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Dialogflow lead qualification for teams that pay for traffic and cannot afford to lose it

Dialogflow AI lead qualification automation that checks each answer, scores against your table and routes hot leads to the right person

In Dialogflow CX a form collects the parameters a flow needs, and each parameter has an entity type such as an email address or a phone number, so what a visitor types is checked against a defined shape before the conversation moves on. BoldBellows records the session ID, the entry page and the parameters collected so far before a score is proposed, scores only from what the visitor said and sends nothing to the CRM that a person has not approved, then designs the form around the questions your sales team actually asks, applies the written scoring table in a webhook, lets a generator or playbook propose a short summary and a next step, saves the lead once to your CRM, and sends the hot ones to a named person with the session history attached. The model never writes to a prospect on its own; a person approves anything that leaves the building.

View Pricing

From $1,385, paid once. Qualify leads with Dialogflow.

  • Qualify leads with Dialogflow
  • Typed answers only
  • Hot leads routed fast
Dialogflow AI lead qualification automation that collects typed parameters in a form scores them against a written table in a webhook routes hot leads to a person with the session attached and keeps the CRM free of duplicates, built by BoldBellows

What the Dialogflow AI route has to show you

  • Typed parametersemail, phone and choices
  • Scored by your tablewritten, not guessed
  • One lead, oncematched on email
  • Hot leads routedwith the session
  • Scoped accessCredentials are limited to the objects in scope and can be revoked
  • No meter running$1,385 covers design, build, tests and handover; nothing accrues afterwards

B2Before and after

A chat that collects names and ranks nobody

A chatbot that collects a name and an email produces a list, not leads. Sales cannot tell the buyer with a budget from the visitor with a question, so everyone waits for the same slow reply. When the agent does ask good questions, a model that scores the answers freehand gives different numbers on different days. The same person chats twice and appears twice in the CRM, and the one visitor ready to buy waits until morning.

Sound familiar?

A list, not leads

Nothing ranked the buyers.

A score that moves

The model scored freehand.

The same person twice

Nothing matched on email.

A ready buyer waiting

Hot leads were not routed.

Typos in the CRM

Answers were not typed.

An unapproved message

The model could write to prospects.

The same sales team, working the leads that matter first

Before

Before Dialogflow to AI workflow automation: manual messages exports, duplicates and broken formulasThe old way: a spreadsheet with a duplicated message, #N/A and #REF! errors and a note asking which row is right.messages (2).csvstatus changes v3.csvmessages_export_FINAL_v3.xlsxMessageContactRepliesStatus#1048Maya Chen9Delivered#1049Omar Hale14Delivered#1049Omar Hale14Duplicate#1050Lena Brooks#N/ADelivered#1051Sam Ortiz21#REF!Which row isthe right one?
  • Every hook delivery becomes a write
  • Every visitor gets the same slow reply
  • The CRM fills with duplicates and half answers with Dialogflow AI Lead Qualification

After

After Dialogflow to AI workflow automation: one clean workflow that matches DialogflowThe new way: a five-week calendar grid coloured by daily net conversions, from low to high.Replies conversions by dayLast 5 weeksMTWTFSS9k13k13k14k18k15k19k19k16k16k20k16kLowHighUpdated live
  • Identity is fixed by one key from the lead's email or phone, the session ID and the version of the scoring table and never re-guessed
  • Hot leads reach a person within minutes for Dialogflow AI Lead Qualification
  • Each lead is saved once with checked answers

D4How it works

How a chat becomes a scored lead in front of the right person

Every step has a check before it, so a fault stops the run instead of spreading.

