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# Aissist.io > Canonical website: https://aissist.io > Primary sitemap: https://aissist.io/sitemap.xml > Machine-readable summary: https://aissist.io/llms.txt ## Summary Aissist.io provides an AI Operational Layer for customer support, sales, and workflow automation. The platform is designed for end-to-end execution across systems, with digital agents that can reason, retrieve data, follow SOPs, and complete work with human escalation when needed. ## Preferred URLs - Home: https://aissist.io - Pricing: https://aissist.io/pricing - Consultation intake: https://aissist.io/contact-us/consult - Contact / request demo: https://aissist.io/contact-us/request-demo - Insights hub: https://aissist.io/insights - Industries hub (benchmarks & comparisons): https://aissist.io/industries - Integrations hub: https://aissist.io/integration - Customer stories: https://aissist.io/customer-stories - Technology overview: https://aissist.io/technology - Multi-Agent Platform: https://aissist.io/technology/multi-agent-platform - Reliable AI: https://aissist.io/technology/reliable-ai - Build Strong AI: https://aissist.io/technology/build-strong-ai-with-four-steps - Gateways: https://aissist.io/technology/gateways - AgentMesh: https://aissist.io/product/agentmesh-agentic-ai-workforce - AI Operational Layer: https://aissist.io/product/ai-operational-layer - Pulse: https://aissist.io/product/pulse-deep-insight - Agent Insight (part of Pulse): https://aissist.io/product/agent-insight - Evolve: https://aissist.io/product/evolve-continuous-optimization ## Core Product Pages - AI Operational Layer: https://aissist.io/product/ai-operational-layer - AgentMesh: https://aissist.io/product/agentmesh-agentic-ai-workforce - Pulse: https://aissist.io/product/pulse-deep-insight - Agent Insight (part of Pulse): https://aissist.io/product/agent-insight - Evolve: https://aissist.io/product/evolve-continuous-optimization ## Product Summary - Primary category: agentic AI platform for customer support, sales, and operations - Core concept: AI Operational Layer for orchestration, execution, governance, and continuous optimization - Product family: - AgentMesh: automation engine for end-to-end support and sales execution - Pulse: insight engine for customers, products, agents, processes, and business analysis - Agent Insight: unified evaluation of AI and human agents on business-defined metrics (resolution, CSAT, QA compliance, tone/policy, custom); results become improvement signals that automatically tune the AI - Evolve: action engine for signals, diagnosis, recommendations, and continuous optimization - AgentMesh measured performance (self-reported, all Aissist.io projects through 31 March 2026): 83% average resolution, error rate under 1%, 4.8/5.0 average CSAT, up to $0.60 per resolution - Third-party rating — G2: 4.9/5, 45 reviews, AI Verified, as of 2026-10-07 (https://www.g2.com/products/aissist-io/reviews) - Main capabilities: multi-agent orchestration, workflow automation, cross-system execution, context retention, escalation to humans, deep analysis, continuous optimization - Input types: text, image, document, audio, video - Channels: web chat, email, voice, messaging, social - Typical use cases: customer support automation, SOP automation, lead qualification, service operations, complex troubleshooting ## Technology Pages - https://aissist.io/technology - https://aissist.io/technology/multi-agent-platform - https://aissist.io/technology/reliable-ai - https://aissist.io/technology/build-strong-ai-with-four-steps - https://aissist.io/technology/token-efficiency - https://aissist.io/technology/self-evolve-ai-for-continuous-improvement Notes: Self-Evolve AI — the self-evolving architecture wiring AgentMesh (execute), Pulse (evaluate every conversation, AI and human-handled), and Evolve (experiment/optimize) into a continuous execute → evaluate → optimize cycle. Problem framing: teaching AI still means hand-authoring rules/intents/flows, which assumes a linear system; agentic behavior emerges from instructions, context, tool