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/* =========================
Speak – Data Collection (page-scoped)
– Same layout pattern as Structured Outputs + Voice Agents
– Minimal CSS: relies on global tokens + btn-* classes
========================= */
.sp-data-collection{
–sp-text: var(–bodyText, #0f172a);
–sp-muted: var(–bodyMuted, #475569);
–sp-light: var(–bodyLight, #64748b);
–sp-border: var(–border, #e5e7eb);
–sp-soft: var(–soft, #f8fafc);
–sp-shadow: var(–shadowMd, 0 10px 30px rgba(0,0,0,.06));
–sp-radius: 14px;
–sp-focus: 0 0 0 3px rgba(37, 99, 235, .22);
color: var(–sp-text);
font-family: var(–fontBody, system-ui, -apple-system, BlinkMacSystemFont, “Segoe UI”, Roboto, Arial, sans-serif);
}
.sp-data-collection *{ box-sizing:border-box; }
.sp-data-collection section{ padding: clamp(2.75rem, 5.5vw, 4.75rem) 1.25rem; }
.sp-data-collection .sp-container{ max-width: 1180px; margin: 0 auto; }
.sp-data-collection .sp-kicker{
font-size:.85rem; font-weight:650; color:var(–sp-muted);
letter-spacing:.05em; text-transform:uppercase; margin:0 0 .6rem;
}
.sp-data-collection h1{
font-family: var(–fontHeading, inherit);
font-size: clamp(2.05rem, 4.2vw, 3.05rem);
line-height: 1.06;
margin: 0 0 1rem;
letter-spacing: -0.02em;
}
.sp-data-collection h2{
font-family: var(–fontHeading, inherit);
font-size: clamp(1.55rem, 2.6vw, 2.05rem);
line-height: 1.15;
margin: 0 0 1rem;
letter-spacing: -0.01em;
}
.sp-data-collection .sp-lead{
font-size: 1.1rem;
color: var(–sp-muted);
line-height: 1.55;
max-width: 920px;
margin: 0;
}
.sp-data-collection a{ color: inherit; }
.sp-data-collection a:not(.elementor-button):not(.elementor-button-link):not([role=”button”]){
text-decoration: underline;
text-underline-offset: 3px;
}
.sp-data-collection a.elementor-button,
.sp-data-collection a.elementor-button:hover{ text-decoration:none !important; }
/* Hero */
.sp-data-collection .sp-hero{ background:#fff; border-bottom:1px solid var(–sp-border); }
.sp-data-collection .sp-hero-grid{
display:grid;
grid-template-columns: 1.1fr .9fr;
gap: 1.75rem;
align-items:start;
}
@media (max-width: 920px){
.sp-data-collection .sp-hero-grid{ grid-template-columns: 1fr; }
}
.sp-data-collection .sp-cta-row{
display:flex; flex-wrap:wrap; gap:.75rem;
margin-top: 1.35rem; align-items:center;
}
.sp-data-collection .sp-trial{
margin-top:.85rem; color:var(–sp-muted);
font-size:.9rem; line-height:1.45;
}
.sp-data-collection .sp-trial b{ color:var(–sp-text); font-weight:650; }
.sp-data-collection .sp-hero-side{
border:1px solid var(–sp-border);
border-radius: var(–sp-radius);
background: var(–sp-soft);
padding: 1.15rem 1.15rem 1.25rem;
box-shadow: var(–sp-shadow);
}
.sp-data-collection .sp-hero-side h3{
margin:0 0 .65rem;
font-size:1rem; font-weight:700; letter-spacing:-0.01em;
}
.sp-data-collection .sp-mini-list{
margin:0; padding-left: 1.1rem;
color:var(–sp-muted); line-height:1.55; font-size:.95rem;
