EdZola
Livelihoods sector playbook

Livelihoods programmes change households, enterprises, farms and markets. Your system should see all four.

A graduation programme, an SHG federation, a producer organisation, migrant support and an artisan value chain do not run on the same workflow. This playbook helps you connect the people, seasons, services, assets and market relationships that make each model work.

Works offline, in the local languageBuilt on ZohoYour data stays yours
S
Sabita MajhiLV-0386 · Household of 5 · Kandhamal
FULL JOURNEY
Wealth_ranking_Tikabali_block_v2.xlsxVillage 7
Sabita M.Cat Dnot in SHG??
Sabita MajhiCat Cdup?
01 · Found and verified
Ultra-poor, verified 14 MarJoined Maa Tarini SHG, Tikabali
Done
Sabita plan: 2 goats
saving 20/wk? books ok?
02 · Ready for the asset
Business plan approved: goat rearingSaved ₹20 a week for 12 weeks
Ready
SHG meeting register · Aug · pg 11
Sabita M.PPPAAA
03 · This month
Missed three SHG meetings in a rowPPPAAA
Flagged
Action today
Mentor visit booked with RupaBefore the second goat is sold
Due today
PDF
Graduation_report_cohort2_FINAL.pdfDue Friday · asset count doesn't match
04 · Graduation
Assessed by a second officerCriteria tracked at every visit until then
Mar 2027

Five places, one household. Nobody can see the asset is about to go.

Six common operating models

Find the model closest to how your work actually runs.

Many organisations combine two or three. Start with the closest match, then borrow only the parts that solve a real programme problem.

Model 01

Graduation and household resilience

Households receive support in stages, such as consumption help, assets, coaching and linkages.

Usually organised around
Households, members, vulnerability, assets, visits, milestones and graduation decisions.
Watch for
Graduation is a reasoned review, not a single score or one-time income check.
Start with
Household plan, asset, coaching visit and milestone.
Model 02

SHG and federation strengthening

Women organise, save, borrow, govern groups and connect to institutions.

Usually organised around
Members, groups, meetings, savings, loans, governance and federation levels.
Watch for
Financial records and institution quality need to be visible together.
Start with
Member, group, meeting, savings and governance check.
Model 03

Farmer or producer organisation

Farmers pool inputs, advice, produce and market access through a collective.

Usually organised around
Farmers, plots, crops, seasons, lots, services, buyers and payments.
Watch for
The same farmer may have several plots and crops across changing seasons.
Start with
Farmer, plot, season, service and produce lot.
Model 04

Migrant-worker services

Workers receive information, placement help, documents, entitlements or crisis support.

Usually organised around
Workers, households, source/destination, jobs, documents, cases and referrals.
Watch for
Workers move and change numbers, so keep backup contacts and records that travel with them.
Start with
Worker profile, journey, service request and referral.
Model 05

Artisan and value-chain enterprise

Producers move from design or production through quality, orders, fulfilment and payment.

Usually organised around
Artisans, skills, products, batches, orders, inventory, buyers and payments.
Watch for
Income depends on fair, timely orders, not only training completed.
Start with
Artisan, product, order, delivery and payment.
Model 06

Digital or AI-enabled extension

Advisory reaches farmers or entrepreneurs through field staff, voice, chat or content.

Usually organised around
Users, questions, advisories, content versions, adoption and escalation.
Watch for
A message sent is not advice used; unsure answers go to a person.
Start with
User, advisory, content version and follow-up.

Not sure which one you are? Bring one tracker and describe a normal week. The model usually becomes clear quickly.

Sound familiar?

Your programme runs on good people and scattered data.

These come up in almost every first conversation we have with livelihoods teams.

"We found out the goats were sold at the graduation survey."

Visit notes sit in mentors' diaries, so a sold asset shows up months later as a number.

Programme manager
"The group register always looks perfect."

The CRP fills it in after the meeting. Nobody can match the book to the cash in hand.

Field officer
"Purchase, handover and payment live in three files."

The week before audit goes on matching vendor bills to handover photos and bank references.

Finance officer
"We report jobs created. Nobody can show the enterprise is still open."

The count comes from the loan file. A year on, we don't know which businesses are trading or who works in them.

Head of MEL
Real work, not only illustrations

What we've actually built.

We haven't published an SHG or graduation case study yet. These are the real systems a livelihoods build rests on: eligibility rules and scheme tracking for families, and approvals at every level for women's skilling.

Also worked with:Tapasya Foundation

Recognise your programme? Tell us where the work gets stuck and we'll suggest a useful first version.

One worked example · Household graduation and enterprise support
Follow one programme journey

For a graduation-style programme, four questions show whether support is becoming a durable livelihood.

This is one operating model, not a template for every livelihoods organisation. Use it to see how everyday work, follow-up and management decisions can connect.

Stage 01 · Find the poorest

Which households did the village list miss?

The poorest families are often left out of village meetings, SHGs and government lists. Wealth ranking with the community and a verification visit find them, before any money is spent.