results, and history, so optimization becomes whack-a-mole, content lags product changes, and the AI stays static during incidents. Today's playbook (authoring + human transcript review) fails on effort, signal quality, latency, and verification. The loop benchmarks against your top human performers on real traffic, runs each change as a measured experiment across resolution/CSAT/sentiment/effort, and optimizes for genuine resolution. Open questions named on the page: reward definition, knowledge vs behavior gap, partial observability, where human guidance fits, experiment design. Small pilot program with several customers; data from Q4 2026. Keywords: self-evolve AI, self-evolving AI, auto learning. - https://aissist.io/technology/gateways - https://aissist.io/insights/multi-agent-architecture ## Key Commercial Pages - Pricing: https://aissist.io/pricing - Pricing reference: https://aissist.io/pricing Notes: 1,000 free tickets monthly, no credit card, no commitment. Growth is pay-per-use with no contract, metered at $0.09 per AI interaction and capped at $0.60 per resolution — whichever is lower. Estimated cost per resolution is $0.2-$0.6 and varies by channel: email, online form and social media ~$0.25 (about 2 AI interactions per session), web chat, WhatsApp and SMS $0.60 at the cap (about 5). Enterprise adds a volume discount, higher SLA, and dedicated AI engineer support. Billing is post-usage, with no installation charges or hidden fees. Pulse and Evolve: Weekly Insight and AI Optimization are included with AgentMesh; Ledger, Agents Insights, Business 360 and Signals are contracted separately. - Partnership: https://aissist.io/partnership - Security: https://aissist.io/security - Consultation intake: https://aissist.io/contact-us/consult - Demo request: https://aissist.io/contact-us/request-demo ## Industry Benchmarks - Industries hub (all benchmarks & comparisons): https://aissist.io/industries - AI Agent Pricing Benchmark 2026 (18 vendors): https://aissist.io/industries/ai-agent-pricing-benchmark-2026 Notes: What 18 AI customer service agents charge, normalized to effective cost per resolved conversation, with every figure tagged [Published] (vendor pricing page), [Reported] (third-party procurement data, primarily Vendr) or [Estimated]. Published per-resolution rates cluster $0.50-$2.00: HubSpot Breeze $0.50/resolution, Aissist.io ~$0.60/resolution (no platform fee, no seats), Yuma ~$0.65, Gorgias $0.90-$1.00 (AI-resolved tickets also billed as helpdesk tickets), Intercom Fin $0.99/outcome + $29-$139/agent seats. Zendesk publishes NO per-resolution rate; the $1.50-$2.00 band widely quoted for it is [Estimated] from third parties (a Zendesk PM's forum remark that list is "definitely below $2", and analyst firm Futurum's "starting at $1.50") - plausible, not confirmed, and it sits on top of seats plus $50/agent Advanced AI. Flat per-ticket add-ons sit on top of a helpdesk you still pay for: My AskAI $0.10/ticket, eesel AI $0.40/ticket, Siena AI $750/mo + $0.90/ticket. Per-conversation and per-session models bill for failures: Salesforce Agentforce $2.00/conversation (~$3.33 effective at 60% resolution, the most expensive here), Kustomer ~$0.60/conversation + $89-$139/user seats, Freshdesk Freddy $0.10-$0.49/session at 23-30% resolution (~$1.60-$2.00 effective). Enterprise quote-only tier starts ~$50K/yr before any conversation: Sierra ~$1.50/resolution with ~$150K ACV + $50K-$200K setup, Decagon ~$50K/yr + ~$0.99/conversation (Vendr median ACV $432,750, range $105K-$923K, read 20 Aug 2026; sample size unpublished), Ada ~$30K/yr entry (Vendr median ~$70K), Forethought ~$0.12/deflection + platform (Vendr median ~$59.5K), Maven AGI no public data. Effective spread across the field: ~$0.13 to $3.33+ per resolved conversation. Five pricing models (per resolution, per conversation, per session, flat per ticket, enterprise custom); the model drives the bill more than the rate. Fine print that decides invoices: assumed resolutions (Fin bills when a customer exits without replying), double billing (Gorgias), session inflation, automatic overages (from 1 January 2026 Zendesk bills above-allowance usage automatically each month on non-standard contracts; overage is the default account setting and can be switched off, which pauses the AI agent at the limit instead, and Zendesk emails the account owner at 80% and 100% of allowance). - AI Agent Reliability Benchmark 2026 (error rate by severity): https://aissist.io/industries/ai-agent-reliability-benchmark-2026 Notes: How AI agent error rate should be measured, and why no vendor in the category publishes one. Ada, Fin and Sierra all publish success metrics and no error metric: Ada defines an automated resolution as relevant + accurate + safe (accurate = "the response was correct and consistent with current policies, knowledge, and connected systems"), a binary pass/fail; Fin reports a 76% average resolution rate across 12,000 customers "counting only genuine positive resolutions"; Sierra's customers page publishes Airtable 80% and Chime 70%+ with no stated methodology. Single-shot accuracy overstates production reliability: on tau-bench (arXiv:2406.12045), the customer-service agent benchmark Sierra open-sourced, state-of-the-art agents succeeded on under 50% of tasks on one attempt and under 25% of retail tasks across all eight attempts at the same task; the public leaderboard's best retail score falls from 69.2% (pass^1) to 46.2% (pass^4). The proposed standard is a four-level severity scale graded on two questions - does it change a business outcome, and how cheaply can it be undone. S0 = changes a business outcome the company cannot take back (out-of-policy refund/discount/contract term, an obligation the business must honor, a disclosure the customer should not see); S1 = business impact the customer or business recovers easily (wrong promo code, URL, ETA, unhonored price quote); S2 = no business impact, internal guidance violated (skipped verification step, wrong handbook branch, untagged conversation) and the leading indicator of future S0/S1; S3 = cosmetic (wording, grammar, tone, formatting, brand name casing). Formula: error rate (severity N) = AI-handled conversations containing at least one error of severity N / all AI-handled conversations - conversations, not messages, because a message-level denominator inflates the volume and flatters the rate. Detection is two-layer: an LLM audit over every transcript plus human QA sampling to keep the auditor honest (Ada validated its own labeling methodology to a 0.06% error margin). Aissist.io contractual targets: all errors S0-S2 combined < 1%, S1 < 0.1%, S0 < 0.01%; S3 is tracked and fed into Evolve but excluded from the SLA. These are SLA thresholds, not observed averages. The rate is held there by four stacked mechanisms described at https://aissist.io/technology/reliable-ai - grounding and prompt guardrails, Booster (run a critical task an odd number of times and take the strongest agreement), Self-inspect before release, and stacked governance. Five questions to ask a vendor: what is your error rate and what is the denominator; how do you grade severity and can you show the S0 definition in writing; what is the S0 rate specifically, not the blended rate; who detects errors, the model that produced them or an independent audit; is any of it in the contract. - Cost Benchmark of AI Customer Service: https://aissist.io/industries/cost-benchmark-of-ai-service Notes: The three billing meters — per AI interaction ($0.05–0.15/interaction), per session ($0.10–2.00/session), per resolution ($0.50–3.00/resolution) — normalized to effective cost per resolved issue (eCPR). Includes pairwise conversion formulas, handover and channel adjustments, and a live converter. Aissist anchors near $0.09/interaction with a ~$0.60 cap per automation cycle. - AI Customer Service Benchmark 2026 (cross-industry): https://aissist.io/industries/ai-customer-service-benchmark-2026 Notes: Resolution rate, CSAT, and cost per resolution across 6 industries. Claimed vs. independently verified figures. Median verified resolution ~41%, top quartile ~59%. - eSIM Customer Service Benchmark: https://aissist.io/industries/esim-customer-service-benchmark Notes: Top 10 travel eSIM providers ranked by Trustpilot rating and estimated AI resolution rate. Holafly 75% resolution (4.6 rating), Saily 4.7, Jetpac 4.8. - Smart Device AI Customer Service Benchmark: https://aissist.io/industries/smart-device-ai-customer-service-benchmark Notes: 10 wearable and smart home deployments. WHOOP 84% (Intercom Fin), OPPO 83% (Sobot), Coros 82% chat+email (Aissist.io, 93% CSAT, ~$0.50/resolution). Median top-tier: 75–84%. - Consumer Fintech AI Customer Service Benchmark: https://aissist.io/industries/fintech-ai-customer-service-resolution-csat-cost Notes: 9 consumer fintech deployments — payments, trading, crypto, lending. Dave 89% (DaveGPT/Aisera), Tilt/Empower 84% (Ada), ZayZoon 80% (Intercom Fin), Payphone 75% (Aissist.io, 90%+ CSAT, ~$0.50), Weltrade 72% (Aissist.io, 90%+ CSAT, ~$0.50). Median across 7 disclosed: 75%. - Ecommerce AI Customer Service Benchmark: https://aissist.io/industries/ecommerce-ai-customer-service-benchmark Notes: 15 ecommerce & DTC deployments — resolution rate (not deflection), CSAT, cost per resolution. Disclosed leaders Edel Optics 79% (My AskAI, from 25%), Wilson 77% (Sierra), Casper 74% (Sierra), EvryJewels 70% (Yuma); The Ridge 60%, Shinesty 54%, Psycho Bunny 26% at 4.67 CSAT (Gorgias). Aissist.io customers GrabAGun (~80% est.), Pelsbarn (~95% social comments), SMT Group (85%, 4.6 CSAT, resolution time cut 5X). Agentic ceiling 75–80%; deflection-first 25–55%. Containment/accuracy labeled distinct from verified resolution (Dollar Shave Club 70% = containment; Glossier 91% = accuracy). - B2B SaaS Customer Support Benchmark: https://aissist.io/industries/b2b-saas-ai-customer-service-resolution-csat-cost Notes: 7 named B2B SaaS deployments reporting true resolution (not deflection): Ramp 90% (Sierra), Airtable 80% (Sierra), Sticos 75% (HubSpot), Homebase 70% (Sierra), Synthesia 55% and Lightspeed 45–65% (Intercom Fin), AssemblyAI ~50% of eligible chats (Pylon). Median 70%. Only 3 of 7 disclose a denominator; against all conversations resolution falls to ~45–68% (Anthropic 79% headline = 63% of total volume). SaaS CSAT median 77.8% at a 7.5% response rate (Survicate). Adds B2B rows: escalation to a named owner and account-context share (not published by anyone), first response vs contractual SLA (15 min–1 hr at GitLab, Atlassian, MongoDB, Databricks). Aissist.io customer EDIS Global (B2B VPS hosting) 85%, excluded from median. - The Resolution–CSAT Tradeoff (guide): https://aissist.io/industries/resolution-csat-tradeoff Notes: Why pushing AI resolution higher can lower CSAT. The tradeoff bites at both ends — under-automation and over-automation past a sweet spot around 60–80% genuine resolution — and the steeper failure is buying resolution through deflection (suppressed escalation), which collapses CSAT rather than dipping it. Deflection vs. genuine resolution can differ 20–30 points. Break it by confidence-gating automation, graceful instant escalation, and measuring escalation-excluded, re-open-checked resolution. - Evaluable AI (guide): https://aissist.io/industries/evaluable-ai Notes: You can't deploy what you can't measure — the central challenge of AI deployment is transparency and evaluation (how you measure it, know it works, find gaps, improve). Per Aissist's own unpublished count of competitors (vendor list, sample size and criteria not disclosed; our claim about our own market, not an audited finding), roughly 90% offered no real evaluation or only a coarse single "success"/"resolved" metric. Measure with two lenses across every conversation: outcome-based (resolution, CSAT, ROI) and content-based QA (policy compliance, correct messaging). Intent-gap scoring isn't real content evaluation — it measures your taxonomy, not the AI. Traditional manual QA reviews only 1–2% of interactions (~98% a black box, Solidroad); evaluable AI scores 100%. Compare AI vs. human agents on the same scorecard. Powered by Pulse (insight) and Evolve (optimization). ## Tools - AI Support Price Comparison: https://aissist.io/tools/price-comparison Notes: