Win the agent's preliminary recommendation.
Once you're on the shortlist, the agent evaluates. It reads your docs, your pricing page, your changelog. It does not read your homepage video. Lowest token-to-value wins.
The problem
Your homepage was written for humans. The agent is reading your docs.
Buyer-agents skip marketing pages. They go straight to documentation, API references, pricing, and comparison content — because that's where the truth lives. If your docs are gated, fragmented, or written for a human reading top-to-bottom, the agent gives up and recommends the competitor whose answer was one fetch away.
Our thesis
We rebuild your docs, pricing, and comparison surfaces so an agent can extract a complete vendor evaluation in the fewest tokens possible — and arrive at your name as the preliminary recommendation.
median token-to-value reduction (audit clients)
buyer-task surfaces re-engineered
canonical capability matrix
What we ship
Concrete deliverables, not slides.
Docs-as-GTM rewrite
Treat documentation as the primary GTM surface, not an afterthought. Restructured information architecture, copy-paste-perfect code samples, and explicit 'when to use' framing on every page.
Token-to-value audit
We measure exactly how many tokens an agent needs to consume before it can confidently recommend you on each top buyer task. Then we cut that number in half.
Agent-readable pricing page
Structured pricing data — not screenshots of pricing cards. Clear units, included quotas, overages, and a machine-parseable plan-comparison matrix.
Comparison & alternatives pages
First-party vs.-competitor pages that an agent can lift directly into a recommendation. Honest, sourced, schema-tagged. Owns the 'X vs Y' query class.
Capability index & changelog
A single canonical capability matrix and a structured changelog feed so agents always see the current state of your product, not last year's snapshot.
How it runs
From signal to compounding.
- Week 1–2
01. Token-to-value baseline
ALG360 Scan measures evaluation cost across your top 20 buyer tasks. Benchmark against 3 competitors.
- Week 3–6
02. Docs IA + rewrite
Re-architect docs around buyer tasks. Rewrite the top 30 pages for agent extraction. Ship structured pricing.
- Week 7–10
03. Comparison surface
Build the comparison page set, deploy schema, and seed retrieval indexes. Re-baseline Scan.
- Ongoing
04. Quarterly recut
Capability matrix updated each release. Comparison pages refreshed when competitors ship.
Inside the pillar
The shortlist version.
- Docs-as-GTM rewrite — agents read documentation, not pitch decks
- Token-to-value audit on every conversion path
- Agent-readable pricing, comparison & feature pages
Outcome
Lowest token-to-value in your competitive set.
Stack
- ALG360 Scan
- Mintlify / Fern / ReadMe
- Algolia / Inkeep
- Schema.org
- Custom token-cost profiler
"We rebuilt our docs around the buyer-agent and watched agent-attributed trials grow 4× in a quarter. The docs are now the funnel."
FAQ
Pillar questions, answered.
- What is token-to-value?
- The number of tokens an agent must consume from your site to give a confident, complete recommendation on a buyer task. Lower is better — it means cheaper, faster, more accurate agent retrieval, which compounds into more recommendations.
- Do we have to rewrite all our docs?
- No. We start with the top 20–30 buyer-task pages — that's typically 80% of the agent-evaluation traffic. The rest gets a lighter schema + structure pass.
- What about gated content?
- Anything gated is invisible to the agent. We'll help you decide what to ungate and what to keep behind a form — usually the answer is 'ungate the evaluation content, gate the consultative content.'
See where you stand on Evaluable.
The ALG Readiness Audit benchmarks you on every pillar — including this one — and ships a 90-day execution roadmap.