How Dialogflow to AI workflow automation works: messages in, one clean proposal each, on a scheduleYour Dialogflow agent sends data through a read-only link to the BoldBellows sync, which removes duplicates and handles status changes, then fills your AI workflow on your schedule.Your DialogflowagentContacts,conversations,MessagesQueueStatus changesBoldBellows syncReads only, writes onceNo duplicate proposalsEdits and status changes updateYour time zone and settingsYour AIworkflowQueues, reportsand a dashboardRead-onlyYour scheduleHow Dialogflow to AI workflow automation works: messages in, one clean proposal each, on a schedule, stacked: your agent, the BoldBellows sync, then your AI workflow.Your Dialogflow agentContacts, conversations,messagesMessagesQueueStatus changesBoldBellows syncReads only, writes onceNo duplicate proposalsEdits and status changes updateYour time zone and settingsYour AI workflowQueues, reports and a dashboardRead-onlyYour schedule

1. Check it is real and in scope

The Dialogflow notice is validated, the underlying record is read, and only a qualifying Dialogflow event moves on.

2. Ask, check, score

Questions are asked in a form, each answer is typed and checked, and the table produces the score.

3. Match, save, route

The lead is matched on email, saved once, and hot ones go to a person with the session.

E5Inside your workflow

Inside the qualification agent: the parts that ask, score and route

Signal answers whether this should happen. Control answers what exactly gets written. Reconcile answers whether it is still right next month.

Owner dashboard fed by Dialogflow to AI workflow automationOwner dashboard in AI workflow: four KPI tiles, a daily net conversions chart and a best sellers list.Owner dashboardLast 14 daysReplies conversions1842+8.2%Messages312+5.1%Avg message6+1.4%Status changes64-0.6%Replies conversions by day8 Sep21 SepTop intentsWelcome message86Survey request54Support reply41Booking confirmation37Verification code29

Signal

Intake rule reader

Interprets the Dialogflow payload against the intake rule and stamps the decision on the ledger.

Signal

Stable identity ledger

Gives every Dialogflow proposal an identity made of the object, the model run and the policy, then stores the decision under it.

Signal

Map change log

Records every change to the Dialogflow field map, so a later question about a the AI workflow value has an answer.

Control

Parameter validator

Checks each collected answer against its entity type.

Control

Lead matcher

Keeps a repeat from becoming a second proposal by checking one key from the lead's email or phone, the session ID and the version of the scoring table before the write.

Control

Scoring table matrix

Applies your written rules and records which fired.

Reconcile

Lead register

Measures how far each proposal has slipped from the Dialogflow record and reports it.

Reconcile

Score drift monitor

Flags answers whose score changed between runs, in the Dialogflow AI Lead Qualification service.

Reconcile

Failure ledger

Records every failed or refused write to the AI workflow with its cause and what was done, on the Dialogflow AI Lead Qualification service.

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G7Who it is for

Where universities need to turn Dialogflow conversations into qualified leads: collect typed parameters in a form, score them against your written table, route the hot ones to a person and keep the rest warm

Visitors want a quick, relevant reply, and sales teams want to spend their time on the people most likely to buy, for this Dialogflow AI Lead Qualification service.

Sales director

"Show me the buyers."

Leads arrive scored.

Account executive

"Give me context."

I get the session history.

Marketing manager

"Stop the waste."

Traffic becomes ranked leads.

RevOps analyst

"Clean data."

Each lead is saved once.

Founder

"Don't annoy prospects."

A person approves outreach.

Visitor

"Answer me quickly."

The right person replies.

Where the money goes: visitors, parameters and scores

Cost your qualification agent, one rule at a time

Signal begins at $1,385.

View Pricing

One setup fee

I9Your options

Where a contact form dressed up as a chat and zaps stop, a Dialogflow qualification agent with a written scoring table begins

Each can ask a question. Only one types the answers, scores by your table and routes the hot ones.

Comparison table: Where a contact form dressed up as a chat and zaps stop, a Dialogflow qualification agent with a written scoring table begins
How a chat startsA chat formA generic chatbotOur scoring agent
What starts the workThe next person to lookEach delivery, repeats includedRule-approved events, logged with the reason
How identity is decidedGuessed from contextA single field treated as the keyOne key: email or phone, session ID and scoring table version
What reaches the AI workflowA few fields, entered lateA copy without contextA scored lead saved once, with the session attached for the owner
After an uncertain resultOpen both apps and compareRuns again from the startMatches on one key from the lead's email or phone, the session ID and the version of the scoring table, then repairs or holds
By handstops to export, copy, paste, fixAutomatedno stops · every 15 minutes

A late arrival

Events that reach the route after a newer one are recognized by timestamp and routed for review instead of applied, in the Dialogflow AI Lead Qualification service.