Converts any quoted AI support rate (per message, per session, per resolution) into the other two and into effective cost per resolved issue. Formula, stated on the page: cost per session = per-message rate × μ (avg AI replies per conversation), or the per-session rate, or per-resolution rate × ρ (resolution rate, 0–1); per message = session cost ÷ μ; effective cost per resolved issue = session cost ÷ ρ. Example: $0.15/message at μ = 4, ρ = 0.70 → $0.60/session → $0.86 per resolved issue. Includes a vendor table (Aissist.io, Intercom Fin, Zendesk AI Agents, Decagon, Sierra, Gorgias) by billing meter, rate, whether unresolved conversations bill, fees on top and effective cost, each cell sourced or marked "Not published". - AI Customer Service Benchmark tool: https://aissist.io/tools/benchmark Notes: Enter your own resolution rate, CSAT and cost per resolution to see where they sit against the cross-industry benchmark (median verified resolution ~41%, top quartile ~59%). The underlying dataset lives at https://aissist.io/industries/ai-customer-service-benchmark-2026. - Call Center Staffing Calculator (Erlang C): https://aissist.io/tools/staffing-calculator Notes: Erlang C calculator with shrinkage and AI inputs. Workload (Erlangs) = contacts per hour × AHT seconds ÷ 3,600; agents on contacts = smallest N meeting the service level (SL = 1 − P(wait) × e^(−(N−A)·T/AHT)) under a max-occupancy cap; agents scheduled = agents ÷ (1 − shrinkage); the AI run removes the truly resolved share of contacts and raises residual AHT, then repeats. Example: 120 contacts/hr, 360s AHT, 80/20, 30% shrinkage → 16 on contacts, 23 scheduled; 28% true resolution with residual AHT +15% → 20 scheduled (3 removed, 13%). Method and sources: https://aissist.io/insights/call-center-staffing ## Comparisons - Aissist vs. Intercom Fin: https://aissist.io/industries/aissist-vs-intercom-fin Notes: Stack-agnostic head-to-head across capability, multi-agent architecture (AgentMesh), cost (up to $0.60/resolution vs $0.99/outcome + seats), independence, billing predictability, languages (65+ vs 45+), and performance. Context: Intercom renamed to Fin; Salesforce agreed to acquire Fin (~$3.6B). - Aissist vs. Zendesk Forethought: https://aissist.io/industries/aissist-vs-zendesk-forethought Notes: Stack-agnostic head-to-head across capability, coherent multi-agent architecture (AgentMesh) vs assembled stack, cost (up to $0.60/resolution vs Zendesk's unpublished rate, third-party estimates ~$1.50–$2.00, + seats), independence, billing predictability, languages, and performance. Context: Zendesk acquired Forethought (announced March 11, 2026). - Aissist vs. Decagon: https://aissist.io/industries/aissist-vs-decagon Notes: Mid-market vs. enterprise head-to-head across capability, multi-agent orchestration (AgentMesh) vs Agent Operating Procedures, cost (up to $0.60/resolution published vs custom enterprise pricing), price transparency, time to deploy (~10 min self-serve vs guided rollout), and performance. Context: Decagon ~$4.5B valuation, $250M Series D (January 2026), enterprise pure-play. - Aissist vs. Sierra: https://aissist.io/industries/aissist-vs-sierra Notes: SMB-ready vs. enterprise-scale head-to-head across capability, lean multi-agent (AgentMesh) vs 15+ model "constellation" on Agent OS, cost (up to $0.60/resolution published vs ~$150K/yr floor + setup + $1–$2.50/res), price transparency, time to deploy (~10 min self-serve vs 3–7 months), and performance (both bill on resolution, not deflection). Co

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aissist.io

30 technologies on aissist.io

aissist.io

30 technologies on aissist.io

JavaScript Frameworks & Libraries2

Frontend Build Tools & CSS3

Web Servers, Languages & Backend Frameworks1

CDN & Hosting2

Analytics & Tag Management2

Advertising & Affiliate Tracking3

Transactional Email1

Fonts & Icon Fonts2

Live Chat, Helpdesk & AI Support1

Reviews, Ratings & Social Proof1

Security, WAF & Bot Protection1

Translation & Localization1

Video, Audio & Media Delivery1

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