Problems we solve

Examples

Revenue & GTM

ABM spend on traffic that never behaved like buyers

Decision-stage accounts mix researchers with real evaluators. Grade which sessions actually looked like buyers — before the quarter closes.

By role

Revenue & GTM

For CMO, growth, RevOps — B2B SaaS, DTC, and e-commerce

B2B

Wasted ABM spend on traffic that never behaved like buyers

The pain: You pay for in-market accounts from 6sense or Demandbase, but CPL stays flat — Decision-stage traffic mixes researchers with real evaluators.
Why your stack misses it: Account buying stages refresh daily. They cannot tell you whether this visit is comparing vendors or passively browsing.
What intentLM does: Classify each session path in real time — upgrade-seeking, competitor evaluation, passive research — to grade campaign quality, not just account stage.
Outcome: Reallocate budget toward high-intent sessions; feed offline conversions by intent tier to ad platforms.

Both

Losing ready-to-buy visitors who never fill out a form

The pain: Trials and anonymous visitors hit pricing and stall — but no MQL fires until they self-identify.
Why your stack misses it: Form fills and CRM stages are lagging indicators. Product analytics shows page views, not buyer state.
What intentLM does: Session-path classification on pricing, checkout, and paywall patterns — webhook to Slack or your MAP while the user is still on site.
Outcome: Founder or SDR outreach on hot sessions before intent cools.

B2B

Sales prioritizing the wrong accounts at the wrong time

The pain: SDRs chase accounts that “look in-market” when the person on your site today is not the buyer.
Why your stack misses it: Lead scores and account intent are aggregates. They do not describe this session’s behavior.
What intentLM does: Attach session intent to the visit that preceded the form — path class + confidence on the converting session.
Outcome: Prioritize callbacks for demo-imminent or purchase-ready sessions, not every Decision account.

B2C

Cart abandonment before your recovery email fires

The pain: Shoppers idle on cart or checkout, compare prices, and leave — Klaviyo or abandoned-cart flows trigger an hour later.
Why your stack misses it: Email recovery is batch and post-session. By then the shopper is on a competitor site.
What intentLM does: Classify cart-abandonment-imminent and checkout-hesitation paths in real time — trigger chat, offer, or agent while they are still on site.
Outcome: Recover revenue in the session, not in the inbox.

B2C

Checkout friction that kills high-intent shoppers

The pain: Payment and shipping loops, rage clicks on checkout, and tab switches with no support or simplify trigger.
Why your stack misses it: E-commerce analytics shows funnel drop-off, not whether the shopper is stuck vs. comparing.
What intentLM does: Detect checkout friction and purchase-intent patterns — route to concierge chat or surface shipping help before exit.
Outcome: Save high-intent sessions that would have silently abandoned.

By role

Customer Success

For VP CS, support, retention — subscriptions, SaaS, and DTC

Both

Finding out about churn after the cancel flow starts

The pain: Customers enter billing, downgrade, and cancellation pages long before CS or a save offer appears.
Why your stack misses it: Health scores and NPS are periodic. Session replay requires someone to watch hours of video.
What intentLM does: Churn-signal intents fire on cancellation and downgrade paths — alert CS or trigger save workflow in the same session.
Outcome: Intervene before the subscription ends, not in the exit survey.

Both

Support agents who start every conversation blind

The pain: The first message is always “How can I help?” while the user already spent ten minutes on pricing and docs.
Why your stack misses it: Ticketing systems see the ticket text, not the behavioral path that led to it.
What intentLM does: Classify session intent before chat opens and pass it to the agent or copilot.
Outcome: Faster resolution and higher CSAT without asking the user to re-explain their journey.

B2B

Expansion signals buried in usage data nobody monitors

The pain: Accounts hit seat limits and feature gates but no one reaches out until renewal.
Why your stack misses it: Product analytics dashboards are not wired to real-time CS alerts for paywall moments.
What intentLM does: Expansion intents (seat capacity, feature gate, blocked invite) trigger webhooks to CS or account owners.
Outcome: Proactive upgrade conversations at the moment of need.

B2C

Post-purchase anxiety that drives returns

The pain: After checkout, buyers loop on order confirmation, tracking, and returns policy — then request refunds or chargebacks.
Why your stack misses it: Post-purchase flows are treated as operational, not as a retention moment with classified intent.
What intentLM does: Detect post-purchase anxiety paths and trigger reassurance, proactive support, or order-status help in the same session.
Outcome: Fewer returns and chargebacks before buyer remorse hardens.

By role

Product & Growth

For VP Product, growth, lifecycle — apps, media, and retail

Both

Analytics that tell you what happened, not what it meant

The pain: Funnels show drop-off at step three — not whether the user was confused, comparing, or ready to buy.
Why your stack misses it: Mixpanel, Amplitude, and PostHog are event warehouses. You maintain “intent” segments by hand.
What intentLM does: A shared taxonomy maps paths to typed intent classes — plus exportable token sequences for churn and LTV models.
Outcome: One language for buyer state across teams and downstream ML, not fifty custom event definitions.

