A survivor-support centre, women’s collective, safe-city programme, norm-change campaign and workplace training initiative work in different ways. This playbook helps teams keep that difference while connecting the evidence needed for action and learning.
| Renuka P. | 28 | widow? FHH?? |
| Renuka Patil | 29 | dup? |
Five places, one woman. Nobody can see why she stopped coming.
Many organisations combine two or three. Start with the closest match, then borrow only the parts that solve a real programme problem.
Teams provide immediate safety planning, shelter connection and coordinated support.
People receive continuing psychosocial, legal and court-related assistance.
Communities audit public spaces and engage authorities on infrastructure and services.
You work with groups, schools, media or communities to shift beliefs and behaviour.
Women, girls or gender-diverse people organise, make decisions and influence institutions.
Programmes combine livelihoods, workplace policy, training or digital safety.
Not sure which one you are? Bring one tracker and describe a normal week. The model usually becomes clear quickly.
These come up in almost every first conversation we have with gender equality teams.
"Our agency scores went down at endline."
The baseline was done in the courtyard with the mother-in-law listening. The endline was private. Now the programme looks like it failed.
"Half the group stopped coming after the violence module."
Nobody wrote down why, so the module got cut instead of anyone working with the families.
"Once the training's done, we only hear from the peer leaders who are comfortable on the phone."
The quiet villages stay quiet. By the time a field associate visits, the group hasn't met for four months.
"The funder asks what changed for women. We send loan numbers."
Repayment is excellent, and we still can't say who decided how the money was used.
We haven't built for an SHG federation or a peer-leader cascade yet, and we won't pretend otherwise. These are real systems for girls' safety, women's skilling and scheme access for families.
Students, trainers and sessions in one system, with location-stamped trainer attendance, a restricted case module for abuse disclosures and donor reports.
Read the case study →TRRAIN10,000+trainees a yearPankh (persons with disability) and TRRAIN Her (women) run in one system, from regional teams down to each trainee, with centre and placement approvals and automatic certificates. Live in four months.
Read the case study →Tapasya Foundation5,000+families matched to schemesA master list of government schemes and their eligibility rules matches each family to the right ones, and each application is tracked until the benefit arrives. Live in four months.
Read the case study →Recognise your programme? Tell us where the work gets stuck and we'll suggest a useful first version.
This is one operating model, not a template for every gender equity organisation. Use it to see how everyday work, follow-up and management decisions can connect.
Stage 01 · Reach every woman
The women who come first to a meeting are rarely the ones with least voice. Widows, Dalit and Adivasi women, women with disabilities and young brides are often kept at home.
| ID | Member | Village | Status |
|---|---|---|---|
| WM-0932 | Renuka Patil | Hebballi | Re-interview booked |
| WM-0917 | Shabana Mulla | Garag | Not private |
| WM-0925 | Gangavva Hosamani | Hebballi | Listed, not met |
| WM-0901 | Sunanda Madar | Amminbhavi | Enrolled |
Stage 02 · Keep women coming
Attendance falls in harvest season and after the first session on violence, when some husbands push back. The right response depends on whether it's the calendar, the content or the backlash.
| Group | Module | Attendance | Action |
|---|---|---|---|
| SHG Kavya | 4 · Safety at home | 58% | Supervisor visit |
| SHG Chaitanya | 4 · Safety at home | 66% | Leader calls |
| SHG Asha | 3 · Money basics | 79% | Harvest dip |
| SHG Deepa | 5 · Your rights | 88% | On track |
Stage 03 · Money, enterprise & support
A woman may be saving, starting a small business and seeking legal aid in the same month. What counts is whether the loan's in her name, she controls it, and the referral led somewhere.
| ID | Member | Linkage | Status |
|---|---|---|---|
| WM-0917 | Shabana Mulla | Widow pension | 52 days pending |
| WM-0925 | Gangavva Hosamani | PM Ujjwala | Documents missing |
| WM-0932 | Renuka Patil | NRLM enterprise loan | In her name |
| WM-0901 | Sunanda Madar | Job card | Received |
Stage 04 · Prove what shifted
Attendance and loan numbers won't answer the funder's impact question. You need an endline on the same measures as the baseline, and a way to keep tracking women after the programme leaves.
| Group | Grade | Income decisions | Status |
|---|---|---|---|
| SHG Chaitanya | Grade C | +8 points | Extended support |
| SHG Asha | Grade B | +21 points | Review in 3 months |
| SHG Kavya | Grade A | +34 points | Graduating |
| SHG Deepa | Grade A | +29 points | Federated |
Choose the modules that fit your theory of change. Survivor services require very different privacy and case controls from campaigns, audits or livelihood programmes.
Support consent-led intake, risk review, safety actions and safe follow-up.
Keep services connected while separating restricted notes and professional roles.
Turn community observations into location-based actions and verified improvements.
Track who encountered what, how often and what changed beyond reach.
Follow group quality, decision-making, enterprise support, income and agency together.
Manage certification, workplace compliance, online-harm response and evidence for advocacy.
Most AI projects stall because the records underneath are messy. So we work in a fixed order.
Every field has to earn its place by helping someone decide something. If nobody uses it, we don't collect it.
A system people avoid is worse than a spreadsheet. We design around shared phones, patchy signal and WhatsApp.
On clean, connected records, AI can read paperwork, draft reports and flag who needs attention. A person always checks.
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.
We picked the jobs that save gender equality 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 →
“We only hear back from peer leaders who are confident filling in a form.”
Your team still decides: The field associate listens to a sample each week and calls any leader whose notes look off.
“Our trainers fill registers on paper and someone types them up days later.”
Your team still decides: The trainer checks the list against her register before it's saved.
“The same woman joins a second programme two years later and we count her twice.”
Your team still decides: The coordinator checks every flagged pair before any record is merged.
“We find out a group has stopped meeting when the field associate finally visits.”
Your team still decides: The coordinator talks it through with field staff before changing any visit plan.
“The funder asks what changed for women and we send the number trained.”
Your team still decides: The M&E lead reviews the split tables and the response rate before the narrative is written.
“Women tell us what they did with the training, and those answers sit unread in a column.”
Your team still decides: Two team members check a sample against the originals.
Women's safety depends on this data. These lines don't move, whoever's asking.
Member list, attendance and savings against target, read out in the meeting or sent as a voice note.
Her groups, the next module and the women who've missed two sessions, on one screen.
Open referrals, days since last contact and safety plans, visible to nobody else.
Loans in women's own names, scheme applications by block and groups with backlash reports.
Paired baseline to endline on income, mobility and voice, split by social group and tier.
If the baseline was taken with her mother-in-law in the room, the endline shows a fall that isn't real. We record whether every interview was private, and report change on the pairs that were.
It's harder to report. It's also the number that holds up.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.
Tell us where the work gets stuck today. Spreadsheets, notebooks, WhatsApp groups, all of it.
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.
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.
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.
Field training and launch, then 30 to 60 days of close support. After that, the system keeps changing as your programmes do.
We connect to WhatsApp and voice notes, IVRS, your survey tools, bank linkage records and country-specific forms in Swahili, French or English, rather than replacing what already works. Zoho licences sit in your organisation's name, and everything exports whenever you want it.
The questions your finance head and IT lead will ask, answered before the call.
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.
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.
Each person sees only the records their role needs. Violence disclosures sit in a restricted layer only trained staff can open.
Edits are logged with who made them and when, so an audit or a funder query doesn't start from memory.
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 features run on your records under your account's controls. Beneficiary records never go into public AI tools.
Use these focused implementation guides alongside the sector playbook.
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.
The same approach, shaped to each kind of work.
