Key details

  1. October 2026 snapshot: 18 vendors, 265 stores, 12,327 captured and 12,322 judged conversations.
  2. Rolling 120-day window; rankings and counts change.
  3. Shopping composite 40% automation, 35% quality, 25% speed; support 50/40/10.
  4. At least 15 judged conversations per job to rank.
  5. Gorgias reports 68% support automation and 75/100 quality; #1 overall and support, #4 shopping.
  6. Benchmark is run by a market participant; third-party reproduction not established.

What builders should take away

  1. Separate shopping from post-sale support evaluations.
  2. Measure accuracy, actionability and latency as well as containment.
  3. Test with your own policies, authentication and product catalog.
  4. Read sample sizes, weights and evaluator conflicts before buying.
  5. Treat public scoring rubrics as a test template, not certification.

What changed

Gorgias' October 2026 rolling benchmark currently shows 12,327 captured conversations, 12,322 blind LLM-judged conversations, 18 vendors and 265 live stores. Its public 120-day ranking uses cold sessions and two job-specific composites: shopping automation/quality/speed weighted 40/35/25 and support weighted 50/40/10. Gorgias ranks its own product first overall and in support but fourth in shopping, noting slower shopping answers than competitors. The live numbers change over time.

Why it matters

Operators selecting ecommerce AI support should not treat automation or ticket containment as proof of good answers. A system can avoid handoffs while misunderstanding policy, fail to take action, or stall shoppers. The public rubric offers a practical test design, but the evaluator is also a competitor; procurement should include independent tests on the buyer's own catalog and rules.

The benchmark tests live stores, not demo sandboxes

At inspection the live page reported 265 stores, 18 vendors, 12,327 conversations captured and 12,322 judged. Cold sessions use store-specific catalog questions. An evaluator never requests a human; handoffs must originate from the agent. At least 15 judged conversations per job are required for ranking. This is a moving snapshot, not a fixed research sample.

Support and shopping are different jobs

Support scores weight automation 50%, answer quality 40% and speed 10%. Shopping weights them 40%, 35% and 25%. Speed earns 100 at three seconds or less and zero at 22 seconds or more. Rankings therefore depend on what the buyer needs an agent to do.

Containment is not the same as useful resolution

Gorgias reports its support agent at 68% automation and 75/100 support quality in the inspected snapshot, first overall in support but second for quality behind Sierra. It reports roughly 18-second shopping answers, slower than some rivals. These are operator-produced metrics, not independently reproduced measurements.

Configuration can matter more than the model

The benchmark describes authentication walls, repeated order-number requests and clarification loops as frequent causes of failure. It also reports large differences between stores using the same vendor. Buyers should test identity, returns, product information and escalation rules with their own data rather than rely on a vendor leaderboard.

Treat the evaluator's conflict explicitly

Gorgias sells an agent in the comparison and claims the top overall rank. Its rubric is public, but independent reproduction of runner, sample and scoring has not been established. SaaStr analysed an earlier smaller snapshot while disclosing an investment relationship with Gorgias. That is useful commentary, not independent validation.

What to watch next

  • Independent reproduction and sample audit.
  • Rolling ranking changes as store coverage grows.
  • Improved guardrails that reduce handoff loops without hurting accuracy.
  • Measured recontact rates, support costs and conversion outcomes.

Still unclear

  • Gorgias evaluates its own competing product.
  • Blind LLM scoring and store selection have not been independently audited.
  • Counts and rankings are live and not stable historical figures.
  • Store-specific results may not generalise to other business models.
  • Earlier specialist reports quote different, older sample windows.

Sources

Direct reading behind this dossier.

3 sources

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