Casily vs. Writing Case Studies Manually: A Cost and Time Comparison
Most teams don't compare case study tools against each other — they compare any tool against the status quo: writing case studies by hand, or more often, not writing them at all. Here's that comparison, honestly.
The manual process, costed
A typical manual case study cycle:
| Stage | Typical time |
|---|---|
| Get customer to agree to an interview | 1–2 weeks of chasing |
| Schedule + run the interview | 1 week |
| Transcribe and draft | 1–2 weeks |
| Internal review and edits | 3–5 days |
| Customer approval | 1–3 weeks |
| Design and publish | 3–5 days |
| Total | 4–8 weeks |
Cost per case study: roughly £500–£2,000 if you hire a freelance writer, or 8–15 hours of internal time if you write it yourself — usually a founder's or marketer's hours, which is why it keeps getting deprioritised.
The hidden cost is the one that hurts: because each story takes weeks, most teams have 1–3 case studies total, they're outdated, and none match the specific prospect sales is talking to today. The case study you don't have doesn't lose deals loudly — it loses them silently.
The transcript-first process with Casily
Casily replaces the interview-and-draft cycle with extraction from calls you've already had:
- Ingest: forward call transcripts by email, send them via webhook from your recording tool, or upload files. Sales calls, onboarding sessions, QBRs — individually or combined.
- Extract: a two-pass process first pulls and verifies the raw components (challenge, solution, results, quotes tied to source lines), then drafts. This is what prevents the invented-quote problem generic AI writers have.
- Draft: you get a structured case study in a consistent format, ready to lightly edit and send to the customer for approval.
- Tag and match: every approved story becomes a tagged proof asset (industry, use case, result type). When sales needs proof for a specific prospect, AI Match surfaces the most relevant case studies — so proof gets used, not just published.
Time per case study: under an hour of your time, mostly review. Customer approval is faster too, because you send a finished draft instead of requesting an interview.
Side by side
| Manual | Casily | |
|---|---|---|
| Time to first draft | 2–4 weeks | Same day |
| Your hands-on time | 8–15 hrs | ~1 hr |
| Cost per story | £500–2,000 or internal hours | Free (early access) |
| Customer's time required | 45–60 min interview + review | Review only |
| Source material | New interview | Existing call transcripts |
| Quote accuracy | High (human) | High (traceable to transcript) |
| Consistent structure across stories | Depends on writer | Built in |
| Matching proof to prospects | Manual/tribal knowledge | AI Match on tagged library |
| Realistic library size after 3 months | 1–3 stories | 8–15 stories |
When manual is still the right choice
An honest comparison includes this part:
- Flagship enterprise stories. If you're producing one hero case study for a marquee logo with professional design, video, and PR coordination, hire a specialist writer. Use the tool for the other 90% of your proof library.
- No recorded calls. If you have no transcripts, recordings, or written customer material at all, you'll need to run interviews first — though one recorded 30-minute call is enough source material to start.
- Heavily regulated narratives. Where every sentence needs legal sign-off regardless of source, the drafting speed matters less than the approval pipeline.
For everything else — the working proof library that sales actually pulls from — the transcript-first process wins on speed, cost, and volume.
Frequently asked questions
Does AI-generated mean generic?
Generic output comes from generic input. Because Casily drafts from your customer's actual words in actual calls, the specificity is the customer's, not the model's. You edit for voice; you don't write from scratch.
What does Casily cost?
It's currently free for early-access users and design partners. Get started at casilyai.com.
We already have a writer — is this still useful?
Yes, differently: use it to hand your writer a structured, verified extraction instead of a raw transcript. Writers are faster and better when the mining is done.