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Intercom
Customer communications platform providing messaging, support, help center, and AI-powered customer service tools.
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Learn how AI can unlock productivity, according to AI founder Andrew Louder - The Intercom Blog
AI founder Andrew Louder explains that the days of AI being unattainable are over, with accessible off-the-shelf tools available. He illustrates this with a law firm client that reduced e-discovery work from nearly 1,000 hours per case to about 40-50 hours using AI. Louder advises that the build-versus-buy decision depends on awareness of existing solutions and requirements. He emphasizes that change management, including awareness, communication, and training, is crucial for successful AI adoption. According to Louder, businesses should conduct AI readiness assessments, establish data management plans, and build robust knowledge bases to ensure AI effectiveness.
Discover the secret sauce of innovative companies, according to Stanford professor Behnam Tabrizi - The Intercom Blog
Stanford professor Behnam Tabrizi discusses his book ‘Going on Offense,’ which analyzes 26 companies to uncover the culture of perpetual innovation, based on a survey of over 6,000 executives, academics, and industry experts. He explains that Microsoft’s renaissance under Satya Nadella came from elevating engineers, prioritizing cloud computing, and making early bets on AI, creating a startup mindset. Tesla’s unique culture aligns with a holistic approach to cultivating change, inviting active participation across the company and a fierce dedication to overcoming challenges. Legacy companies often fail because they make cautious, incremental bets rather than bold moves like Nvidia’s 2018 bet on AI, which made it a trillion-dollar semiconductor leader. Tabrizi argues AI will not eliminate jobs but will displace those who fail to leverage it effectively, urging organizations to go on offense by deploying AI for growth and productivity.
The father of customer journey mapping, Chip Bell, talks driving innovation through customer partnership - The Intercom Blog
Chip Bell, author and co-developer of customer journey mapping, advocates for co-creation partnerships where customers become partners in innovation. By shifting from a transactional relationship to a partnership, companies can unlock customer creativity and unexpected breakthrough solutions. Bell recommends approaching inquiries with genuine curiosity, building trust through truth-telling and promise-keeping, and embracing daredevil learning that treats risk-taking as a learning venture. He contrasts AI’s limitations, stating it cannot replicate the human empathy and emotional depth that underpin genuine customer relationships. The key is to make customers feel welcomed to contribute, transforming them into co-creators of memorable experiences.
Announcing ‘The 2026 AI Sentiment Report’: How end users feel about AI Agents
The 2026 AI Sentiment Report examines how end users feel about AI Agents in customer service and other uses such as shopping. Overall sentiment improved: 49% reported positive experiences, up nine percentage points from 2025, and that figure rose to 74% after respondents watched an Agent resolve a real query. Trust remains conditional, with 54% willing to trust AI with simple or routine issues but not complex ones involving discretion, privacy, continuity, accountability, or judgment. The research identifies easy escalation to a human and upfront disclosure that users are interacting with AI as the two leading ways to increase comfort, while trust also varies by channel, with chat strongest and social weakest. It concludes that businesses can treat trust as a design problem, including reassuring customers that shopping assistants serve their interests rather than merely the company’s financial goals.
Announcing Evals and Releases: Evaluate Fin before, during, and after you go live
Fin’s new Evals and Releases provide an evaluation and rollout system for testing changes before launch, controlling deployment, and assessing live conversations, alongside Monitors. Evals group multi-turn Simulations built from real conversations around a theme, then score them pass/fail using deterministic checks and an LLM judge against criteria such as required responses, Procedures, or data connector calls. Releases let teams edit content, Procedures, and Guidance away from live Fin, run Evals on proposed changes, collaborate, and publish to everyone, ramp traffic, A/B test, or roll back. Monitors evaluates every live conversation and can turn failures into new Simulations for subsequent testing. The combined workflow addresses probabilistic behavior, regression risk, and drift as support content and policies change, while Operator can draft changes, tests, and Releases for review and approval.
The 9 best tools for your early-stage startup tech stack in 2026
Early-stage startups face a crowded software market and limited budgets, making technology selection a question of leverage rather than completeness. The guide proposes three selection principles: choose tools that scale with usage, integrate with the existing stack, and solve a clearly defined problem. It then recommends Stripe for payments, Slack for collaboration, Granola for meeting notes, Fin and Intercom for customer service, Linear for planning, Datadog for observability, MongoDB for data, Rippling for workforce administration, and Apollo for outbound sales and CRM. The examples emphasize operational coverage, from Stripe’s subscriptions and MongoDB’s flexible schema to Fin’s stated ability to resolve up to 76% of support conversations. The Fin Startup Pack is presented as a way to reduce adoption costs through partner credits and discounts, including more than $500,000 in credits and specified offers for each recommended tool.
