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Fieldbase
Laptop Study
Brand Tracker Q2
Valid
Screener
S
QAgenumber
S
QGendersingle
S
QBrandsingle
M
QFeaturesmulti
L
QZiplookup
ƒ
sScreenoutscript
ƒ
sQuotascript
Main survey
M
QAwarenessmulti
G
QSatisfgridSingle
G
QImportancegridSingle
◈
QCbccbc
N
QPricenumber
⊞
QBrandSortcardSort
S
QNPSsingle
S
QRecommendsingle
T
QFeedbackopen
ƒ
sComputescript
Demographics
S
QIncomesingle
S
QEducationsingle
N
QHouseholdnumber
Close
i
QThankYouinfo
survey.yaml
lists.yaml
scripts.py
 
 
  
  
 
  
 
 Progress 11% 
 
 
 
 
 
 
  
  
 
← Back
 
Next →
 
Q1 of 9
 
 
 
 
 
 
 
    
 
 
 
Start your survey
 
        Upload
 a spec.
Get a working survey.

 

Drop the Word doc or paste the questionnaire. Fieldbase reads the
        question text, options, programming notes, and routing instructions.

 
 
  
  
 
 
 
 
 Programming_Spec_v2.1.docx 
 
 
Brand Tracking Study — Programming Spec
 
 
Q1. Which of the following brands have you used in the past 3 months?
 
[PN: MULTIPLE RESPONSE. RANDOMIZE 1-5. ANCHOR 99. EXCLUSIVE.]
 
 
1. Dell
 
2. HP
 
3. Lenovo
 
4. Apple
 
5. Asus
 
99. None of these
 
 
[IF CODE 99, TERMINATE. OTHERWISE CONTINUE.]
 
 
 
Q2. Which one of these brands do you use most often?
 
[PN: SINGLE RESPONSE. SHOW ONLY BRANDS FROM Q1.]
 
 
 
Q3. Thinking about [PIPE BRAND FROM Q2], how satisfied are you with each of the following?
 
[PN: GRID SINGLE. ONE RESPONSE PER ROW. SCALE LOW TO HIGH. RANDOMIZE ROWS.]
 
Rows:
 
 
1. Overall quality
 
2. Value for money
 
3. Ease of use
 
4. Availability
 
 
Scale:
 
 
1. Very dissatisfied
 
2. Somewhat dissatisfied
 
3. Neither satisfied nor dissatisfied
 
4. Somewhat satisfied
 
5. Very satisfied
 
 
 
 
Q4. What is the main reason for your rating?
 
[PN: OPEN END. ASK IF ANY CODE 1 OR 2 AT Q3.]
 
 
 
Q5. How likely are you to continue using [PIPE FROM Q2]?
 
[PN: SINGLE RESPONSE.]
 
 
 
  
 
 
 
→
 
 
 
  
 
 
 
 
 
Fieldbase
 
5 questions generated
 
 
  
 
 
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- qid: QBrandsUsed
  type: multi
  text: |
    Which brands have you used
    in the past 3 months?
  order: random
  options:
    1: Dell
    2: HP
    3: Lenovo
    4: Apple
    5: Asus
    99:
      label: None of these
      exclusive: True
 
# Screenout
- script: |
    if QBrandsUsed.selected.any(99):
        endSurvey()
 
- qid: QMainBrand
  type: single
  options.from: QBrandsUsed.selected
  if: QBrandsUsed.selected.size > 1
 
- qid: QSatisfaction
  type: gridSingle
  rows:  # 4 rows
  scale: # 1-5 satisfaction
 
 
  
 
 
 
       
 Brand Study 
 
 
 
 
Q1 of 5
 
Which brands have you used in the past 3 months?
 
Select all that apply
 
1
✓
Dell
 
2
HP
 
3
✓
Lenovo
 
4
✓
Apple
 
5
Asus
 
99
None of these
 
 
 
← Back
 
Next →
 Q1 of 5 
 
 
 
 
  
 
 
60%
 
workload reduction
on average per study
 
 
 
5x
 
faster from spec
to first preview
 
 
 
0
 
syntax errors
validated at generation
 
 
  
 
 
      
 
 
Your data is not used for AI training.
 
Uploaded questionnaires are processed securely. Temporary processing data is deleted within 7 days. Your survey content remains confidential.
 
 
 
 
 
 
   
 
 
 
 
AI assist in the editor
 
Select a block.
Use ⬡Inline assist to program.
 

Pick a section, describe the change, see what the AI suggests
          before anything happens. Accept, refine, or skip — you're in
          charge every step.

