Field data infrastructure

Hard-to-reach data, made usable.

We collect data from the field through everyday messaging apps — WhatsApp, Telegram, Messenger, and the rest — structure it, and deliver it ready for your dashboards, AI models, or operations stack.

Talk to us → See how it works ↓

GoNsave Field

412 contributors online

M. Rahman · Loan officer

Visit complete, photos attached.

09:14 ✓✓

📷 Site photo

Field agent · BD-04

▶ 0:24

Enumerator · MX-12

Store audit done, 14 SKUs captured.

09:21

97.7% auto-structured

9,612 records this week

5+ years operating · 30M+ structured data points · Active across Southeast Asia and Latin America

What we do

Three capabilities. One job.

We sit between hard-to-digitize environments and the teams that need to act on what is happening there. Engage one capability or all three.

01 · Reach

Reach the field.

We reach the people in the field — gig workers, loan officers, enumerators, store agents — through everyday messaging apps. No new app to download. No training. Photos, voice notes, text, payslips, all of it lands in one structured record.

WhatsApp Telegram Messenger SMS

9:41●●●

GoNsave Field

WhatsApp · receiving

M. Rahman · Loan officer

Visit complete at borrower #4218.

09:14 ✓✓

📄 Payslip / receipt

Rider · SG-12 · auto-OCR

▶ 0:24

Enumerator · MX-12

Store audit done, 14 SKUs captured 📦

09:21

🏪 Storefront audit

One number,
any contributor

02 · Structure

Turn messy signals into clean data.

Field submissions are inconsistent by nature. We standardize them. Photos get classified. Voice gets transcribed. Free text gets parsed. The output is a clean, queryable dataset — not raw exports for your team to clean up later.

Image classification Voice-to-text Entity parsing Reconciliation

📷

IMG_4218.jpg
payslip · 1.2 MB

RAW

🎙️

voice-0024.m4a
0:24 · BN

RAW

📝

"price up at zone 4…"
free text · ES

RAW

📍

geo: 23.8103, 90.4125
implicit

RAW

submissions

97.7% valid

Type / Source

Geo

Status

04218

Payslip
Image · WA

SG-12

04219

Voice note
Audio · WA

BD-04

04220

Price obs.
Text · TG

MX-12

Review

04221

Store audit
Image · WA

MX-12

03 · Deliver

Send it where your team works.

We hand the data off in whatever shape you need: a dashboard, an API, a CSV into your CRM, a feed into your AI pipeline, or back to your operations team for next-day decisions.

Dashboards REST API CSV / CRM AI pipelines

Structured

GoNsave Layer

v2.real-time

📊

Dashboards

BI · custom · embed

🔌

REST API

/v1/records

📄

CSV / CRM

scheduled exports

🧠

AI pipeline

training · inference

Who we work with

Built for teams that depend on field signal.

Same capability stack, four very different applications. Pick the one that sounds like your team.

Gig & delivery platforms

Last-mile pay-out data, competitor incentive tracking, rider supply signals. Built for ops teams making real-time pricing and retention decisions.

Market research agencies

Field inputs at scale, without enumerator overhead. Pricing studies, retail audits, behavioral panels, and customer-facing brands collecting structured product feedback.

Microfinance institutions

Loan officer activity, post-disbursement borrower signals, donor reporting. We surface the field reality before it turns into a default or operational chaos.

NGOs & development organizations

M&E pipelines, program monitoring, donor-ready outcome data. We turn enumerator chaos into reportable evidence.

Why GoNsave

We are not a tool you adopt. We are the layer in between.

We do not sell software you have to deploy. We do not sell reports that sit in a folder. We sit between the field and the team that needs to act — and we make sure the data gets there in a shape they can use.

Zero onboarding for the field.

Workers use the messaging apps they already have. No new app, no training, no IT cycle.

Built for low-digitization environments.

We work where connectivity is patchy, devices are mixed, and rigid mobile apps fail.

Structured for action, not archives.

Every dataset comes out clean and queryable — ready for dashboards, ML, or your existing tools.

In practice

Real numbers from a live deployment.

Lead engagement: 4,500 gig-worker users active across three markets.