The same visitor returns after a week

The new session matches the first on email, the answers are merged into one lead, the score is recalculated, and the owner sees one lead with two sessions. Finding the proposal comes first: one key from the lead's email or phone, the session ID and the version of the scoring table is searched, the hit is compared, and only then is an action chosen.

Nothing is written to the AI workflow until one key from the lead's email or phone, the session ID and the version of the scoring table is defined and stored for each proposal.

Pricing

Build your Dialogflow AI Lead Qualification price

Pick a package, answer three short questions about how Dialogflow data should reach AI Lead Qualification, and your price and turnaround move as you choose.


Choose your packageRequired

The three packages differ by how much routing, reconciliation and governance sits around the Dialogflow to AI Lead Qualification route.

A focused production route for one Dialogflow event family and one governed lead qualification queue, delivering Dialogflow AI lead qualification automation that collects typed parameters in a form, scores them against your written table in a webhook, routes hot leads to a person with the session attached and keeps the CRM free of duplicates with identity, evidence and failure controls from day one.

  • One approved Dialogflow event family and business-state admission rule within the Dialogflow AI Lead Qualification service.
  • One authorized account, workspace, organization, or base boundary within the Dialogflow AI Lead Qualification service.
  • One AI Lead Qualification object contract with field and relationship mapping within the Dialogflow AI Lead Qualification service.
  • Stable matching through Google Cloud project plus location, agent, environment, session, intent, page, or webhook request ID and source identity paired with the authoritative ai lead qualification record, execution, conversation, transaction, or decision identifier
  • Tests for duplicates, stale events, malformed data, denied access, and timeouts within the Dialogflow AI Lead Qualification service.
  • Implementation map, test record, and premium PDF operations handover within the Dialogflow AI Lead Qualification service.

Adds branching, existing-record matching and a bounded historical pass for teams whose Dialogflow signals need more than one approved lead qualification path, while still delivering Dialogflow AI lead qualification automation that collects typed parameters in a form, scores them against your written table in a webhook, routes hot leads to a person with the session attached and keeps the CRM free of duplicates.

  • Everything in Foundation
  • Up to four approved routing or state-transition branches within the Dialogflow AI Lead Qualification service.
  • Existing-record resolution and one bounded historical checkpoint within the Dialogflow AI Lead Qualification service.
  • Lookup-before-replay protection for uncertain writes
  • Named exception ownership and a decision-ready evidence packet within the Dialogflow AI Lead Qualification service.
  • Field, permission, and destination-state validation before mutation within the Dialogflow AI Lead Qualification service.

Adds reconciliation, drift detection and an aged-exception report for teams that now depend on lead qualification proposals every day.

  • Everything in Controlled Flow
  • Scheduled source-to-destination reconciliation
  • Schema, field, scope, and webhook drift checks within the Dialogflow AI Lead Qualification service.
  • Volume-aware retry ceilings and stale-event protection
  • Aged-exception reporting with ownership status
  • Thirty-day release watch and operating review

For the Dialogflow AI Lead Qualification service, multi-business-unit routing, unrestricted bidirectional synchronization, regulated archives, unsupported extensions, or additional destination systems receive a separate written quote. Third-party subscriptions and usage charges are excluded.

Which Dialogflow changes should be allowed to start an AI lead qualification decision?Required

Governed lifecycle set: Use this when several documented Dialogflow states must converge on one lead qualification decision.

How should a Dialogflow record that already has an AI lead qualification decision be recognized?Required

Ambiguity queue: Send zero-match and multi-match lead qualification cases to a named reviewer instead of letting the model guess.

What should happen when an AI lead qualification action can't be confirmed?Required

Managed repair case: Give every uncertain lead qualification action an owner, a live-state comparison and an approved replay decision.

