Why I Called "Sarlaben"
This morning I found myself thinking about a simple act I had watched since childhood: a woman gently milking a cow, feeling the animal’s warmth and rhythm. That quiet intimacy between human and animal has always felt far removed from the cold dashboards of data science — until now.
Amul’s new AI assistant, "Sarlaben", is changing that distance. By turning decades of cooperative milk records, veterinary logs, and even satellite fodder data into an always-on advisory service, the dairy world is quietly moving from cowshed intuition to cloud-powered decisions From cowshed to cloud: Amul Diary launches AI assistant 'Sarlaben' .
I wrote about digital farming some time ago — about how data could nudge traditional practices toward higher productivity and inclusion — and Sarlaben feels like that idea arriving in the market, in voice and Gujarati, at scale Digital Farming .
What "Milking Data" Really Means
Milking data is not a metaphor — it is a sequence of practical steps:
- Collect: every litre at the village collection point, each veterinary visit, the insemination log; decades of this live in cooperative systems.
- Clean & integrate: make procurement, health, and fodder signals talk to one another.
- Analyse: surface early-warning signs for mastitis, nutritional gaps, or breeding windows.
- Advise: deliver personalised, actionable guidance back to the farmer — by app or by phone.
Sarlaben demonstrates each link of that chain. Importantly, it makes the guidance personal: not generic best practices, but recommendations shaped by a farmer’s herd history and cooperative records.
Why Voice + Local Language Matters
Technology often stalls at the last mile. What Amul is doing right is coupling high-fidelity data with accessibility:
- Voice calls for feature-phone users.
- Gujarati as the first language of interaction.
These design choices recognise a truth I’ve returned to in my writing: adoption is not just about accuracy; it’s about language, trust, and habit. A farmer who can call and ask about a bloated rumen at 4 a.m. — and get an immediate answer — is more likely to act than one who must read a long advisory in English on an app.
The Upsides I Celebrate
- Productivity gains: timely feeding and vaccination reminders can raise yields and reduce loss.
- Inclusion: women producers, smallholders and feature-phone users can join an information loop they were historically excluded from.
- Systemic intelligence: connecting AMCS-style procurement data with veterinary logs enables insights that no single veterinarian or extension worker could infer alone.
These are the wins I had hoped digital farming could deliver. Today, those hopes look less like theory and more like operational reality.
What Still Keeps Me Awake
Data-led care is promising — but not unproblematic. A few caveats I worry about:
- Data governance: who owns the herd-level data? Cooperatives, farmers, platform providers, or the AI team? Clear rules are essential.
- Algorithmic bias: models trained on cooperative data from one region may not generalise to very different geographies, breeds, or feed patterns.
- Over-reliance: advice should augment, not replace, local vets and indigenous knowledge.
- Incentives: an always-on assistant can become a channel for nudges that prioritise procurement or inputs. Transparency is non-negotiable.
If milk becomes a source of recurring, monetisable signals, we must ensure farmers truly benefit from the value they produce.
How I’d Design the Next Phase
If I were advising the cooperative and its partners, I would push for three priorities:
- Farmer-first data contracts: opt-in models with clear revenue- or service-sharing for insights derived from herd data.
- Local vet integration: build workflows so that AI flags are triaged by local vets, not only by call-centre scripts.
- Explainability & recourse: every recommendation should come with a plain-language rationale and a path for a farmer to contest or seek second opinion.
These are not technical luxuries — they are what turn a novelty into a durable public good.
A Small Thought to End On
Watching a woman milk a cow used to be an act I associated with patience and craft. Today, that same act is also a stream of signals: yield, health, cycles, economics. If we steward that stream well — with consent, transparency and local partnership — we can amplify livelihoods without diminishing dignity.
Sarlaben is an early, important step. I’ll be following how farmers use the voice channel, how vets respond to AI alerts, and whether cooperative governance evolves fast enough to keep the benefits where they belong: in the hands of the producers.
Regards,
Hemen Parekh
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