4,500

Active gig-worker users across three markets in our flagship deployment.

+5–6%

Average earnings uplift for users engaged through the GoNsave layer.

70%

Retention at 12+ months — well above category benchmarks.

Delivery & gig

foodpanda Uber Rappi DiDi Grab ShopeeFood iFood KEETA Deliveroo Lalamove Gojek 99

Microfinance & research

BRAC Field Market Research MoneyHero SingSaver Money101 MoneyMax Seedly Creatory

See a live case →

Talk to us

Tell us what you can't see today.
We'll tell you whether we can help.

✉ Email 📅 Calendar link 💬 WhatsApp

Talk to us → Book a call

← Home What we do

Capabilities.

Three capabilities. Engaged together or individually. Most clients start with Reach and grow into Structure and Deliver as the data volume builds.

01 · Reach

A messaging-first capture layer.

For organizations whose data sources are people in the field, not databases.

How it works: Workers, agents, officers or enumerators send what they have — photos, voice notes, payslips, text — to a number they already use, on WhatsApp, Telegram, Messenger, or whatever messaging app is dominant in their market. We bundle it into a structured record on our side. They never download anything new.
Where it shines: Last-mile pay-out tracking, post-disbursement borrower monitoring, retail audit, M&E enumerator workflows, competitor pricing capture in markets where digital sources are incomplete.

9:41●●●

GoNsave Field

receiving · 412 contributors

M. Rahman · Loan officer

Visit complete at borrower #4218.

09:14 ✓✓

📄 Payslip / receipt

Rider · SG-12 · auto-OCR

▶ 0:24

Enumerator · MX-12

Store audit done, 14 SKUs captured 📦

09:21

🏪 Storefront audit

02 · Structure

Turn inconsistent submissions into clean, queryable data.

How it works: We classify images, transcribe voice, parse free text, and reconcile data across submissions. Output is a structured dataset — auditable, sortable, exportable.
Where it shines: M&E reporting cycles, donor-ready outcome datasets, retail benchmarking, fraud and quality control on field workflows.

📷

IMG_4218.jpg
payslip · 1.2 MB

RAW

🎙️

voice-0024.m4a
0:24 · BN

RAW

📝

"price up at zone 4…"
free text · ES

RAW

📍

geo: 23.8103, 90.4125
implicit

RAW

submissions

97.7% valid

Type / Source

Geo

Status

04218

Payslip
Image · WA

SG-12

04219

Voice note
Audio · WA

BD-04

04220

Price obs.
Text · TG

MX-12

Review

04221

Store audit
Image · WA

MX-12

03 · Deliver

The hand-off layer. Data lands where the team already works.

How it works: We push to dashboards (BI, custom, or your own), to APIs, to internal tools (CRMs, ops platforms, ticketing), to AI pipelines (training data, real-time inference), or as flat exports.
Where it shines: Operations teams making same-day pricing decisions, AI teams that need clean training data from real environments, NGOs preparing donor reports, MFIs feeding repayment risk signals into credit decisions.

Structured

GoNsave Layer

v2.real-time

📊

Dashboards

BI · custom · embed

🔌

REST API

/v1/records

📄

CSV / CRM

scheduled exports

🧠

AI pipeline

training · inference

Talk to us

See how this fits your use case.

Talk to us → Who we work with

← Home Who we work with

Who we work with.

Each audience has the same structure: their reality, what we capture, and one proof point. Same capability stack underneath — very different applications on top.

Audience 01

Gig & delivery platforms.

The reality: Ops teams pay riders to show up and ride. They lose drivers every week without fully knowing why. Competitor pay-outs are invisible. Internal data is messy and slow.
What we capture: Pay slips, competitor incentive screenshots, rider supply signals, fulfillment data, peak-hour pay rates.
Proof: A live deployment with 4,500 gig-worker users across three markets, lifting earnings 5–6% and holding 70% retention at 12+ months.