 
 
 
 
 
 
 
 
 
 
Fieldbase
 
 
 
 
 
 
survey.yaml
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
◇
 
No question selected
 
Select a question to preview
 
 
 
 
← Back
 
Next →
 
Q1 of 5
 
 
 
 
 
 
 
 
    
 
 
 
Author in any language
 
Your team's language.
Your team's surveys.
 

Write content and prompt the AI in any language your team works in.
        Keywords and identifiers stay English — so exports drop straight
        into SPSS, Stata, or Excel without renaming.

 
 
  
 
 EN English 
 
- qid: QBrandsUsed
  type: multi
  text: |
    Which brands have you used
    in the past 3 months?
  options:
    1: Dell
    2: HP
    3: Apple
    99: None of these
 
  
 
 FR Français 
 
- qid: QBrandsUsed
  type: multi
  text: |
    Quelles marques avez-vous
    utilisées au cours des
    3 derniers mois ?
  options:
    1: Dell
    2: HP
    3: Apple
    99: Aucune de celles-ci
 
  
 
 ES Español 
 
- qid: QBrandsUsed
  type: multi
  text: |
    ¿Qué marcas ha usado
    en los últimos
    3 meses?
  options:
    1: Dell
    2: HP
    3: Apple
    99: Ninguna de estas
 
 
  
 
 ⬡
The AI prompt works in your language too

 
 
 EN Read the screener section, frame and program as per the specs 
 
 FR Lis la section screener, structure et programme selon les specs 
 
 ES Lee la sección del screener, estructura y programa según los specs 
 
 DE Lies den Screener-Abschnitt, strukturiere und programmiere gemäß den Specs 
 
 
  
 What stays English: structural keywords
      (qid, type, options),
      identifiers (QBrandsUsed), and script variable names.
      They become column names in your exports — keeping them ASCII means
      SPSS, Stata, and Excel handle the file without renaming or transliteration.

 
 
  
 
 
 
Results & export
 
See your results instantly.
Export clean data.
 

Auto-generated topline reports with cross-tabs and filters.
        Deterministic column naming across every export — no cleanup,
        no surprises between waves.

 
 
 
  
 
 
 
 
 
 
 
Fieldbase
 
Auto Topline
 
 
 
 
QAgenumber
 
QGendersingle
 
QBrandsUsedmulti
 
QMainBrandsingle
 
QSatisfactiongridSingle
 
QNPSnps
 
QRankrank
 
QSpendnumber
 
QFeedbackopen
 
 
  
 Cuts Total · n=10,847 Female · n=4,831 18–34 · n=3,210  ⬡ Share cut 
 
 Filter QGender = Female✕ + Add filter Save as cut… 
  
Brand awareness — past 3 months
 
QBrandsUsed · multi · n=10,847
 
 Dell 
64%
 6,942 
 
 Apple 
58%
 6,291 
 
 HP 
38%
 4,122 
 
 Lenovo 
31%
 3,363 
 
 Asus 
21%
 2,278 
 
  
Satisfaction — mean scores
 
QSatisfaction · gridSingle · scale 1–5
 
 
TotalDellAppleHPLenovo
  
Quality3.83.44.53.23.6
 
Value3.23.62.13.83.7
 
Ease of use3.93.54.63.33.4
 
Availability3.74.23.83.92.8
  
 
  
Likelihood to recommend — NPS
 
QNPS · nps · n=10,847
 
 
 +42 NPS 
 
 
 
18%
 
22%
 
60%
 
 
 Detractors Passives Promoters 
 
 
 
 
 
  
 
 
 
 
 
 
 
Fieldbase
 
Data Export
 
 
 
 
Brand Tracker Q1 2026
 
10,847 respondents · Export data
 
 
 
SPSS
.sav
 
CSV
.csv
 
Excel
.xlsx
 
ASCII
.dat
 
 
Export shape
 
 
 
Standard
 
One row per respondent. Flat data.
 
 
 
Long / Modeling
 
One row per task. For conjoint.
 
 
 
Column preview
 
 
ColumnSourceType
 
QBrandsUsed_1Dell0/1
 
QBrandsUsed_2HP0/1
 
QBrandsUsed_3Lenovo0/1
 
QMainBrandMost used brandint
 
QSatisfr1Quality1–5
 
QSatisfr2Value1–5
 
QSatisfr3Ease of use1–5
 
QSatisfr4Availability1–5
 
QNPSNPS score0–10
 
QFeedbackOpen textstr
 
 
 
↓ Export .sav
 42 columns · 10,847 rows 
 
 
 
 
 
 
   
 
 
 
What you get
 
Everything an AI survey platform
should have been.
 
