressure.

Challenge 1: the riskiest assumption

A technically sound idea can still fail when switching costs or adoption are underestimated.

Grok 4.3 Fast21.5s

The original recommendation is too compliance-centric. The stronger view ranks who pays, how fast, and whether the product is genuinely high-risk.

Where the prior case is weak

European delivery needs evidence that customers will convert before category timing runs out.

1
Three models consult at once.
The same question goes to three strong models chosen for that task, all at the same time. No setup, no model picker.
2
Rileva cross-examines them.
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3
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Stuck on the pricing page again. Feels like we’re going in circles.

Claude Haiku 5.5 · Auto

Recalled from memory

Prefers one clear recommendation
March — onboarding rewrite
Q3 pricing notes.pdf

You’ve been here before. In March the onboarding rewrite stalled the same way — what broke the loop was choosing one audience and shipping the narrow version.

My recommendation: write the page only for the team plan your Q3 notes show already converts, and leave the second tier out until it does.

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GmailRead-only

“What did the supplier say about the delivery date?”

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Claude01
Writing & nuance
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OpenAI02
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Gemini03
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Grok04
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Mistral05
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Perplexity06
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Separately5 subscriptions

ChatGPT Plus~€20
Claude Pro~€20
Gemini Advanced~€20
SuperGrok~€30
Perplexity Pro~€20

5 logins. 5 invoices.

~~€110+~~/month

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Index

Leading models. One workspace.

These are the models Rileva can use right now. Auto chooses among them; you can also select one yourself.

OpenAI
GPT-6 LunaGPT-6 SolGPT-6 Astra
Claude
Claude Haiku 5.5Claude Sonnet 5.5Claude Fable 5.1Claude Opus 5.5
Gemini
Gemini 3.8 FlashGemini 3.1 Pro
Grok
Grok 4.3 FastGrok 4.7
Mistral
Mistral Small 4Mistral Large 3Magistral Medium
Perplexity
Perplexity FastPerplexity Research

Image generation

OpenAI Image
GPT Image Flare (Quick)GPT Image Sunburst (Best)
Gemini Image
Nano Banana 2 (Quick)Nano Banana Pro (Best)
Grok Imagine
Grok Imagine (Best)

Ask for an image in any chat — one engine, or several side by side to compare.

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No training
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Read-only
Read-only connections
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Common questions

Clear by design.

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: One place to work with leading AI models. You ask a question; Rileva sends it to an appropriate model and shows you which one answered.
: Auto reads the request and routes it to the model whose strengths fit it — careful reasoning, nuanced writing, live research, large files, or speed. You can lock a model or change it at any point.
: Council asks three strong models the same question, compares where they agree and disagree, and writes one considered answer. You can open the comparison and read each perspective.
: The models listed here are the ones currently available. Auto chooses among them, and you can select one manually in a chat.
: Yes. Conversations, uploaded files and generated images are saved to your workspace, and projects keep related work together. Sign in to keep it across devices.
: No. Your conversations, files and connected accounts are never used to train AI models — not by Rileva, and not by the model providers we route to. Your questions are sent to a model only to produce your answer.
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Stop choosing between AI models.

Where they agree

Where they differ

What it depends on

Recommended next move

Recommended next move

Recommended next move

Ask once. Rileva chooses who answers.

You ask one question.

Rileva understands what it needs.

The right model answers.

Ask three models at once. Get one synthesized answer.

The strongest challenge

Challenge 1: the riskiest assumption

Where the prior case is weak

Three models consult at once.

Rileva cross-examines them.

You can inspect every perspective.

Intelligent memory across every model.

Rileva learns how you work.

It recalls what matters.

Any model picks up where you left off.

Your tools, in every answer.

Different tasks need different strengths.

Writing & nuance

Planning & logic

Files & multimodal

Current & direct

Speed & languages

Live research

Replace every AI subscription with one workspace.

Plus

Pro

Max

Leading models. One workspace.

Image generation

Your data stays yours.

No training on your data

Read-only connections

You stay in control

Clear by design.

Rileva on your desktop and phone

Ask one question. Get the right answer.