}
.sp-data-collection .sp-mini-list li{ margin:.25rem 0; }
.sp-data-collection .sp-pills{ display:flex; flex-wrap:wrap; gap:.5rem; margin-top:1rem; }
.sp-data-collection .sp-pill{
display:inline-flex; align-items:center; gap:.45rem;
padding:.35rem .75rem; border-radius:999px;
border:1px solid var(–sp-border); background:#fff;
font-size:.82rem; color:var(–sp-muted); white-space:nowrap;
}
/* Code snippet */
.sp-data-collection .sp-code{
background:#0b1220; color:#e5e7eb;
border-radius:14px; padding:14px; overflow:auto;
font-size:13px; line-height:1.55;
border:1px solid rgba(255,255,255,.08);
margin-top: .9rem;
}
.sp-data-collection .sp-code code{ color:inherit; }
.sp-data-collection .sp-code__row{
display:flex; align-items:center; justify-content:space-between;
gap:10px; margin:0 0 10px 0;
}
.sp-data-collection .sp-code__label{
font-size:12px; letter-spacing:.02em;
text-transform:uppercase; opacity:.9; font-weight:700;
}
.sp-data-collection .sp-copybtn{
background:rgba(255,255,255,.08);
border:1px solid rgba(255,255,255,.14);
color:#fff; border-radius:12px;
padding:8px 10px; cursor:pointer;
font-weight:700; font-size:12px;
}
.sp-data-collection .sp-copybtn:hover{ background:rgba(255,255,255,.12); }
/* Sections */
.sp-data-collection .sp-section-alt{
background: var(–sp-soft);
border-top:1px solid var(–sp-border);
border-bottom:1px solid var(–sp-border);
}
/* Trust */
.sp-data-collection .sp-trust{
padding-top: clamp(1.8rem, 3vw, 2.5rem);
padding-bottom: clamp(1.8rem, 3vw, 2.5rem);
background:#fff;
border-bottom:1px solid var(–sp-border);
}
.sp-data-collection .sp-trust-pill{
display:inline-block;
background: var(–sp-soft);
border:1px solid var(–sp-border);
padding:.35rem .75rem;
border-radius:999px;
font-size:.85rem;
color:var(–sp-muted);
}
.sp-data-collection .sp-trust-pill b{ color:var(–sp-text); font-weight:700; }
.sp-data-collection .sp-logo-grid{
display:grid;
grid-template-columns: repeat(6, minmax(0, 1fr));
gap: 1rem;
align-items:center;
margin-top: 1.15rem;
}
@media (max-width: 1020px){
.sp-data-collection .sp-logo-grid{ grid-template-columns: repeat(4, minmax(0, 1fr)); }
}
@media (max-width: 560px){
.sp-data-collection .sp-logo-grid{ grid-template-columns: repeat(2, minmax(0, 1fr)); }
}
.sp-data-collection .sp-logo{
display:flex; align-items:center; justify-content:center;
padding:.6rem .5rem; border-radius: 12px;
border:1px solid transparent;
}
.sp-data-collection .sp-logo img{
max-height:42px; width:auto;
filter: grayscale(1); opacity:.78;
transition: all .18s ease;
}
.sp-data-collection .sp-logo:hover{
border-color: var(–sp-border);
background: var(–sp-soft);
}
.sp-data-collection .sp-logo:hover img{ filter:none; opacity:1; }
.sp-data-collection .sp-metrics{
display:grid;
grid-template-columns: repeat(4, minmax(0,1fr));
gap: 1rem;
margin-top: 1.25rem;
}
@media (max-width: 980px){
.sp-data-collection .sp-metrics{ grid-template-columns: repeat(2, minmax(0,1fr)); }
