You don't need a $300/month ASO suite to do solid keyword research. An LLM can genuinely speed up the brainstorming and clustering stages — but it will also confidently hand you a search-volume number it invented. Here's a workflow that uses AI for what it's actually good at, and keeps humans and real data in the loop for what it isn't.
Most keyword research advice assumes you already have a paid tool showing you exact search volume and difficulty scores. If you're a solo developer or a small team without that budget, the honest starting point is: AI can replace the tedious brainstorming and organizing work, but it cannot replace the data you'd normally get from App Store Connect, Google Play Console, or a paid ASO tool's own measured rankings.
Large language models are trained on huge amounts of text, which makes them good at generating plausible keyword variations, synonyms, and category-adjacent phrases quickly. What they are not good at — structurally, not as a matter of which model you pick — is reporting an exact, current search volume number, because that number isn't something they were trained to measure; it's something a search engine or app store computes from live query logs the model never had access to. When an LLM gives you a specific volume figure with no cited source, treat it as fabricated until you check a real source, not as an approximation.
Conceptual workflow split — not a specific tool's actual architecture.
Start by describing your app plainly to an LLM — what it does, who it's for, what problem it solves — and ask for a large list of candidate keyword phrases across categories: functional (what the app literally does), problem-based (what pain point it solves), audience-based (who uses it), and competitor-adjacent (features similar to known competitor apps, without using their brand names in the keyword field itself, which most platforms restrict). The goal here isn't precision, it's coverage — you want more raw candidates than you'll ultimately use, because the next steps are about filtering, not generating.
A common mistake is grouping keywords by how similar the words look. What actually matters for ASO is grouping by what the searcher is trying to accomplish — a "budget tracker for freelancers" searcher and a "budget tracker for couples" searcher are both searching "budget tracker," but they're different audiences with different creative and messaging needs. Try the clustering demo below with a few keyword phrases to see the difference between surface clustering and intent clustering.
Simple illustrative clustering based on shared audience words ("freelancers", "couples") in this demo — a real workflow would use an LLM call or embedding similarity, not this basic keyword match.
This is the step AI cannot do for you. On iOS, the closest thing to real signal without a paid tool is Apple Search Ads' keyword suggestions and the "popularity" score visible when setting up a Search Ads campaign — it's a relative popularity indicator, not raw volume, but it's real platform data, not a language model's guess. On Google Play, Play Console's own store performance reports show which search terms are already driving impressions and installs to your live listing, which is real, first-party, and free. Any keyword an LLM suggested that you haven't checked against one of these is still just a hypothesis.
Once you've filtered a candidate list down to keywords with real supporting signal, the next job is deciding where each one goes. On iOS, Apple's own guidance treats the title, subtitle, and hidden keyword field as three separate opportunities — a keyword repeated across all three doesn't add extra ranking weight, so the efficient move is to place your single highest-priority term in the title, your next tier in the subtitle, and reserve the 100-byte keyword field for net-new terms: synonyms, long-tail modifiers, and competitor-adjacent phrases that didn't fit elsewhere. On Google Play, there's no separate hidden keyword field — the algorithm reads your title, short description, and full description directly, so validated keywords need to be woven into readable sentences rather than just listed, which is its own small writing exercise AI can help draft but shouldn't be trusted to finalize unsupervised.
A keyword's real search demand can vary sharply between markets — a term that's strong in U.S. English search behavior might be rare or phrased completely differently in another locale. If you're localizing your listing, treat each locale as needing its own validation pass against real data for that market, not a direct translation of your English-validated list. An LLM can help generate translated candidates quickly, but the same rule applies: verify before you commit character-limited metadata space to a translated guess.
Frontier language models have gotten meaningfully better at avoiding fabrication on general factual questions, but domain-specific and numeric claims remain a weak spot — research tracking hallucination rates across 2025-2026 still finds meaningful error rates on numeric and specialized claims, especially when a model isn't citing a live source. A search-volume number, a competitor's exact download count, or a claimed ranking position are exactly the kind of specific numeric claims where this risk is highest. Treat any such number from an LLM as a hypothesis to verify, never as a citation-worthy fact on its own.
Directionally true, unverified: exact hallucination rates vary widely by model, task type, and whether the model has live search grounding — don't treat any single published percentage as a fixed, current number for a specific tool you're using; verify against your own tool's stated capabilities.
Say you're researching keywords for "InvoiceEasy," a freelancer invoicing app. These are illustrative numbers to show the shape of the workflow, not real measured data.
| Candidate keyword | Source | Validated? |
|---|---|---|
| invoice maker freelance | AI brainstorm | Confirmed — appears in Apple Search Ads suggestions |
| invoice tracker small business | AI brainstorm | Confirmed — real Play Console search-term impressions |
| invoice app (12,400 searches/mo) | AI brainstorm, with a specific number attached | Rejected — no real source for the number, treated as fabricated until otherwise shown |
The lesson isn't "don't use AI" — two of the three candidates were genuinely useful starting points. It's "never ship a specific volume number an AI invented without checking it against a real platform source first."
Illustrative relative time allocation, not a measured benchmark — the point is the shift in where time goes, not the specific hours.
This workflow is deliberately scoped for a solo developer or a two-to-three-person team without a dedicated ASO budget — it isn't a substitute for a full paid ASO suite if you're operating at a scale where ranking-position tracking, historical trend data, and automated alerting genuinely pay for themselves. The trade-off is explicit: you give up continuous automated monitoring and precise historical volume trends, and in exchange you get a process that costs an LLM subscription (or nothing, if you're using a free-tier model) plus the free, first-party validation sources every developer already has access to through their own App Store Connect or Play Console account. For most early-stage apps, that trade is a reasonable one — the bigger risk isn't under-investing in tooling, it's skipping the validation step entirely and shipping AI-generated guesses straight into production metadata.
Keyword research isn't a one-time setup task. Search behavior shifts with seasonality, platform algorithm changes, and competitor activity — a term that validated well six months ago can quietly lose relevance without any signal reaching you unless you check again. A practical cadence for most small teams is a light validation pass monthly (just re-checking your existing keyword set against current Apple Search Ads popularity or Play Console search-term data) and a full brainstorm-and-cluster refresh quarterly, or after any major app update that changes what the app actually does. The AI-assisted steps (brainstorming, clustering) are cheap enough to run more often than the validation step, since validation is the part that costs real time against a live data source — which is exactly why it's worth being disciplined about not skipping it even when it's tempting to just ship the AI's raw list.
Use AI to generate and cluster a wide net of keyword candidates fast — it's genuinely good at that. Never trust a specific numeric claim (volume, difficulty, competitor data) from an AI without validating it against a real platform source like Apple Search Ads or Play Console. The workflow that actually works treats AI as a first-draft engine and real platform data as the only source of truth for anything you'd stake a metadata decision on.