Your team does

  • CRPs record wealth ranking on a shared phone after the village meeting, offline, with a photo of the social map.
  • Field officers run the PPI or Stoplight survey on home visits, in the local language.
  • A programme officer verifies on a separate form, without seeing the first answers.

The system does on its own

  • Where the ranking and the score disagree, a verification visit comes first.
  • A duplicate check on household head, village and location stops a family enrolling twice.
  • Once a household is confirmed, the woman is linked to her group on her record.

Where AI helps at this stage

  • Voice notes from ranking meetings sorted into household categories
  • Villages where ranking, score and visit disagree, shown side by side
  • The PPI survey adapted into Hindi, Odia, Tamil or Swahili with local examples
Programme officer · TargetingLivelihoods & Economic Empowerment · Stage 01
EXAMPLE DATA
Households ranked1,146Across 18 villages
Shortlisted312Ultra-poor and poor
Verified in 30 days74%231 of 312
Ranking and score disagree38Visit first
IDHouseholdVillageStatus
LV-0386Sabita MajhiTikabaliVerified
LV-0391Jharana PradhanSarangagarhDisagreement, visit due
LV-0402Mamata NayakBudhediVerification due
LV-0377Kuni DigalTikabaliVerified
Do nextHalf the disagreements come from two villages where ranking ran as one big meeting. Rerun it hamlet by hamlet, with an officer from outside the village.
Build your sector playbook

Choose the parts your programme needs, not a giant system all at once.

Choose the parts that match where your programme intervenes: household, institution, production, enterprise, mobility or market. Connect only what your team can act on.

Households, groups and institutions

Connect individual participants with households, SHGs, federations or producer groups.

Enterprise and household finance

Follow capital, loans, sales, expenses, cash flow and income without pretending to be precise.

Farm seasons and advisory

Plan crops, visits, inputs, practices and harvest evidence around the agricultural calendar.

Assets, loans and coaching

Track transfer, use, maintenance, repayment and the next coaching action.

Orders and market linkage

Move from buyer demand to production, quality, fulfilment, invoices and payments.

Migration and entitlements

Support portable documents, services, referrals and cases across source and destination.

Questions the system should answer

Useful on an ordinary workday

  • Which household, group, plot or enterprise is this about?
  • What support was promised and delivered?
  • What changed in practice, production or cash flow?
  • What risk or market block needs action now?
Measures worth seeing together

Delivery beside outcomes

  • Income and cash flow trends
  • Asset use, repayment and resilience
  • Practice adoption, production and yield
  • Order fulfilment and payment time
  • Entitlement access and safe migration
Keep people in charge

Boundaries to agree before build

  • Field teams validate context behind financial numbers
  • Advice states its source and uncertainty
  • Credit or graduation decisions are never automatic
  • Sensitive migration and debt data has tight access
How we build it · the MAP approach

We fix the data first. AI comes last, and it's better for it.

Most AI projects stall because the records underneath are messy. So we work in a fixed order.

M
Model

Start with the decisions you need to make.

Every field has to earn its place by helping someone decide something. If nobody uses it, we don't collect it.

  • Connected records for household, member, SHG, federation, enterprise, loan or asset, mentor visit and graduation or enterprise check
  • SHG, NRLM (LokOS) or VSLA group IDs and bank linkage details stored alongside, so you can match against government and bank records
  • Graduation criteria and income indicators agreed with your funder before the first form goes out
A
Align

Fit it to how your field team already works.

A system people avoid is worse than a spreadsheet. We design around shared phones, patchy signal and WhatsApp.

  • Offline forms in Odia, Hindi, Bengali or your language
  • A different screen for the CRP, the mentor, finance and the manager
  • Visit reminders and handover checks that fire on their own
  • Groups get a monthly savings statement, on WhatsApp or on paper
P
Power with AI

Then add AI where it saves real hours.

On clean, connected records, AI can read paperwork, draft reports and flag who needs attention. A person always checks.

  • Six tested use cases, picked for the hours they save
  • A human check written into every one
  • Clear lines on where AI doesn't belong

Where you start depends on where you are. If your records are already in good shape, we can move to AI quickly. If they're in notebooks, we start there, and we don't start from zero.

Offline forms, local languages, links to government IDs and AI features are set up to suit each programme's needs and consent rules, so not every build includes all of them.

Where AI helps

Six places AI earns its keep.

We picked the jobs that save livelihoods teams the most hours. Each starts with a problem we hear often, and each keeps a person in charge of the decision. How we think about AI →

Collect fasterBuild the habit

“We can't have staff typing lakhs of SHG registers every month.”

Reads a photo of the group's paper register into savings, internal loans, bank loans and repayments.

Your team still decides: The CRP confirms totals with the group leader before submitting.

Collect fasterCoach to graduation

“Our field officers' visit notes stay in diaries, so problems show up in the annual survey.”

Turns a field officer's or mentor's voice note into savings, income, enterprise status and agreed actions.

Your team still decides: The officer confirms the key numbers before saving.

Work smarterTrack the asset

“Before every audit we spend a week matching vendor bills, loan disbursals and handover photos.”

