Win the agent's preliminary recommendation.
Once you're on the shortlist, the agent evaluates. It reads your docs, your pricing page, your changelog and your trust signals. 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 — 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
ALG360 turns your docs, pricing, comparison and trust surfaces into a single agent-readable evidence library — 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
buyer-task surfaces re-engineered
canonical evidence library
What's inside
Capabilities that ship in the product.
Evidence library
One canonical source of truth for capabilities, customers, integrations, security posture and pricing — structured for agent extraction and synced into every surface an agent might touch.
Token-to-value scoring
Continuous measurement of exactly how many tokens an agent must consume before it can confidently recommend you on each top buyer task — with the specific edits that cut that number in half.
Agent-readable pricing & procurement metadata
Structured pricing — not screenshots. Clear units, included quotas, overages, and a machine-parseable plan-comparison matrix that includes the procurement metadata enterprise agents need.
Trust signals an agent can verify
Customer logos, case studies, security certifications and changelog feeds emitted as structured data the agent can quote and link, not buried in PDFs or behind a form.
How it runs
From signal to compounding.
- Day 1
01. Token-to-value baseline
ALG360 measures evaluation cost across your top 20 buyer tasks. Benchmark against three competitors.
- Week 1–3
02. Evidence library build
Centralize capability, pricing, trust and comparison content into the canonical source of truth. Wire it to every surface.
- Week 3–6
03. Comparison & pricing surfaces
Ship the comparison page set, deploy structured pricing, and emit trust signals as schema. Re-score.
- Ongoing
04. Quarterly recut
Capability matrix updated each release. Comparison pages refreshed when competitors ship.
Inside the pillar
The shortlist version.
- Evidence library of agent-readable docs, pricing & comparisons
- Trust signals, proof points and pricing metadata in one source of truth
- Continuous token-to-value scoring as competitors ship
Outcome
Lowest token-to-value in your competitive set.
Stack
- Evidence Library
- Token-to-Value Scoring
- Pricing & procurement schema
- Trust signal emitters
"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. ALG360 surfaces 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 Evaluate.
The Agent Readiness Scan benchmarks you on every pillar — including this one — and ships the exact fixes that move the needle.