No legacy baggage. Each capability of this AI-assisted survey software is built for the way modern survey programmers actually work — fast, code-first, AI-augmented.
 
 
  
 
     
 
Code-first authoring
 
Clean, readable syntax for every question type and every routing rule. Autocomplete and live validation from a single contract — every rule defined once, enforced everywhere.
 single · multi · grid · cbc · maxdiff · cardSort 
  
 
       
 
Conjoint and MaxDiff included
 
CBC with three response modes — choice, rating, ranking. Upload a design file, reference it in a single line. Utility-ready exports drop straight into your analysis tool.
 design: project://laptop_cbc_v2 
  
 
     
 
Quotas that hold
 
Cell locking on every completion. No over-quota, no soft counts, no manual reconciliation — even under heavy concurrent traffic. The numbers you set are the numbers you get.
 quota("AgeGender", 1).isFull() 
  
 
     
 
Live respondent preview
 
Every change updates the preview as you type. Branching, loops, piped values, hidden logic — see exactly what a respondent sees, without deploying. Test routing in seconds.
 
  
 
      
 
Analysis-ready exports
 
Deterministic column naming — every export column is mechanically derived from the question structure. No surprises, no manual mapping, no cleanup before analysis.
 QBudgetr1c1 · QBudgetr1c2 · QBudgetr2c1 
  
 
         
 
Theme framework
 
22 professional themes with seven style variants. Every visual property is a token — change how a survey looks without touching what it does. Respondent-facing UI that holds up to client review.
 
 
 
 
  
 
 
 
 
Built for both sides
 
Programmers ship faster.
Researchers see it work.
 

Code is what AI is genuinely good at. So programmers get a real
          editor — not a drag-and-drop puzzle that hides the logic.
          Alongside it, a live respondent view that researchers and
          stakeholders can read at a glance, no training required.

 
 
 
  
 
15+ question types including gridNumber, mixGrid, cardSort (open/closed/hybrid), lookup with cascading, and rank.
 
 
 
  
 
60 skin variants — radios and cards, sliders and dials, emoji scales and bipolar sliders, podium and tap-to-rank. Same question types, different respondent feel.
 
 
 
  
 
Script blocks with familiar Python-like logic — if/elif/else branching, accessor-driven routing, computed hidden values.
 
 
 
  
 
Design asset manager — upload CBC and MaxDiff design files, validate structure, preview tasks, copy a one-line reference into the survey.
 
 
 
  
 
Segmentation and complex routing tested on real production projects — not theoretical, not demo-only.
 
 
 
 
 
    
 
 
 
survey.yaml
 
# Closed card sort
- qid: QBrandSort
  type: cardSort
  style: closedSort
  text: Sort these brands
        into categories.
  options:
    1: Target
    2: Walmart
    3: Costco
  slots:
    1: Premium
    2: Value
    3: Not sure
 
 
 
Live preview
 
 
Sort these brands into categories.
 
 Target Walmart Costco 
 
 
 
Premium
 
Target
 
 
 
Value
 
WalmartCostco
 
 
 
Not sure
 
 
 
 
 
 
 
 
 
 
  
 
 
 
Your data, your terms
 
Built for the way research data has to be handled.
 
 
 
 
      
 
Your data isn't used to train AI
 

Survey content, respondent answers, and the code you author are
          never added to AI training data. Your work — and your clients'
          data — stays your own.

 
 
 
     
 
ISO 27001 certified, GDPR aligned
 

Certified under ISO 27001 and aligned with GDPR through our parent
          company, Inginit. Data is encrypted in transit and at rest, with
          tenant isolation by project. Full details on the
Inginit trust center.

 
 
 
      
 
You own and control your data
 

Export the full project — code, design files, responses — at any
          time. Delete on request, including from backups within standard
          retention windows. Any operational metrics we keep for product
          improvement are anonymised aggregates that can't be traced back
          to you.

 
 
 

Fieldbase is built by Inginit. For certifications, sub-processors,
      retention windows, and to request a DPA, see the
Inginit trust center.

 
 
   
  
 
 
Now accepting early access

 
Write prompts to code.
 

The AI-native survey platform. Describe what to program. Fieldbase
      writes production-ready survey code.

 
Request early access →
 
  
 
 
 ⬡ Inline assist ⌘K 
 
 

Program Q1 to Q5 with skip logic and piping.
          Read [PN] notes for routing, randomisation,
          exclusive codes, and terminates.