Pricing & retention console

3 markets · live

4,500

Active riders

+5.4%

Earnings vs. baseline

70%

12-mo retention

Cost per order · weekly

Motorcycle E-bike Bicycle

Competitor incentive captures

📡

Rappi · Guadalajara

$1,650 surge bonus · Thu

📡

Uber Eats · MX

Día del Niño 30 Apr · Little Caesars

📡

DiDi Food · Bogotá

2x quest pay 11am–2pm

14m

Audience 02

Microfinance institutions.

The reality: Default risk hits the credit team late. Loan officer productivity stays flat while the portfolio doubles. Every donor cycle burns weeks of M&E.
What we capture: Loan officer field activity, borrower income volatility signals, repayment behavior, donor-reportable outcome data.
Proof: Working with a leading Southeast Asian MFI to surface post-disbursement income volatility before it turns into a default.

Portfolio health · Bangladesh

Updated 11:02

12,418

Active borrowers

3.8%

PAR 30 days

94%

Visit verified

Repayment rate · monthly

On-time repayment 96.2% · target 95%

Field activity · today

✓

Karim Uddin · Sylhet

Visit verified · on track

09:14

Nasrin Akter · Rajshahi

Volatility signal · 14d income drop

10:02

✓

Fatema Begum · Chittagong

Follow-up · 4 photos

10:48

Audience 03

Market research agencies.

The reality: Pricing studies, retail audits, and behavioral panels still depend on enumerators with clipboards. Quality is uneven. Turnaround is slow.
What we capture: Structured field inputs from enumerators, store agents, or end-customers themselves — photos, voice, text, payslips, pricing observations.
Proof: Multi-country pricing and incentive coverage across delivery, retail, and consumer goods.

Retail price study · LATAM

Q2 wave · in progress

1,284

Stores covered

9,612

SKU observations

92%

Coverage target

Avg. SKU price · 5 markets

Recent enumerator submissions

📸

Tienda 7-Eleven · CDMX

14 SKUs · price + shelf photo

11:21

🎙️

OXXO Mérida

Voice note · stockout flag

11:08

📸

D1 Bogotá

22 SKUs · promo display captured

10:54

Audience 04

NGOs & development organizations.

The reality: M&E teams re-collect the same field data every donor cycle. Programs run in places where rigid apps fail. Donor reports take weeks to compile.
What we capture: Enumerator data, program participant signals, field activity, outcome metrics.
Proof: Field-program data infrastructure compatible with most donor reporting templates.

M&E outcomes · Program K

Donor-ready · Q3

8,420

Participants

+11%

Outcome lift

100%

Donor template ready

Outcome indicator over time

Enumerator field activity

📝

Cohort 12 · West region

240 baseline surveys submitted

13:01

📸

Sentinel site #4

Photo evidence · 6 attached

12:42

📄

Donor export

Compatible with 5 standard templates

12:18

Your team

Sound like your team?

Talk to us → See capabilities

← Home About

How we work.

From a WhatsApp thread in Manila to a verified borrower record — the same trip, in minutes instead of weeks.

GoNsave was built to solve one stubborn problem. Organizations operating in the field — microfinance institutions, delivery platforms, research agencies, NGOs — need data from the field every week. Most of the time, it doesn't reach them in time, or doesn't reach them at all. Not because it isn't there. The data exists. People are doing the work. The signal is being generated. It just gets trapped in messaging threads, paper notes, and field officer memory.

We unblock that flow. Workers in the field talk to us through the messaging apps they already use every day — WhatsApp, Telegram, Messenger, and the rest. On our side, what arrives as photos, voice notes, payslips, and free text gets standardized, structured, and routed to wherever the team needs it: a dashboard, an API, a CSV, an AI pipeline, an internal ops tool.

Five years in, that pattern has carried us through delivery platforms across Southeast Asia and Latin America, microfinance institutions, market research agencies, and field NGOs. The use cases vary. The capability is the same.

Where we work

The regions where field intelligence matters most.

We operate across Southeast Asia and Latin America — regions where the field signal is rich, the digital trail is thin, and the team that needs the data is rarely in the same building as the people generating it.

Southeast Asia: Singapore · Malaysia · Indonesia · Philippines · Bangladesh · Vietnam · Thailand
Latin America: Mexico · Colombia · Brazil · Chile · Peru · Argentina