}
.sp-data-collection .sp-metric{
background:#fff;
border:1px solid var(–sp-border);
border-radius: var(–sp-radius);
padding: 1.05rem 1.1rem;
box-shadow: var(–sp-shadow);
}
.sp-data-collection .sp-metric b{
display:block;
font-size:1.15rem;
letter-spacing:-0.01em;
margin-bottom:.25rem;
}
.sp-data-collection .sp-metric span{
color:var(–sp-muted);
font-size:.9rem;
line-height:1.35;
display:block;
}
/* Cards */
.sp-data-collection .sp-grid{
display:grid;
grid-template-columns: repeat(3, minmax(0, 1fr));
gap: 1.15rem;
margin-top: 1.25rem;
}
@media (max-width: 980px){
.sp-data-collection .sp-grid{ grid-template-columns: repeat(2, minmax(0,1fr)); }
}
@media (max-width: 620px){
.sp-data-collection .sp-grid{ grid-template-columns: 1fr; }
}
.sp-data-collection .sp-card{
background:#fff;
border:1px solid var(–sp-border);
border-radius: var(–sp-radius);
padding: 1.25rem 1.25rem 1.15rem;
box-shadow: var(–sp-shadow);
display:flex;
flex-direction:column;
min-height: 190px;
}
.sp-data-collection .sp-ic{
width:38px; height:38px;
border-radius:12px;
border:1px solid var(–sp-border);
background: var(–sp-soft);
display:flex;
align-items:center;
justify-content:center;
margin-bottom:.9rem;
flex:0 0 auto;
}
.sp-data-collection .sp-ic svg{
width:20px; height:20px;
stroke: var(–sp-muted);
fill:none;
stroke-width:2;
stroke-linecap:round;
stroke-linejoin:round;
}
.sp-data-collection .sp-card h3{
margin:0 0 .55rem;
font-size:1.02rem;
line-height:1.25;
letter-spacing:-0.01em;
min-height:2.6em;
}
.sp-data-collection .sp-card p{
margin:0;
color:var(–sp-muted);
line-height:1.55;
font-size:.95rem;
min-height:4.9em;
}
.sp-data-collection .sp-card-foot{
margin-top:.9rem;
display:flex;
gap:.75rem;
flex-wrap:wrap;
align-items:center;
}
.sp-data-collection .sp-chip{
font-size:.82rem;
color:var(–sp-muted);
border:1px solid var(–sp-border);
background: var(–sp-soft);
border-radius:999px;
padding:.25rem .6rem;
}
/* Split cards */
.sp-data-collection .sp-split{
display:grid;
grid-template-columns: repeat(2, minmax(0,1fr));
gap: 1.15rem;
margin-top: 1.25rem;
}
@media (max-width: 920px){
.sp-data-collection .sp-split{ grid-template-columns: 1fr; }
}
.sp-data-collection .sp-split-card{
border:1px solid var(–sp-border);
border-radius: var(–sp-radius);
background:#fff;
padding: 1.2rem 1.2rem 1.1rem;
box-shadow: var(–sp-shadow);
}
.sp-data-collection .sp-split-card h3{
margin:0 0 .5rem;
font-size:1.08rem;
letter-spacing:-0.01em;
}
.sp-data-collection .sp-split-card p{
margin:0 0 .9rem;
color:var(–sp-muted);
line-height:1.55;
font-size:.95rem;
}
.sp-data-collection .sp-split-actions{
display:flex;
gap:.75rem;
flex-wrap:wrap;
align-items:center;
}
/* SEO */
.sp-data-collection .sp-seo{
background:#fff;
border-top:1px solid var(–sp-border);
border-bottom:1px solid var(–sp-border);
}
.sp-data-collection .sp-seo-grid{
display:grid;
grid-template-columns: 1.05fr .95fr;
