Eight ways to use AI language models in your SaaS product

Executive overview

Most founders are thinking too narrowly about AI — treating it as an SEO content tool. The real opportunity is anywhere your customers have to write something, or anywhere you have a large corpus of text that needs to be queried.

The pattern: if there's a corpus of text (including audio/video transcripts), AI can absorb it, answer questions, summarise it, or repurpose it. The strongest applications are domain-specific and hard to commoditise.

Avoid rebuilding things that will be open-sourced within months. Focus on internal tools, domain-specific integrations, or niche problems where the obvious solution won't be built for free.

Eight opportunities

1. In-app writing assistant — anywhere customers write, offer AI-generated suggestions. Subject line generator, video script writer, social post repurposer. Can double as a free standalone tool for inbound traffic.
2. Cold outreach personalisation — feed enrichment data into the model and generate personalised draft emails or LinkedIn messages from a template, at scale.
3. Custom knowledge base as a service — ingest a company's internal corpus (docs, transcripts, books, talks) and let staff query it via chat. Surfaces knowledge that's trapped in people's heads or buried in files.
4. AI-augmented customer support — consume internal and external knowledge bases to handle live chat, draft email replies, and serve as an internal product reference for support and sales staff.
5. Transcript-to-prose converter — spoken transcripts don't read like written prose; AI can reformat them into book chapters, essays, or articles while preserving the author's voice.
6. Domain-specific code generation — train the model on your internal data model so it can write code that understands your objects, not just generic JavaScript snippets.
7. Product feedback synthesiser — ingest support emails, feature voting boards, forums, and Slack channels; ask which features to build next and what impact they'd have on your best customers.
8. Sales call analyser — pull transcripts from all sales calls and query them: why are we losing deals, what do top performers do differently, how can we improve demos?

When to use new tech like this

• Don't rewrite your roadmap over a trend — for most companies this is a nice-to-have, not a game-changer.
• Obvious ideas (subject line generators, generic content tools) will be commoditised fast — often open-sourced for free.
• Favour domain-specific or hard problems; they take longer to become commodities.
• A week or two building an internal tool or free utility is a reasonable experiment; months of engineering on something that will be open-sourced is not.
• Cost matters: OpenAI charges per token, which can make some use cases uneconomical.