AI products
we have shipped
Five products across PropTech, events, CRM, AgriTech, and HealthTech. Each one shipped to production with real users on the other end. The patterns repeat: a hard data problem, an AI core, and an interface that hides the complexity.
Selected work
Each case study covers the challenge, the solution, the tech stack, and the numbers that moved.
Kshetra
The first Vastu consultant built on machine learning. Upload a floor plan, get a compliance report.
VowStory
An event and guest-management platform built for Indian weddings and the celebrations around them.
Splurge
A fast, focused CRM for small sales teams. Pipeline visibility, deal tracking, follow-ups. No enterprise onboarding tax.
GrainTech
A platform that gives grain traders and processors a live view of stock moving from farm to mandi to warehouse.
NutriTrac
A nutrition tracker that turns a photo of a meal into a calorie and macro breakdown. The photo is the entire input.
Industries we serve
Where we have done our deepest work.
Property decisions are expensive and slow. We have built systems that price assets in real time, surface market signals most teams miss, and turn scattered listing data into a usable signal for brokers, developers, and proptech startups.
Venue operators, festival teams, and hotel groups all run on the same problem: matching staff and resources to a moving schedule. We have built AI concierge systems, dynamic scheduling engines, and guest-management platforms that handle that work in the background.
Banks, lenders, and neobanks live inside a regulator's clock. We build the fraud-detection pipelines, risk-scoring models, and KYC automation that let a team move faster without stepping outside compliance.
HealthTech work has to clear HIPAA and GDPR from day one. We build predictive triage, patient-engagement platforms that hold attention between visits, and records-automation tools that take the paperwork off clinicians' desks.
Recommendations, demand forecasting, on-site search. The work is concrete: build models that adapt in real time to what shoppers are doing, and tune inventory and pricing off the same signal.
Most of the data a factory floor generates gets thrown away. We build the predictive-maintenance models and quality-control pipelines that turn it into early warnings, and we plug them straight into the ERP the plant already runs.
Curricula built for the average student serve none of them. We build adaptive learning platforms and AI tutoring assistants that adjust to each learner, plus the analytics dashboards that give teachers visibility into who is falling behind.
SaaS scale problems land together: multi-tenancy, usage metering, retention. We have shipped AI copilots, workflow-automation engines, and analytics layers that slot into existing SaaS platforms, so product teams can ship intelligent features without a rebuild.
How we build products
LIVE PIPELINEDiscovery & Strategy
We map your users, market, and the actual problem. The roadmap that comes out fits the budget you actually have.
UX & Product Design
Wireframes, prototypes, and a working design system. Every screen traces back to a user research finding, not to a hunch.
Architecture & Setup
Picking the stack, setting up the infrastructure, and writing the kind of code you will still want to read in two years.
Development Cycles
Two-week sprints, demos every Friday, and the room to change direction when something is not working.
QA, Testing & Hardening
Automated tests, manual QA on the devices your users actually carry, and a security and performance pass before we ship.
Launch & Iteration
We deploy, we watch the dashboards, and we keep iterating on what real users do once the product is in their hands.
Working prototype from $1,000. Real UX, core features, and a demo link you can send to investors.
Learn more →Services for products
that lean on AI
Strategy, design, engineering, and the AI work. One team covers all of it, from the first whiteboard to the production deploy.
View all services →AI Development
Custom models, LLM integrations, and AI-native products. We design the model, the interface, and the feedback loop together, because that is where an AI product gets useful or fails.
Explore service →AI · StrategyAI Consulting and Strategy
Find the AI use cases worth building, sequence them into a roadmap, and decide on the architecture before any code is written.
Explore service →AIGenerative AI Development
LLM integrations tuned to your domain. Content pipelines, copilots, and assistants that speak the language of your product.
Explore service →Roughly half the typical delivery time
Our reusable components and patterns take about half the engineering time off a usual build.
Tell us what you are building.
Send a few lines about the project. We will reply within 24 hours with a free consultation and a rough estimate.
Frequently 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.