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 →Most products stall between idea and launch.
Slow discovery. Brittle architecture. AI tacked on at the end because someone remembered it. Most teams we worked with before they came to us had the same set of problems.
Why Passionfrut
A good product comes from steady work on strategy, design, and engineering. We do all three, in that order, and we keep going after launch.
Integrated AI & Product Engineering
We design AI into the architecture from the start. Design, code, and model decisions get made in the same room, not bolted together later.
Outcome-Driven Delivery
Sprints are measured against business outcomes. Did engagement move. Did retention. Did the funnel. Shipping a feature is not the same as shipping a result.
Rapid Prototyping
A working prototype in weeks, not months. You get something to put in front of users and investors before you commit to the full build.
Scalable Architecture
The MVP and the production system share a backbone. You add capacity as you grow instead of ripping out foundations when traffic arrives.
Flexible Team Integration
Work with us the way that fits. We can embed alongside your team or run the build end to end.
Continuous Quality & Reliability
Automated tests, CI/CD, and monitoring ship with the first commit, not with the first incident.
Digital products designed and shipped
AI-powered systems running in production
Client satisfaction across engagements
Shorter delivery time using our reusable AI components
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.
Featured projects
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.
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.
The stack
we build with
The tools we reach for, and the reasons we reach for them. No layer is picked by default.
AI & ML
Mobile
Web Frontend
Backend & APIs
Cloud & DevOps
Databases
What our clients say
We had a working prototype in three weeks and used it to close our seed round. They picked up what we were building faster than the firms we had pitched the same problem to.
They operated more like an embedded product team than a vendor. The AI features they shipped are the ones our customers bring up most often on sales calls.
The AI inside our platform is invisible to the user, which is the point. People just notice the system gets them to the right answer faster. Half of them have no idea there is a model in the loop.
What you get when you work with us.
We ship code that runs in production, holds up under load, and earns trust from the teams that depend on it.
Speed, stability & high performance
We optimise for load time, zero-downtime deploys, and infrastructure that takes a traffic spike without paging anyone at 3am.
Intelligent AI features out of the box
Every product we ship comes with AI built in where it earns its place. Smart search, recommendations, predictive analytics, automation. Not bolted on, not in phase two.
Secure, enterprise-grade development
SOC 2-aligned process, encrypted data pipelines, role-based access, and a security review on every release. The audit trail is there when procurement asks for it.
Idea to live product.
We take a product from concept to live customers in weeks. Here is what the schedule looks like.
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.