gap: 1.5rem;
align-items:start;
margin-top: 1.25rem;
}
@media (max-width: 980px){
.sp-data-collection .sp-seo-grid{ grid-template-columns: 1fr; }
}
.sp-data-collection .sp-seo p{
color:var(–sp-muted);
line-height:1.7;
font-size:1rem;
margin:0 0 1rem;
max-width: 980px;
}
.sp-data-collection .sp-seo h3{
margin: 1.25rem 0 .55rem;
font-size: 1.05rem;
letter-spacing:-0.01em;
}
.sp-data-collection .sp-seo-card{
background: var(–sp-soft);
border:1px solid var(–sp-border);
border-radius: var(–sp-radius);
padding: 1.15rem 1.15rem 1.05rem;
box-shadow: var(–sp-shadow);
}
.sp-data-collection .sp-seo-card h3{
margin:0 0 .55rem;
font-size:1.02rem;
letter-spacing:-0.01em;
}
.sp-data-collection .sp-seo-card p{
margin:0 0 .85rem;
color:var(–sp-muted);
line-height:1.6;
font-size:.95rem;
}
.sp-data-collection .sp-seo-card ul{
margin:0;
padding-left: 1.1rem;
color:var(–sp-muted);
line-height:1.6;
font-size:.95rem;
}
.sp-data-collection .sp-seo-card li{ margin:.35rem 0; }
/* FAQ */
.sp-data-collection .sp-faq-wrap{
margin-top: 1.25rem;
background:#fff;
border:1px solid var(–sp-border);
border-radius: var(–sp-radius);
overflow:hidden;
box-shadow: var(–sp-shadow);
}
.sp-data-collection .sp-at-faq{ border-bottom:1px solid var(–sp-border); padding: 0 1.15rem; }
.sp-data-collection .sp-at-faq:last-child{ border-bottom:none; }
.sp-data-collection .sp-at-faq-q{
width:100%;
text-align:left;
padding: 1rem 0;
background:none;
border:none;
cursor:pointer;
font-weight:700;
display:flex;
align-items:center;
justify-content:space-between;
gap:1rem;
color:var(–sp-text);
font-size:.98rem;
}
.sp-data-collection .sp-at-faq-q:focus{ outline:none; box-shadow: var(–sp-focus); border-radius:10px; }
.sp-data-collection .sp-at-faq-ic{
width:28px; height:28px;
border-radius:999px;
border:1px solid var(–sp-border);
background: var(–sp-soft);
display:flex;
align-items:center;
justify-content:center;
font-weight:800;
color:var(–sp-muted);
flex:0 0 auto;
}
.sp-data-collection .sp-at-faq-a{
padding: 0 0 1rem;
color: var(–sp-muted);
line-height: 1.6;
font-size: .95rem;
}
/* Final CTA */
.sp-data-collection .sp-final{ background:#fff; border-top:1px solid var(–sp-border); }
.sp-data-collection .sp-final-grid{
display:grid;
grid-template-columns: repeat(2, minmax(0,1fr));
gap: 1.25rem;
margin-top: 1.25rem;
text-align:left;
}
@media (max-width: 920px){
.sp-data-collection .sp-final-grid{ grid-template-columns: 1fr; }
}
.sp-data-collection .sp-panel{
background: var(–sp-soft);
border:1px solid var(–sp-border);
border-radius: var(–sp-radius);
padding: 1.25rem;
box-shadow: var(–sp-shadow);
}
.sp-data-collection .sp-panel h3{
margin:0 0 .5rem;
font-size:1.02rem;
letter-spacing:-0.01em;
}
.sp-data-collection .sp-panel p{
margin:0 0 .95rem;
color:var(–sp-muted);
line-height:1.55;
font-size:.95rem;
}
.sp-data-collection .sp-panel-actions{
display:flex;
gap:.75rem;
flex-wrap:wrap;
align-items:center;
}