How soon do you need it live?Required

Standard follows the schedule your other choices set. Pay to move up the queue and go live sooner.

Optional extrasOptional

Each one begins switched off. Turn on what your rollout needs, watch the total, switch it back off.

J11The process

Six steps from a chat widget to a qualification engine

Each of the six steps has an output you can read, not just a status update.

  1. Define a qualified lead

    We write down what makes a Dialogflow lead hot, warm or cold, and which answers decide it.

    Day 1
  2. Approve access

    We set up read access in Dialogflow, choose how the model is reached and agree what every decision record will contain.

    Access review
  3. Agree the table and the wording

    In the Dialogflow AI Lead Qualification service, the questions, the scoring table, the routing and what the model may and may not say are written out and approved first.

    Build phase
  4. Stress the edges

    Late deliveries, doubled events and partial failures are simulated in a test the AI workflow and the results reviewed with you, on the Dialogflow AI Lead Qualification service.

    Before you review
  5. Launch with notes

    The last step is release: a defined start, a review window and a PDF you can keep.

    Release day
  6. Post-launch review

    We go through the exception list and the checkpoint log with you and agree what, if anything, needs a change.

    Day 1
Handover for Dialogflow to AI workflow automation: notes, a checked run and a health checkHandover timeline: brief, map, build, test, hand over and a week of support, with the current step highlighted.From first brief to a workflow that runs itself1Brief1 page2MapFields3BuildSync4TestEdge cases5Hand overPDF pack6Support1 weekClear scopeSet onceTested on real messagesZero duplicatesOwned by youPlain notes

K12Turnaround

How long the AI Lead Qualification packages take

Days are counted from approved Dialogflow access and model setup.

Signal

6 to 10 business days

Control

10 to 16 business days

Reconcile

16 to 24 business days

L13Getting started

What we ask of you

Authorization to reach the Dialogflow objects in scope

Dialogflow CX in a Google Cloud project you control, a CRM account the webhook can write to, and a named owner for hot leads

Access to the Dialogflow account that holds the records in scope

Your expected behavior for edits, cancellations and repeats

The hours when changes to the AI workflow are safest to release

Records or dates that the route should ignore

M14Safety and ownership

Narrow permissions, narrow payloads, nothing patched

The model is given a bounded view of Dialogflow, and its answer is validated and approved before the route acts on it.

Read-only Dialogflow to AI workflow access: your data stays yoursAn AI workflow covered by a padlock with a note that you own it and access is read-only.Messages (read-only)You own itRead-only access

Untouched core, documented edges

Both Dialogflow and the AI workflow are used as their own documentation describes; no workaround reaches into their internals.

Scoped, revocable access

Permissions are written down, minimal and reversible.

Nothing extra travels

The route forwards what was agreed and ignores the rest, so the AI workflow never becomes a second copy of everything in Dialogflow.

Proof before repetition

Nothing is created twice: a missing confirmation triggers a search of the AI workflow by one key from the lead's email or phone, the session ID and the version of the scoring table before any further write.

Minimal logging

Evidence of a run is kept as references, not as copies of data.

Full handover

The PDF names each rule, credential and owner so your admins are never guessing.

N15In their words

What BoldBellows clients say

Verified reviews from business owners who use BoldBellows automations every day, from marketing and sales to support and operations.

AI Workflow Automation for Agencies

Running a digital agency with many clients was overwhelming. BoldBellows built one automation dashboard that tracks tasks, sends updates and creates client reports automatically.

Clients love the transparency and we love the time we get back. Productivity is up, and project delays have dropped to almost zero.

Samuel BoatengVerifiedAccra, Ghana

AI Customer Journey Automation

We wanted a smoother path from first contact to purchase. BoldBellows built an AI system that guides each customer there automatically.

It sends personalized messages, recommends products and follows up at just the right moment. Customer retention has improved dramatically.

Nadia HassanVerifiedCairo, Egypt

AI Document & Contract Automation

Onboarding was slow because we wrote every contract by hand. BoldBellows automated the lot: contracts, welcome emails and client setup.