 
 ↵ Send Esc Cancel 
 
 
 
→ program Q3 as gridSingle, read the PN for scale and rows
 
→ add screenout script if code 99 at Q1, terminate
 
→ pipe Q2 selected label into Q3 text, add probe if low rated
 
 
 
  
 
Any language
 
Your team's language.
Your team's surveys.
 
Author content and prompt the AI in any language. Keywords and identifiers stay English so exports drop into SPSS, Stata, or Excel without renaming.
 
 
  
 
 EN English 
 
- qid: QBrandsUsed
  type: multi
  text: |
    Which brands have you
    used in the past 3 months?
  options:
    1: Dell
    2: HP
    99: None of these
 
  
 
 FR Français 
 
- qid: QBrandsUsed
  type: multi
  text: |
    Quelles marques avez-vous
    utilisées au cours des
    3 derniers mois ?
  options:
    1: Dell
    2: HP
    99: Aucune de celles-ci
 
 
 
 
The AI prompt works in your language too:
 
 FR Lis la section screener, structure et programme selon les specs 
 
 ES Lee la sección del screener, estructura y programa según los specs 
 
 DE Lies den Screener-Abschnitt, strukturiere und programmiere gemäß den Specs 
 
  
 
The platform
 
A real IDE for surveys.
 
Question tree, code editor, live preview — three panels, always in sync.
 
 
  
 
    survey.yaml 
 
 
 
Screener
 
SQBrand
 
MQFeatures
 
ƒsQuota
 
Main
 
GQSatisf
 
◈QCbc
 
⊞QSort
 
 
- qid: QSatisfaction
  type: gridSingle
  text: |
    How satisfied with
    {{QMainBrand
    .selectedLabel}}?
  rows:
    1: Overall quality
    2: Value for money
    3: Ease of use
  scale:
    1: V. dissatisfied
    5: V. satisfied
 
 
  
 
 
   
 
How satisfied are you with Apple?
 
 ✓ Overall quality 4 
 
  Value for money — 
 
  Ease of use — 
 
  Availability — 
 
 ← Back Next → 
 
 
 
  
 
Start your survey
 
Upload a spec.
Get a working survey.
 
Drop a Word or PDF. The AI reads every question, code, and routing note.
 
 
 
 
    Spec_v2.1.docx 
 
 
Q1. Which brands have you used in the past 3 months?
 
[PN: MULTI. RANDOMIZE. EXCLUSIVE 99.]
 
1. Dell
 
2. HP
 
3. Apple
 
99. None of these
 
[IF 99, TERMINATE]
 
 
 
↓
 
 
    survey.yaml 
 
- qid: QBrandsUsed  type: multi  order: random  options:    1: Dell    2: HP    3: Apple    99:      exclusive: True- script: |    if QBrandsUsed.selected.any(99):        endSurvey()
 
 
  
 
 
60%
 
workload
reduction
 
 
 
5×
 
spec to first
preview
 
 
 
0
 
syntax errors
at generation
 
 
  
 
Results & export
 
See results.
Export clean data.
 
Auto topline with filters. Deterministic columns across every wave.
 
 
  
 
    Auto Topline 
 
 
Brand awareness
 
 Dell 
64%
 6,942 
 
 Apple 
58%
 6,291 
 
 HP 
38%
 4,122 
 
 Lenovo 
31%
 3,363 
 
 
  
 
    Data Export 
 
 
 SPSS CSV Excel ASCII 
 
QBrandsUsed_1Dell0/1
 
QMainBrandMost usedint
 
QSatisfr1Quality1–5
 
QSatisfr2Value1–5
 
QFeedbackOpen textstr
 
 
 
  
 
What you get
 
Built for survey programmers.
 
 
 
 
✎
 
Code-first authoring
 
Clean syntax with autocomplete and live validation.
 
 
 
◈
 
Conjoint included
 
CBC and MaxDiff built in. One platform.
 
 
 
⬡
 
Quotas that hold
 
Cell locking. No over-quota under load.
 
 
 
▤
 
Clean exports
 
Deterministic columns. SPSS, CSV, Excel.
 
 
 
◐
 
22 themes
 
Professional respondent UI. Token-based.
 
 
 
ƒ
 
Script blocks
 
Python-like routing, piping, computed values.
 
 
  
 
      
 
 
Your data stays yours.
 
Not used for AI training. ISO 27001 + GDPR aligned via Inginit. Export anytime.
 
 
 
  
 
 
 
Early access
 
Get in early.
 

Fieldbase is in private beta. Email us with a few words about your
        team and the kind of research you run — we'll set up a call and get
        you onto the platform.

  Email [email protected]   

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