Nutrition Tracking That Doesn't Feel Like a Chore
A nutrition app built around a single photo. Point the camera at the plate, get calories and macros back. The manual ingredient logging that loses most calorie-tracker users is gone.
In short: A nutrition tracker that turns a photo of a meal into a calorie and macro breakdown. The photo is the entire input. NutriTrac uses computer vision and an LLM-enriched food database to identify a meal from a photo and return calories and macros in seconds. It is built for the people who quit traditional tracking apps because typing in every ingredient is too slow to keep up beyond two weeks..
NutriTrac uses computer vision and an LLM-enriched food database to identify a meal from a photo and return calories and macros in seconds. It is built for the people who quit traditional tracking apps because typing in every ingredient is too slow to keep up beyond two weeks.
- Manual food logging loses most users inside two weeks. The dropout curve is well documented.
- Food photos come in every lighting condition, from every angle, on every kind of plate. Portion estimation has to be robust to all of it.
- Packaged foods have barcodes. Home-cooked regional dishes do not. The database has to cover both, including the dishes the training set never saw.
- The app has to be fast enough to become a habit. Anything that takes more than ten seconds gets skipped on day three.
Photo-to-Macro Recognition
A computer-vision pipeline that identifies the dish and estimates portion size from a single photo, cross-referencing a nutrition database enriched with regional cuisine.
LLM-Assisted Food Matching
An LLM disambiguates visually similar dishes and fills database gaps by reasoning over ingredient descriptions and the user's own corrections.
One-Tap Logging Flow
A capture-to-log flow that takes under ten seconds from opening the camera to a saved, editable entry in the daily diary.
Adaptive Goal Coaching
A lightweight coaching layer that adjusts daily calorie and macro targets based on logged trends, not on a static formula set at signup.
AI & ML
Mobile
Backend
Cloud
Kshetra
The first Vastu consultant built on machine learning. Upload a floor plan, get a compliance report.
Read case study →Events · SaaSVowStory
An event and guest-management platform built for Indian weddings and the celebrations around them.
Read case study →CRM · SaaSSplurge
A fast, focused CRM for small sales teams. Pipeline visibility, deal tracking, follow-ups. No enterprise onboarding tax.
Read case study →AgriTech · Supply ChainGrainTech
A platform that gives grain traders and processors a live view of stock moving from farm to mandi to warehouse.
Read case study →Have a similar idea?
Tell us what you are building. We will say back whether we can help.
Get Free ConsultationFrequently asked questions
How long does a typical project take?
Most MVPs ship in 8 to 12 weeks. Larger products with AI components typically take 12 to 20 weeks. We will give you a realistic timeline once we have had the discovery call.
What does a prototype cost?
Prototypes start at $1,000 for a focused proof of concept. Full MVPs sit between $5,000 and $25,000 depending on complexity, how much AI is involved, and which platforms you need to ship on.
Do you work with early-stage startups?
Yes. About half our clients are pre-seed to Series A. We are used to requirements that move, budgets that are tight, and the occasional pivot.
Can you integrate AI into our existing product?
Yes. We regularly add search, recommendations, automation, and content generation to existing codebases. A full rebuild is rarely required.
What AI models and platforms do you use?
We do not standardise on one vendor. We work with OpenAI GPT, Anthropic Claude, Google Gemini, open-source models like Llama and Mistral, and custom fine-tuned models where the use case calls for it.
How do you handle ongoing maintenance?
We run monthly retainers for post-launch support, monitoring, bug fixes, and feature work. Most clients stay on one after launch.
What if we already have a technical team?
We plug in however fits best. Embedded engineers inside your team, a parallel squad on a separate workstream, or advisory consultants reviewing your roadmap. We work with whatever stack and rhythm your team already uses.