Doing the right thing when things go wrong
Fin’s incident-management process is designed to reduce customer impact quickly when deployments, alarms, or customer reports indicate a disruption. It combines customer and product signals, including heartbeat metrics for workflows such as Fin’s response generation, with engineer monitoring of rollouts; a rollback can land in a little under two minutes. Responses run through incident.io and Slack, with defined engineering lead, incident commander, business lead, and resolution owner roles, plus the out-of-hours STRIKE responder group. The lifecycle covers triage, investigation, mitigation, and learning, with a stated preference for rolling back to a known state before fixing forward and with explicit alternatives when rollback is unavailable. After recovery, bounded follow-ups and incident reviews aim to improve recurrence prevention, blast-radius reduction, detection, and response, while early escalation and clear communication make the process repeatable rather than dependent on heroics.
How to measure the customer experience as AI scales
CSAT captures fewer than 10% of conversations, and its responses skew toward delighted or furious customers. Most customers say nothing, leaving teams to assess customer experience from an unrepresentative sample that compresses product, policy, and service issues into one score. As AI handles more conversations end-to-end, the post argues for scoring every AI and human interaction on service quality, resolution, customer effort, and related attributes rather than relying on survey responses alone. Fin’s CX Score assigns each interaction a 1–5 score and surfaces reasons behind it, providing roughly five times more coverage than CSAT for most teams. Fin sets targets from CX Score attributes, operational correlations, historical performance, modelling, and team ambition, then uses recurring patterns to route fixes to support, product, or workflow owners.
The new product introduction process: How to make sure your Agent is ready every time you ship
Frequent product releases can leave an AI Agent’s knowledge base outdated, causing it to hand off launch-related questions to humans when support volume peaks. The NPI process described here makes Agent readiness part of shipping, with support involved early in product, engineering, and go-to-market planning. Before launch, teams prepare clear customer-language content with full context, review and retire affected or duplicate material, then test likely questions, edge cases, and answer quality in the customer environment. At Fin, release details, walkthrough notes, demos, and GTM briefs form an input package for Operator, which proposes content updates, drafts, FAQ tests, and fixes. Post-launch Monitors and Scorecards track resolution rate and CX Score for two or four weeks, depending on launch complexity, to guide improvements.
Conversation design: How to make your AI Agent communicate like your team
AI agents need deliberate conversation design to communicate effectively with customers, covering tone, response structure, handoffs, interaction flow, and response quality. The post argues that without an explicit owner, an agent may over-explain, sound flat during frustration, or escalate too late, undermining trust and prompting customers to seek human help. It recommends defining a concise voice reference, adapting register to the situation, matching detail to the question, supporting customer-led topic changes, designing informative handoffs and follow-ups, and keeping behavioral instructions separate from knowledge-base content. Teams can begin without a dedicated hire by naming an owner, selecting a poorly performing conversation type, auditing instructions, and fixing the worst handoff first. At Fin, an A/B test raised CSAT from 72.8% to 78.4%.
Salesforce signs definitive agreement to acquire Fin
Salesforce has signed a definitive agreement to acquire Fin for approximately $3.6 billion, with closing expected in the fourth quarter of Salesforce’s fiscal year 2027. Fin says it began as Intercom 15 years ago and, nearly four years ago, shifted toward modern LLMs to create and define the Customer Agents category. The companies plan to join forces, with Salesforce’s resources intended to help bring Fin to more consumers, while Fin says little will practically change for customers: the author will remain CEO and Des Traynor will continue running R&D. The announcement also points to recent work including the Apex model and the Operator internal agent, and describes the deal as a pivotal moment for Fin’s employees, founders, and leadership team.
How to make the case for giving your AI Agent system access
AI Agents can answer support questions without backend access, but they cannot complete changes such as payment-plan updates, claims, or account edits. Connecting an Agent to CRM, billing, order-management, or other systems moves support from explanation to end-to-end resolution, and the source frames that shift as the basis for an engineering request. Data from a 2026 Customer Service Transformation Report shows improved metrics among 87% of teams with mature AI deployment, versus 62% overall, while only 10% report reaching that stage; an internal comparison found Procedures with system access produced the largest gains in workflows requiring judgment, branching, live data, or handoffs. The recommended case starts with one high-volume, repeatable workflow, maps its data and actions, and defines a narrow pilot. Teams can then progress from no integration to read-only access and finally write actions, using measured results to justify further work.
Extending Fin as the most open Agent platform
Fin is being made available as a Service Agent on top of HubSpot and Freshworks, allowing HubSpot and Freshdesk customers to deploy it without migrating from their helpdesk. The announcement says customers can get it integrated and live in less than an hour across voice, email, chat, social, and other channels, with a stated 76% average resolution rate. Fin is described as handling complex queries that require reading and writing to third-party systems, while remaining configurable to each business’s policies; its openness includes APIs, MCPs, a CLI, access to the Apex trained model, and public product, help-center, and developer documentation. The broader argument is that open Agent platforms let customers experiment, replace components, and avoid vendor lock-in, with competition determined by product quality.
