AI-Native
Product Development
For funded founders and Heads of Innovation at SMEs building new business models. From MVP to production, AI-native from day one, with scalability and compliance built in.
COMMON PROBLEMS
The pressure is real: speed, quality, and no team to build it
A product to ship, no team to build it
You have the idea and the market pull, but hiring a full AI-capable team takes months you don't have, and the window to launch is now.
A hard deadline with quality on the line
An investor demo or market moment is fixed, and shipping something that feels rushed or breaks on stage costs you far more than the deadline itself.
A PoC that won't survive real users
A promising prototype was never built for scale, security, or reliability, so it stalls the moment real traffic and real data hits it.
Competitors are already shipping AI features
Rivals are putting AI in front of users while you're still scoping, and every week of delay widens a gap that gets harder to close.
Three ways to start,
one governed path
Start with a fast MVP, go all the way to a production building, or retrofit AI into what you already run. Each engagement has a fixed scope and a clear handover.
Rapid MVP Launching
- Fast-track live AI flows and key admin features
- Rigorous testing to eliminate AI hallucinations and operational risks
- Verified prototype, investor deck, and production path
What you walk away with:
- A pitch-ready MVP with one live AI flow
- Demo and deck assets for investor conversations
- A clear, costly path to the full production build
AI Integration for Existing Products
- Embed AI into workflows to cut headcount overhead
- Feature-flagged deployment with strict hallucination testing
- Track cost-per-feature and operational ROI as you scale AI adoption
What you walk away with:
- Live AI features inside your existing product
- Adoption and retention data on every rollout
- Clear cost-per-feature economics for each AI capability
Full Product Build
- Complex data, dashboard, and features via AI-assisted engineering
- Full evaluation, observability, and SOC2 path
- Full visibility via logged tracking with team capability transfer
What you walk away with:
- A production-grade product live for real users
- Observability, evals, and a SOC2 path in place
- Full IP, source, and a documented handover to your team
OUR DELIVERY METHOD
(ADLC)
Traditional SDLC with AI copilots embedded at every stage. Each package covers a specific slice of the lifecycle, with human review at every merge.
Plan
AI-assisted PRDs, user stories, and task breakdowns.
Design
AI UI/UX prototyping and design system generation from specs.
Build
Repo-aware code generation with human review at every merge.
Test
AI-generated test suites, performance benchmarks, coverage analysis.
Deploy
SecOps deployment with automated security scanning and monitoring.
White-Label Shuttle Booking MVP Launched In 1.5 weeks
Launch a new white-label shuttle booking platform without diverting engineering; comparable launches took 3–5 weeks.
- Sequential, handoff-heavy process
- 4 disconnected stages: requirements → PRD → spec → sprint dev
- Each handoff adding days of back-and-forth
- Comparable launches took 3-5 weeks
- Gap wasn't effort - it was the path from idea to code
Working alongside the team through ideation, spec drafting, and build, collapsing PM↔engineering handoffs into one continuous flow.
Cold Email Outreach Automation
Lead research and personalized email writing done manually per lead, capping daily volume and forcing a trade-off between speed and depth.
- Manual research per lead
- Hours spent per personalized email
- Speed vs quality trade-off
- Capped daily outreach volume
- Generic emails at scale
An AI agent reads leads automatically to research each company's website, branding, and fit score, then prioritizes and drafts personalized emails saved directly into Gmail for review and send. 90% time reduction per lead, at greater scale.
Automated Business Reporting
BDO spent half a day per reporting cycle on manual data reading, insight extraction, and write-up, leaving no time for deep analysis or visualization.
- Half-day per report on manual work
- Reading raw data across multiple sources
- Extracting and writing insights by hand
- Visualizations cut short to save time
- Gap wasn't skill, it was time budget
An AI skill ingests raw structured and unstructured data, extracts the insights that matter, and auto-generates polished analysis and visualizations in one pass. The full reporting cycle compressed from half a day into a single quick review.
Project Knowledge Centralized Into AI Graph With LLM Wiki
Documentation scattered across Google Docs, Lark, chats, and emails with no centralized source of truth, costing hours per person to hunt information.
- Docs scattered across Docs, Lark, chats, emails
- No centralized source of truth
- Hours wasted per person hunting information
- Decisions duplicated or lost, no version history
- Gap wasn't people, it was the missing knowledge graph
Raw project files auto-ingested into a fully linked knowledge graph where every document, decision, and thread is connected. Exposed via MCP so anyone can ask in natural language and get instant, source-backed answers.
Reduce AI costs 40% through centralized AI governance
Organizations share postpaid API keys with no cost visibility or buy individual subscriptions that go underutilized, creating unpredictable spending and zero governance insights.
- Shared API keys with no cost visibility
- Individual AI subscriptions often underutilized
- Unpredictable monthly AI spending
- No centralized usage or governance insights
- Gap wasn't demand, it was the missing control layer
One gateway captures every AI request, shares pooled capacity across employees, and enforces usage policies automatically. Real-time cost and consumption analytics surface optimization opportunities and training needs.
Employees find company knowledge10× faster with AI
Employees search across Lark Docs, Wikis, HR policies, and SOPs to find answers, slowing onboarding and creating inconsistent responses as company knowledge grows.
- Answers scattered across Lark, Wikis, SOPs, HR docs
- Employees rely on colleagues to find information
- Knowledge grows faster than search can keep up
- Onboarding slowed by fragmented documentation
- Gap wasn't knowledge, it was retrieval
Employees ask questions where they already work, no new tool to adopt. A Retrieval-Augmented Generation service indexes internal documentation and returns source-grounded answers, backed by actual company documents.
Multi-Tenant Superadmin Shipped In 6 Weeks
Single-tenant healthcare admin had to become multi-tenant superadmin with cross-clinic login, while the client added feature requests mid-build across multiple disconnected repos.
- Single-tenant to multi-tenant superadmin migration
- Split across multiple repos with no gitflow
- Client adding feature requests mid-build
- 1-2 devs cannot manually rearchitect at that scale
- Gap wasn't skill, it was multi-repo coordination overhead
Developers set the architectural foundation, execution plan, and multi-repo gitflow first. BAs and QAs use MCP to track evolving specs, while a unified tenant model drives AI-generated code across all repositories.
FAQ
We have an idea but no technical team. Can you still build it?
Yes, that is exactly who Rapid MVP Launching and Full Product Build are for. We handle product scoping, architecture, and delivery end to end, so you get a working AI product without hiring a full team first. Everything ships with documentation and a handover so your future in-house team can own it.
How fast can we get something in market?
Will an MVP hold up when real users and scale arrive?
Can you add AI to a product we already have?
Who owns the code and IP once the build is done?
We have an investor deadline. Can you support the pitch?
Let Enosta be your Partner!
Ready to build the right product the AI-native way?
What our customer say
Thuong Tran
CEO - Nha Tro Van Minh
Duc Ngoc Nguyen
Former Business Analyst - TPBank