Both

Session replay at scale nobody has time to watch

The pain: You pay for FullStory or Hotjar but insights depend on someone manually reviewing recordings.
Why your stack misses it: Replay tools capture behavior; they do not classify it. Review does not scale.
What intentLM does: Automated session-path classification on every visit — no video, no PII.
Outcome: Operational intent signal for 100% of sessions, not the 2% someone watched.

Both

Onboarding drop-off you only see in weekly retention charts

The pain: New users stall in setup. By the time retention dips show up, the cohort is gone.
Why your stack misses it: Weekly cohort reports are too slow for in-session rescue.
What intentLM does: Onboarding and confusion intents on setup loops and help-center spirals — trigger guides or CS outreach same session.
Outcome: Rescue activations before day-seven retention, not after.

B2C

Paywall readers who leave without subscribing

The pain: Free readers hit article limits and plan comparison pages, then bounce without a trial or subscribe CTA at the right moment.
Why your stack misses it: Pageview funnels do not distinguish passive browsing from paywall-ready intent.
What intentLM does: Classify upgrade-seeking and paywall-hesitation sessions — trigger offer, trial extension, or agent at peak intent.
Outcome: Convert anonymous readers before the session ends.

By role

AI & Engineering

For Engineering, agent builders, platform teams

Both

Copilots and agents that lack user context on the first turn

The pain: Your agent asks generic questions while the user’s session already showed clear intent.
Why your stack misses it: LLM context does not include structured behavioral state unless you build extraction pipelines yourself.
What intentLM does: Real-time Intent Object via API or MCP — intent class, confidence, session tokens — before the agent responds.
Outcome: Agents that act on classified behavior, not guesswork from a single chat message.

Both

Building intent in-house on raw clickstreams

The pain: Every team reinvents rules on URLs and events. Privacy review blocks PII-heavy pipelines.
Why your stack misses it: DIY intent layers are expensive, hard to govern, and do not transfer across products.
What intentLM does: Intent as an API: integer tokens leave the browser; classification runs on a shared model tier.
Outcome: Ship intent in days, not quarters — without training on emails, URLs, or DOM text.

Both

The “second SDK” objection blocking instrumentation

The pain: Engineering refuses another browser tag; PostHog or Segment already captures events.
Why your stack misses it: Adding intent usually means duplicating capture or hand-rolling server-side rules.
What intentLM does: PostHog or Segment passthrough: existing events → server-side token mapping → classify.
Outcome: Intent on the CDP you already run — engineering signs off faster.

By industry

Industry-specific pains

Vertical scenarios beyond the role cards above. Cart abandonment, paywall, and post-purchase pains are covered under Revenue, Product, and Customer Success.

PLG, trials, and enterprise sales

B2B SaaS

Demo →

Enterprise buyers research security and procurement alone

High-intent evaluators hit SSO, security, and procurement pages but never request a demo.

Route to AE with behavioral proof — not just firmographic fit.

Recover revenue in the session

E-commerce & DTC

Demo →

VIP shoppers treated like casual browsers

Repeat product views and cart adds with no priority chat or concierge for high-intent sessions.

Route your best sessions to human help or exclusive offers.

Patient journeys without PHI exposure

Healthcare & health tech

Patients abandon scheduling mid-flow

Appointment booking stalls with repeated back-navigation — no coordinator alert.

Live scheduling assist before they leave the portal.

Benefits and claims portals confuse patients

Route to support with classified confusion intent — tokens only, no PHI in the model.

Admissions and yield

Higher education

Counselors cannot prioritize among thousands of prospects

Financial aid, campus visit, and application-start behavior is invisible until CRM sync.

Outreach to applicants showing serious intent while they browse.

Admitted students deposit elsewhere

Deposit-page visits and competitor-comparison paths with no timely nudge.

Yield protection before decision day.

Booking conversion

Hospitality & travel

Guests hesitate after selecting a room

Payment page idle and tab switches while comparing OTA prices.

Rate hold or chat offer before abandonment.

Evaluation and expansion

Cybersecurity

Security evaluators research without requesting a demo

Pricing, comparison, and integration docs consumed with no AE signal.

Notify sales when evaluation intent peaks on your properties.

Application completion

Fintech & financial services

Account and loan applications stall on KYC

Multi-step onboarding loops without completion on funding or disclosure steps.

Completion assist or human handoff before application abandonment.

Two-sided growth

Marketplaces

Supply and demand both stall before the first transaction

Providers never list; buyers search and browse without booking.

Ops outreach on stalled onboarding or nudge at peak purchase intent.

See it on a real session path

Click through a demo product and watch intent update as you navigate.

B2B sandbox E-commerce sandbox Start free →