Playing a different game
Fin Voice 2 is presented as a customer-service voice agent designed to address the latency, complexity, and reliability demands of phone support. Built on Apex Flash, described as a model trained on millions of customer experience interactions and tuned for low-latency channels, it separates real-time speech processing from the answer-generation layer rather than routing speech through a general text workflow. The product is claimed to deliver faster, more natural conversations, higher resolution rates, and higher customer satisfaction, though the post does not provide measurements. It can reportedly handle identity verification, refunds, appointments, emotional-state changes, confirmations, external-system actions, proactive follow-up calls, and contextual handoffs to human agents. Fin Voice 2 also adds configuration and management features, including call-quality and behavior insights, topic analysis, one-click recommendations, and self-service controls.
The ultimate guide to knowledge management for your Service Agent
AI-driven customer service depends on knowledge management because a Service Agent needs accurate product, feature, policy, and troubleshooting information to resolve queries end to end. The guide divides the work into two phases: building a knowledge base, and maintaining, optimizing, and scaling existing content. It recommends using support conversations and tools such as Fin Recommendations to identify gaps, prioritize FAQs, onboarding guides, troubleshooting material, and other resources, then centralize content and control what the Agent can use. Ongoing practices include scheduled reviews, a request log, launch-planning updates, consistent terminology, audience targeting, and contact details for escalation. The stated conclusion is that knowledge management should be a continuous core service function, with improvements producing more resolutions, better data, and further opportunities to refine support.
Speed-to-lead is a solved problem
The post argues that “speed-to-lead” is no longer the central sales constraint because AI Agents can start conversations with website prospects immediately, at any hour and in any language. It describes how shift scheduling, routing delays, CRM batch processing, and unavailable SDRs created response gaps, with Fin’s former SLA targets set at one hour for best-fit leads and forty-eight hours for others. An Agent can combine immediate engagement with qualification, discovery, and an initial demo moment in one continuous conversation, filtering low-fit leads and handing teams richer context. This shifts SDR work toward phone-based selling, relationship building, complex deal navigation, and multi-threaded stakeholder engagement. The post says teams should measure completion, qualification, contact capture, meetings, pipeline, and conversation satisfaction; Fin reports S2 pipeline volume up 77% year-on-year since enabling Fin for Sales in July.
What really matters when evaluating AI Agents for customer service?
Evaluating an AI Agent for customer service requires more than measuring accuracy, resolution rates, containment, or benchmark performance on curated datasets. A credible proof of concept should reproduce real support conditions, including multi-turn, vague, sensitive, multilingual, rephrased, and multi-source queries, while testing recovery, task tracking, and human handoffs. It should also assess the customer experience: natural, on-brand tone, trust, graceful uncertainty, and seamless transitions can differ even when quantitative scores match. Finally, teams should examine whether conversations can be reviewed for gaps, how quickly knowledge and behavior can be updated, and how the vendor supports ongoing improvement. The resulting POC demonstrates not only that the Agent works now, but that it can support long-term operational success after launch.
How we develop pricing and packaging at Fin
Pricing and packaging at Fin is treated as a cross-functional research, forecasting, and strategic problem rather than a finishing step. When new monetization is needed, the process starts with qualitative buyer research to define a pricing model and metric; for Fin, that produced value-based pricing tied to “outcomes,” each defined as the AI Agent successfully handling a customer service query. Quantitative willingness-to-pay studies using Gabor-Granger and Van Westendorp then indicate demand across price points, but do not establish a perfect price. Modeling combines those signals with discounting, usage, segment variation, attach rates, margins, sales capacity, competitor pricing, and finance forecasts to recommend a price alongside ARR and adoption scenarios before executive sign-off. The approach is iterative and applied selectively, while expanding products and capabilities create an ongoing need to review whether the pricing system remains coherent and aligned with delivered value.
Ready for your busiest day: How we scale
Fin describes how its customer service platform is designed to absorb high-volume events while preserving customer isolation, availability, and operational control. At daily peaks, it handles over 150,000 customer requests per second and more than 70,000 asynchronous requests per second; its source-of-truth data spans 128 Vitess shards, while Elasticsearch supports over 650TB, 1.7 trillion documents, and peaks above 40,000 requests per second. The architecture favors AWS primitives, Vitess managed by PlanetScale, online and incremental migrations, fair queues, feature flags, heartbeat metrics, and automatic rollback, with roughly 250 production ships per workday and merge-to-production time under 10 minutes. The stated conclusion is that scale depends less on headline capacity than on clear scaling levers, tenant fairness, customer-outcome monitoring, reversible change, and routine infrastructure improvement without planned customer-impacting downtime.
Agents can do the work
Agent evaluations often ask what an AI Agent can do, but the larger constraint may be whether an organization is structured to let it act. Across organizations in energy, healthcare, ecommerce, gaming, financial services, and property management, teams found that Agents could often perform actions technically, but stalled on unreliable identification, inaccessible live data, or unreachable vendor systems. The post describes five readiness types—content, scope, procedural, data, and execution—and says deeper automation depends on documented processes, trustworthy connected data, stable APIs, and clear ownership of risk. Teams that lacked these foundations often redesigned toward guiding users rather than executing changes, while teams with standardized operations could trigger tightly controlled actions. The unresolved question is how readiness develops, since the gaps may involve undocumented procedures, fragmented systems, missing APIs, or infrastructure and accountability investments beyond buying a more capable Agent.
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