Speak AI Agents + Data Collection

Collect lead and intake details at the right moment in the conversation

Data Collection is different than Structured Outputs. Instead of only analyzing what was said, you can choose when and how the agent asks for key details – at the start, naturally during the call, at the end, or only when a trigger condition is met.

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More leads captured
Ask for details when intent is clear
Less drop-off
Don’t interrupt early with forms
Cleaner CRM data
Standardized fields every time
Faster follow-up
Instant notifications on capture

Data Collection vs Structured Outputs

Both turn conversations into usable data. The difference is whether the agent actively collects the information or whether Speak extracts it after the fact.

Data Collection (active)

The agent asks for specific details at the right time. You decide the timing and conditions, so the conversation stays natural and fields get captured reliably.

See examples
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Structured Outputs (passive)

Speak analyzes what was said and extracts fields if they appear. Great for summaries, scores, and insights when you do not want to interrupt the flow.

Structured Outputs
Read FAQs

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Docs/API

Common data collection fields teams use

Data Collection is ideal when you need reliable capture for lead gen, intake, or routing. Ask for a few essentials, then collect deeper fields only after the caller confirms their goal.

Name + email

Collect contact details naturally after the first helpful exchange, then send an instant notification to your team.

Lead gen
Notification

Role + job title

Capture the caller’s role to route to the right workflow: support, sales, onboarding, research, or partnerships.

Routing
Intake

Website URL

Ask for a website only when relevant, then trigger enrichment or CRM updates automatically in your downstream tools.

CRM
Automation

Budget + timeline

Collect budget and timeline at the end, once the caller has clarity. Helps qualify without making the call feel like a form.

Sales
Qualification

Use case + intent

Confirm why the caller reached out, then trigger the right script, knowledge base, or handoff path based on intent.

Intent
Triggers

Qualification score

Ask one or two qualifying questions only when a trigger is met, then store a consistent score for routing and follow-up.

Scoring
Routing

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How Data Collection works

You can collect the same field in different ways. The key is timing. Ask for the minimum early, then collect deeper details after the caller’s intent is confirmed.

Collect at the start

Good for essentials like name or language choice. Keep it to 1-2 fields so you don’t feel like a form.

Start
Low friction

Collect naturally during the call

Ask only after intent is known. Example: website, role, plan, or region when it becomes relevant.

During
Contextual

Collect at the end

Best for budget, timeline, and next steps. The caller already got value, so they’re more likely to answer.

End
Higher completion

Collect only when a condition is met

Use triggers like “qualified lead,” “billing intent,” or “requesting demo” to ask deeper questions only when needed.

Conditional
Triggers

Validate and standardize

Make fields consistent (format, required vs optional) so CRM records are clean and automations do not break.

Standardization
Quality

Store and forward instantly

Send captured fields as notifications and push them into downstream tools for follow-up, routing, and reporting.

Notifications
Automations

Integrations
Docs/API
FAQs

Playbooks: where Data Collection fits

Use Data Collection when you need reliable capture. Use Structured Outputs when you want post-call analysis without interrupting the flow. Most teams combine both.

Lead qualification + routing

Collect name, email, role, and intent. If the caller is qualified, ask for website and timeline. Then notify your team and route to the right next step.

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Support intake + escalation

Ask for product, plan, and issue category only after the first question. If it is billing or security, collect minimal details and escalate with context.

Knowledge Base
FAQs

Don’t ask too early

Start with help first. After the agent answers one meaningful question, collecting contact info feels natural.

Best practice
Conversion

Keep prompts short

Data Collection prompts should be single-sentence questions. Long prompts reduce completion and feel robotic.

UX
Voice

Collect the minimum for sensitive topics

For billing, account changes, and security, collect only what’s needed and hand off quickly to a human.

Safety
Escalation

AI agent data collection: turn conversations into CRM-ready fields

Conversations are high-signal, but they are messy. People say their name quickly, mention a website in passing, and describe a use case in a few different ways. Data Collection makes these details reliable by giving you control over how a field is collected, when it is collected, and what happens the moment it is captured.

Data Collection is ideal for inbound phone calls, website chat, support intake, and lead qualification. Instead of relying on the caller to volunteer the details you need, your agent can ask at the right moment and store responses in a standardized way. This prevents missing information and reduces back-and-forth for your team.

What is data collection in an AI agent?

Data Collection is a configurable way to capture specific fields from a conversation. You define a label (for example: “Job Title” or “Website URL”), the prompt text the agent should use, and the timing rules. The goal is simple: turn a live conversation into structured fields your team can actually use.

Why timing matters

Asking for too much too early feels like a form and causes drop-off. Asking for too little can lead to poor routing and slow follow-up. The best approach is staged: ask for one or two essentials up front, then collect deeper fields after the caller’s intent is clear or a trigger condition is met.