Matches purchase, disbursal, handover and payment records and flags gaps before audit.

Your team still decides: The finance officer looks into each flag before passing it on.

Work smarterCoach to graduation

“We hear an enterprise has failed when the loan goes overdue.”

Flags households and enterprises whose savings, income or repayments have dropped two visits running.

Your team still decides: Field officers add households they're worried about, flagged or not.

Build fasterBuild the habit

“Women have good business ideas but can't write a plan the bank will read.”

Turns a spoken business idea into a one-page plan with costs, income and the loan needed.

Your team still decides: The field officer and participant go through it line by line.

Work smarterFind the poorest

“Our members qualify for pensions, insurance and housing schemes, but nobody can check each family against every rule.”

Matches each household's profile against a scheme list with eligibility rules and lists what they may qualify for.

Your team still decides: The coordinator checks documents with the family before any application is made.

Where we won't use AI.

These are the poorest families in the village. These lines don't move, whoever's asking.

  • Deciding eligibility without a visitSatellite or phone data misses the widow living in a relative's house.
  • Rejecting at a score cut-offA household one point above the line may be in crisis.
  • Automated grant or loan decisionsThe mentor knows the household. A person owns that call.
  • Fewer visits for 'likely failures'The households struggling most need more coaching.
  • Automated graduationGraduation ends support. Someone who knows the family decides.
  • Financial records in public AI toolsNames and account numbers stay in your private system.
What changes, and for whom

Everyone enters less and gets something back.

Participant

Sees how far she's come.

An asset card for what she received, and a progress card showing which graduation criteria she's met.

CRP

Gets books members trust.

A monthly statement of savings, loans and balances for each group, and the final eligible list to explain to the village.

Mentor

Walks in knowing what's changed.

What's different since the last visit and the actions agreed, before she reaches the door.

Programme officer

Sees who's close and who's slipping.

Households near graduation, those falling behind, and visit coverage by mentor.

Leadership & funders

Get graduation numbers that hold.

Graduation or enterprise survival by cohort, income and savings change, and a check 12 months after support ends.

Households marked graduated to hit a target often slide back within a year. We have graduation checked by someone other than the mentor, go back 12 months later, and report both numbers.

Funders notice. It's the kind of honesty that gets you renewed.
How we work with you

From your first email to a system your team actually runs.

Most systems launch in three to four months. The timeline moves with the number of programmes and roles, how messy the old data is, and how many tools need connecting.

Email us what hurts

Tell us where the work gets stuck today. Spreadsheets, notebooks, WhatsApp groups, all of it.

A 30-minute walkthrough

Free

You show us how the programme runs. We'll tell you straight if you need a new system, a better workflow or nothing at all.

Diagnosis

1 to 2 weeks

We map how your programme works, talk to the people who'll use it, review the tools you use today and hand you a System Blueprint, a clear plan of what we'll build.

Build in stages

6 to 16 weeks

Working screens every 2 to 4 weeks, tested by your field team in your own terms. We clean up your old spreadsheets and move them across.

Launch, then stay

30 to 60 days

Field training and launch, then 30 to 60 days of close support. After that, the system keeps changing as your programmes do.

What we'll need from you

  • A programme owner who can make decisions and join a short weekly review
  • Your current forms, trackers and one recent donor report
  • A few field staff to test screens before launch
  • Access to the tools you want connected

Works with what you already use

We connect to LokOS or NRLM exports, bank statements, WhatsApp, your survey tools and Zoho Books or Tally, rather than replacing what already works. Zoho licences sit in your organisation's name, and everything exports whenever you want it.

100+organisations worked with
150+systems built
5+countries
ZohoAdvanced Partner
75%of our work is with nonprofits
Your data, your rules

What 'your data stays yours' actually means.

The questions your finance head and IT lead will ask, answered before the call.

Your account, your licence

Zoho licences are bought in your organisation's name and hosted in the Zoho data centre region you choose, including India. We work inside your account.

Export any time

Every record exports to Excel or CSV whenever you want, including the day you stop working with us. You're not locked in to us for support either.

Access by role

Each person sees only the records their role needs. Bank account numbers are masked in field views.

A trail on every change

Edits are logged with who made them and when, so an audit or a funder query doesn't start from memory.

Consent and DPDP

Consent is recorded for each purpose, and we agree up front how long data is kept and when it's deleted. ZolaShield, our DPDP platform, shows where you stand.

AI stays inside your system

AI features run on your records under your account's controls. Beneficiary records never go into public AI tools.

Go deeper

Practical guides for this programme context.

Use these focused implementation guides alongside the sector playbook.

Tell us which livelihoods model you run. Bring us one tracker.

We’ll map what your team does now, identify the smallest useful first version and show where follow-up, learning and reporting can share the same information, without forcing your programme into a generic template.

If budget is tight, ask about the Built for Impact Programme, which gives eligible nonprofits free Zoho credits.

Other sectors we build for

The same approach, shaped to each kind of work.

See all 16 sectors on one page →

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