Above Data needed a fundraising-ready MVP that could turn multi-day, analyst-led audience creation into a self-serve product while proving enterprise-scale, multi-tenant SaaS readiness to investors and early customers.
Tintash designed and built signal-based infrastructure platform with conversational workflows, interactive data visualisation, direct ad-platform delivery, and enterprise-grade cloud infrastructure from prototype to production.
Turned an investor-ready MVP into seven figures in sales, replaced deck-driven fundraising with a live product experience, and created a scalable foundation for enterprise pilots and post-raise growth.
We used the demo built by Tintash to secure seven figures in sales in the following quarter.
We launched our MVP on time and now have a UI framework for the rest of our platform. We also have a working UI with an AI agent that we're continuing to train and improve.
They leaned in early, were nimble, and could iterate quickly on plans. Their deep integration into our team and business to act like part of the team was unique.
VP of Product
Above Data
Above Data was an early-stage signal infrastructure platform with a strong thesis: marketing and CPG brand teams should be able to build, refine, and activate signal-based audiences from live, deterministic signal without depending on data engineers, SQL queries, or week-long analyst turnarounds.
To validate that thesis with investors and prospective enterprise customers, they needed a working MVP they could put in front of a fundraising audience. Not a clickable prototype or a deck, but a real, multi-tenant product that a live user could drive end-to-end against real consumer data.
The MVP had to be:
- Polished enough to anchor the fundraising story
- Technically credible enough to survive due diligence
- Architecturally sound enough to support the company through its first enterprise customers post-raise
Their target customers, marketing teams at retailers and CPG brands, had access to valuable first-party and panel data, but no intuitive way to interact with it.
Building a targeted audience typically required:
- Filing a ticket with a data engineer
- Waiting for a SQL-driven extract
- Iterating over email
- Manually loading the resulting list into ad platforms
The cycle could take days, limiting campaign agility and restricting the product to a small group of analysts. Above Data approached Tintash with a clear ambition: build a fundraising-ready MVP that rebuilt the audience-building experience as a conversational, AI-native product any marketer could use.
Key discovery questions included:
- How do users describe an audience in plain English while still ending up with a precise, deterministic signal definition that ad platforms can ingest?
- How do we present millions of consumer records in a way that is digestible, interactive, and actually guides the user toward a high-quality audience?
- How do we keep the AI co-pilot grounded in real data and not hallucinated numbers?
- How do we ship this as a true multi-tenant SaaS product with enterprise-grade authentication from day one?
- How do we close the loop by delivering audiences directly to Facebook, Google, and other destinations without CSV exports?
- How do we do all of this on an investor-driven timeline with the polish and reliability needed for live fundraising demos?
Tintash designed and built the Above Data MVP end-to-end, creating a fundraising-ready, multi-tenant platform that turned analyst-led signal-based audience creation into a self-serve product for enterprise marketers.
- Product design and Figma prototyping
- Next.js frontend development
- Agentic AI product development
- Python AI agent backend
- Data and delivery integrations
- Google Cloud Platform infrastructure
Rather than splitting the project across multiple vendors, Above Data partnered with a single team that owned the product from the first design prototype to production deployment.
Before a single line of production code was written, Tintash designed the complete audience-builder experience in Figma, including:
- Landing page
- Conversational chat
- Interactive charts
- Live Audience Ingredients sidebar
- Dashboard
- Audience detail page
- Four-step delivery wizard
The prototype became both:
- A fundraising asset for investor and design-partner conversations
- The engineering source of truth that kept MVP scope tight and timelines on track
System flow
End-to-end flow of the AI Audience Builder
Tintash built an AI-native Audience Builder where marketers describe an audience in natural language and an embedded AI co-pilot guides them through the segmentation process.
The architecture was designed as a two-tier system:
Python agent backend
Exposed deterministic data tools such as:
- Purchase recency data
- Brand affinity data
- Spending distribution data
Frontend generative UI
- Purchase recency distributions
- Brand breakdowns
- Spending charts
This ensured the AI could never invent numbers, because every chart was grounded in real consumer data pulled from the backend.
Dashboard - "Welcome to Audience Builder"
Every AI-generated chart is interactive.
Clicking a recency band, brand, or spending range:
- Adds the selection to a structured Audience Ingredients panel
- Updates the live consumer count in real time
- Creates a deterministic and auditable audience definition
This gave marketers complete visibility into exactly how an audience was being built.
Above Data needed a credible path to enterprise revenue.
Tintash built:
- Subdomain-based tenant routing
- Organisation-aware authentication
- Secure multi-tenant data architecture
- Enterprise-grade cloud infrastructure
Tintash also owned:
- Cloud environment setup
- Identity and access controls
- Storage and networking architecture
- Deployment pipelines
- Environment templates
New enterprise tenants can now be onboarded through configuration instead of engineering cycles.
Tintash built a dedicated audience detail experience showing:
- Audience size
- Estimated CPM
- Time window
- Audience trend charts
- AI-generated summaries
- Full ingredient breakdown
This gave marketing, finance, and compliance stakeholders a single shareable view of every audience.
Audience creation landing
"Start creating your audience"
Building an audience was only half the workflow. Activation was the other half.
Tintash designed and built a guided delivery flow:
Select Audience → Destination → Parameters → Preview
Audiences can now be pushed directly to:
- Facebook Ads
- Google Ads
- Other destinations
With:
- Audience summaries
- Match-rate visibility
- Delivery validation
This removed the need for CSV exports and manual uploads.
The final MVP became a polished, AI-native marketing product that Above Data can demo live during investor and enterprise conversations.
The platform delivers:
- A personalised dashboard experience
- Draft audiences that can be resumed
- Audience insights and forecasting
- Upcoming delivery schedules
- End-to-end audience creation and activation
- Per-tenant enterprise environments that can be provisioned without re-architecting the platform
Fundraising Ready
A real, working, multi-tenant MVP replaced a deck-and-prototype fundraising narrative, and the demo built by Tintash helped Above Data secure seven figures in sales in the following quarter.
From Days to Minutes
Audience creation shifted from multi-day, analyst-led SQL and CSV workflows to a self-serve conversational experience completed in a single sitting with zero engineering involvement.
Expanded User Adoption
Audience-building moved beyond a small analyst team to the broader marketing function across each customer organisation.
Unified Data Access
Multiple deterministic signal sources are now accessible through a single conversational interface, with the AI selecting the right data source and grounding every chart in real data.
Enterprise Ready
Early design partners and enterprise pilots can now be onboarded through configuration rather than engineering cycles.
Closed Activation Loop
Audiences are pushed directly to Facebook Ads, Google Ads, and other destinations from inside the same product where they are created.