Data Collection vs Structured Outputs

Structured Outputs extract fields after the conversation by analyzing what was said. Data Collection actively asks for specific information. In practice, Data Collection is best for contact details and routing fields that must be captured reliably. Structured Outputs are best for summaries, notes, and insights where you do not want to interrupt the flow.

How notifications and automations fit

Once a field is captured, you can notify your team immediately. This is useful for fast follow-up on qualified leads, routing support issues, or creating clean records for reporting. You can also trigger automations to update CRMs or internal tools, so your team does not need to re-enter information manually.

Best practices for high completion

Keep prompts short and natural. Avoid stacking multiple questions in one turn. Collect deeper fields only after providing value. And make sure you standardize fields (like email, region, and plan) so downstream automations stay stable.

Example: demo request flow

A simple demo request flow might collect name and email after the agent answers a first question, then capture role and use case during the conversation, and finally ask for timeline at the end. If the caller indicates enterprise needs, you can trigger additional collection such as region, deployment requirements, or security review timing.

Example: support intake flow

For support, the agent can confirm product area and issue category first, then ask for account email only if escalation is needed. This keeps the conversation fast for simple questions while still creating clean records for complex cases.

Example: phone intake flow

For phone calls, Data Collection helps capture details that are often missed: spelling of names, email confirmation, call purpose, and the correct next step. Keeping prompts short is especially important in voice.

Frequently asked questions

Common questions about Data Collection, timing options, trigger conditions, and how it differs from Structured Outputs.


Data Collection is a configurable way to capture specific fields (like name, email, website, role, or intent) by choosing how and when the agent asks for them during a conversation.

Data Collection actively asks for information at the right moment. Structured Outputs analyze the conversation afterward and extract fields only if they appear naturally.

Collect 1-2 essentials early, collect contextual fields during once intent is known, and collect budget or timeline at the end after the caller has received value.

Condition-based collection asks for a field only when a rule is met, such as a qualified lead, a specific intent, or a request that requires escalation.

Yes. When a field is captured, you can notify your team immediately and trigger automations to push data into your CRM or internal workflow.

Keep prompts short, avoid stacking multiple questions at once, ask for deeper fields only after providing value, and use triggers so you only collect what is necessary.

Yes. Many teams use Data Collection for must-have fields (contact + routing) and Structured Outputs for summaries, notes, scoring, and insights after the conversation.
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Turn conversations into clean data your team can use

Configure a few fields, choose the right timing, and route captured data to notifications and automations. Data Collection helps your agent drive real outcomes, not just nice conversations.

Start self-serve

Configure your first 4 fields, test completion rates, then add conditional triggers for deeper lead qualification.

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Work with our team

Want a production-ready intake flow? We’ll help you map fields, timing, and triggers, then connect automations for CRM and routing.

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Questions? Call +1 (647) 372-1565 or email [email protected]


AI-Powered Analysis with Speak AI

Speak AI combines transcription, NLP analytics, sentiment analysis, and AI agents into one platform. Built for researchers, teams, and enterprises working with audio, video, and text data. Supports 100+ languages.

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Build Audio and Video Data Collection Agents with Speak AI

Data collection agents built on Speak AI ingest audio and video from configured sources, extract structured fields via AI, and deliver clean data to downstream systems — automatically and at scale. The API accepts file uploads, public URLs, and streaming inputs, and returns structured JSON with transcript text, speaker segments, entities, themes, and custom-prompted fields.

Agent patterns that work with Speak AI

  • Interview data extraction — submit batches of interview recordings, receive structured field output (sentiment, themes, named entities) per respondent
  • Media monitoring pipelines — ingest video clips from configured sources, extract keyword and topic data, push to a data warehouse or CRM
  • Call analytics — process inbound or outbound call recordings, extract compliance flags, action items, or sentiment scores
  • Survey response processing — transcribe and code audio or video survey responses at scale without manual review

Technical setup

Authenticate with your Speak AI API key, submit files or URLs to the transcription endpoint, configure webhooks to receive completed results, and define custom extraction prompts for your specific data fields. Full documentation at speakai.co/developers.

Build data collection agents on Speak AI — get your API key.

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