Investor overview · 13 September 2026
Practical intelligence for every stage of the farming season.
Krishi AI helps small and medium-scale farmers make informed decisions, manage costs and protect earnings through accessible knowledge, practical guidance and technology.
Download the product overview (PDF)A mission grounded in farming
Our mission is to increase farmers' net income and improve quality of life. Krishi AI is shaped by first-hand experience of rising input costs, soil degradation and the challenge of applying consistent operating procedures without timely, practical information.
Founder Vivek Maswadkar combines a 25-acre soybean and turmeric farm with 20 years in information technology. The opportunity is to earn trust through quality, adoption and demonstrated farmer outcomes.
The moat to build
Trusted local guidance, useful season histories and permission-based learning from many farms.
What farmers can do today
A connected set of information, guidance and record-keeping tools in one app.
Farming assistance
Text and optional voice conversations about everyday farm management, with saved and exportable conversations.
Crop photo assistance
A crop photo can help a farmer explore possible crop-health problems and prepare questions for local experts.
Mandi prices
Dated, government-sourced Maharashtra market records for more informed selling preparation.
Weather
A location-based, three-day forecast to support planning of field work and travel.
Knowledge library
Browse available agricultural documents by crop or category and revisit practical reference material.
Field Diary
Record activities, observations, inputs, costs and photos to build a season history.
Input offers and enquiries
Explore available input listings and take supplier enquiries further when the required details are available.
Carbon-credit information
Learn about a potential sustainability opportunity and submit an enquiry; no eligibility or payment is established.
Current features provide informational support. AI responses and photo results can be incomplete or wrong; important agronomic and treatment decisions need local verification. Mandi records are reference information, not purchase offers or guaranteed sale prices.
Technology and execution strengths
Designed for accessible, locally relevant assistance with deliberate product controls.
Language accessibility
English, Hindi, Marathi, Telugu and Tamil interfaces, with text and optional voice interaction.
Local knowledge and RAG
A retrieval-augmented foundation connects agents to curated crop-document titles, descriptions and references.
Agentic architecture
Specialist agents coordinate advice, photo analysis, clarification and supplier requests using controlled tools.
Lean, agile execution
Direct farmer feedback and short decision cycles support focused, practical iterations.
Product direction
Collective farm intelligence—built with farmer choice.
Voluntary questions, photos, crop intentions and diary records can become useful signals only when context, quality and risk are checked. The intended cycle is to observe, validate, interpret, share targeted reviewed guidance, and learn from recorded outcomes.
01
Observe
02
Validate
03
Interpret
04
Act
05
Learn
Participation should be voluntary. Aggregated insights may be shared with partners; identifiable farm records require specific permission. No collective analytics outcome is claimed as delivered today.
Six-to-twelve-month direction
A focused Maharashtra cohort, starting with soybean and turmeric.
Guidance and planning
Months 1–6 · Initial pilots
Review soybean and turmeric guidance with local experts. Pilot crop-stage plans, reminders and diary links with a small cohort.
Months 7–12 · Expansion
Expand reviewed coverage after pilot feedback. Add structured harvest and sales records with clearer season cost summaries.
Listings and enquiries
Months 1–6 · Initial pilots
Pilot farmer produce listings and input requirements, with useful details and direct buyer or supplier enquiries.
Months 7–12 · Expansion
Improve matching, listing freshness and enquiry follow-up. Test aggregated demand and coordinated purchasing where viable.
Farmer and expert directory
Months 1–6 · Initial pilots
Create opt-in profiles with crop, location, experience and service details, together with clear contact rules.
Months 7–12 · Expansion
Add discovery by crop and area, supported introductions and feedback where the directory proves useful.
Collective analytics
Months 1–6 · Initial pilots
Standardise voluntary crop intentions and diary inputs. Define quality checks and pilot a small number of expert-reviewed alerts.
Months 7–12 · Expansion
Test aggregated planting-demand insights, regional comparisons and harvest coordination. Explore insurance use only with validating partners.
What determines readiness to expand
Continued use, record completeness, guidance relevance, completed enquiries and actions after alerts. Net-income change should be assessed using comparable season costs and sales records, with weather and price context.
Interested in the Krishi AI journey?
We welcome conversations with investors who value practical, evidence-led technology for Indian agriculture.
Contact Krishi AI