Your category page stopped being where the shortlist forms.
B2B buyers now open a chat window first and arrive at your site already holding a shortlist of three vendors. If the model didn't name you, no amount of demo-page optimisation recovers it. Meanwhile your own GTM motion is the most automatable in any sector — and the traffic decline hitting B2B sites hardest makes both problems the same problem.
34%
Average year-over-year organic traffic decline across affected B2B sites
B2B organic traffic studies, 2024–2025
- The typical length of a model-generated shortlist
3 vendors
The typical length of a model-generated shortlist
- Organic CTR lift for brands cited in AI Overviews
+35%
Organic CTR lift for brands cited in AI Overviews
- What enterprise buyers now ask about your product AI
Evals
What enterprise buyers now ask about your product AI
- Third-party sources models lean on for software
G2 & Reddit
Third-party sources models lean on for software
The pressure this sector is under
Not a market-size slide. The three things we hear in the first ten minutes of nearly every call in this industry.
- 01
Discovery happens before you see it
Comparison and alternatives research now runs inside an assistant. Your analytics show a direct visit from an already-convinced buyer, or nothing at all — which makes the losses invisible in exactly the dashboard you'd check.
- 02
Content marketing's return has collapsed
The top-of-funnel post that used to earn a click now feeds an answer that never links out. Volume content is the worst-performing spend in most B2B budgets, and the teams producing it usually know.
- 03
Your product AI is now a procurement question
Ship an AI feature and enterprise buyers ask about evals, data retention, sub-processors and model providers. Firms without good answers lose deals to firms whose answers are merely adequate.
Where SaaS teams get the most out of us
The visibility work and the automation work reinforce each other — the same entity and content foundation feeds both the models and the pipeline.
Shortlist visibility
Getting named for 'best X for Y', 'alternatives to [competitor]' and category questions across every engine, then tracked weekly so displacement shows up as a dated delta rather than a bad quarter.
Moves: Citation rate on category and comparison queries
Comparison & alternatives pages
Honest, specific, extraction-ready comparison content — including where you genuinely lose. Models reward pages that answer the constrained question rather than pages that only sell.
Moves: Presence in generated comparisons
Third-party source strategy
Review platforms, ranked listicles and community threads are what models actually cite for software. We work them deliberately and transparently, and track which sources feed which answers.
Moves: Source coverage behind cited answers
Pipeline & GTM automation
Inbound qualification against ICP, enrichment, routing, meeting booking and CRM hygiene — the SDR work that scales linearly with headcount until it doesn't have to.
Moves: Speed to lead and SDR hours per meeting
Support deflection & docs
Retrieval-grounded answers over your documentation and ticket history with citations, plus the gap analysis showing which documentation is missing based on what customers keep asking.
Moves: Ticket deflection and time to resolution
Product-embedded AI
AI features inside your product built with eval suites, guardrails and observability from day one — because the second version of an AI feature is much harder to ship if the first has no tests.
Moves: Feature reliability and release confidence
Enterprise readiness for your AI
The assurance pack security reviews demand: model cards, retention and sub-processor documentation, eval evidence, and pre-filled answers to the AI questionnaires now standard in procurement.
Moves: Security review cycle time and win rate
How we work with a product team
Every sector has rules that decide what can be built and what can only be demoed. We'd rather state ours before the scoping call than discover them in a security review.
We ship as pull requests
Work lands in your repositories under your review process, in your conventions. Nothing is delivered as a recommendation deck for your engineers to reimplement.
Evals before features
Any product AI gets a golden dataset and a threshold before it ships, because retrofitting evals onto a shipped feature costs several times more than writing them first.
Attribution is honest about zero-click
We report citation rate and AI referral traffic separately, and we say plainly that the latter undercounts. Pretending last-touch captures this is how GEO budgets get cut for the wrong reason.
Comparison content stays truthful
We publish where competitors are genuinely better. Models and buyers both punish comparison pages that only flatter the author, and the credibility is what earns the citation.
What your buyers are asking a model right now
A sample of the prompts we baseline for this sector on day one. If a competitor is named in the answer and you aren't, that gap is measurable before you hire anyone.
- “best alternatives to [competitor]”
- “how to get cited by ChatGPT as a SaaS vendor”
- “AI SDR automation for B2B”
- “evals for production LLM features”
- “AI security questionnaire responses for SaaS”
The services this sector usually buys, in order
Sequencing matters more than scope. These are ordered the way we'd actually run them, and most clients stop after the first two.
B2B SaaS questions, answered
How do we know we're losing deals inside AI answers?
Should we still be publishing blog content?
We're pre-product-market-fit. Is this too early?
Can you work alongside our in-house engineers?
See the shortlist your buyers are being given.
Send your category and two competitors. We'll run the comparison queries through every major engine before the call and show you the answers your buyers are reading.
30-minute strategy call
With an engineer, not a closer
Typical reply time: under 4 business hours.
hello@searchsynth.ai