What took 48 hours now takes under 10 minutes, and clients regularly tell us how professional it feels.

Michael O’ConnorVerifiedDublin, Ireland

AI Social Comment & DM Automation

We get thousands of comments and messages every week. BoldBellows built an AI system that replies instantly, filters spam and surfaces the important ones.

Engagement is up and our page looks active around the clock. It’s like having a full social media team that never stops.

Sofia DimitrovVerifiedSofia, Bulgaria

AI CRM Automation

Our CRM was barely used because updating it by hand was tedious. BoldBellows automated data entry, tagging, segmentation and follow-ups.

Now the CRM is always up to date and our campaigns are far more accurate. It improved our whole sales pipeline.

David KimVerifiedSeoul, South Korea

AI Voice Assistant

BoldBellows built a custom AI voice assistant for our customer hotline. It answers questions, books appointments and handles simple requests.

Customers love how fast it is, and our call-centre workload dropped dramatically. One of the smartest upgrades we’ve made.

Hannah JohnsonVerifiedChicago, USA

AI Automation Partner

BoldBellows is hands-down the best AI automation partner we’ve worked with. Their systems are fast, reliable and built around how our business actually works.

Every automation they’ve delivered, from customer service to content, runs flawlessly. We’ve saved time, cut costs and grown faster than we expected.

Kwame MensahVerifiedKumasi, Ghana

AI Chatbot & Customer Support

BoldBellows built an AI support chatbot for our online boutique, and it has changed how we run the business. We used to have two full-time staff answering the same questions all day. Now the AI handles order tracking, product recommendations and even refund requests on its own.

What surprised me most is how natural it sounds. Customers compliment the ‘agent’ without realizing it’s AI. Our support workload dropped by 75% and customer satisfaction went up dramatically.

Maria GonzálezVerifiedMadrid, Spain

AI Content & Social Media Automation

Our marketing agency couldn’t keep up with content. BoldBellows set up an AI content engine that writes blog posts and captions and even designs our social media creatives.

It spots trends and suggests topics for us, so we stay ahead of competitors. We now produce three times the content with the same team, and engagement has soared. They didn’t just automate our workflow, they lifted our whole brand.

Daniel BrooksVerifiedToronto, Canada

P16FAQ

Dialogflow lead qualification: what sales teams ask

We collected the questions that decide most purchases and answered them.

PriceTimeAccessHandover?Every question, answered
What does this add over a Dialogflow AI action in a connector?

A zap can provide every serious enquiry captured, scored against your table and routed the same hour. It is not built to check each answer, score by your table or keep duplicates out of the CRM. The work here is the judgment around the connector, not the connector itself.

Does the model decide the score?

No. Your written table produces the score; the model only proposes a summary.

How are answers checked?

Form parameters use entity types such as email and phone number.

Where are leads stored?

In your CRM, matched on email, with a log in your own store.

Who contacts the prospect?

A person. The agent never writes to a prospect by itself.

Can a visitor skip a question?

Yes, and the score notes what is missing, for this Dialogflow AI Lead Qualification service.

Which CRMs work?

HubSpot, Salesforce and others with an API, on the Dialogflow AI Lead Qualification service.

Can we limit the model to a few Dialogflow fields?

A signed field map comes with every build: what the model sees, what it may propose and what only a person may change in Dialogflow.

Can the AI route handle exceptions our way in the Dialogflow AI Lead Qualification service?

Yes. A custom Dialogflow AI route is the normal case, not the exception, and packages start at $1,385.

What if Dialogflow retires a feature we use?

The handover lists every interface the route uses. If one changes, the affected item stops and is flagged instead of being written from a guess. Repairs after your review window are quoted separately. This page also answers searches for custom dialogflow ai lead qualification automation.

Q17Turn website chats into Dialogflow leads your sales team can rank

Give your sales team leads already sorted by your table

We design the Dialogflow AI route around it, starting at $1,385.

  • From $1,385, paid once
  • Typed parameters
  • Scored by your table
  • No monthly fee from us

Unusual volumes or extra systems are quoted on request.