From Stalled Pilot to Working AI in 30 Days: 4 Real AI Navi Builds
You backed the pilot. You hired the vendor. And your board is still waiting. If that sounds familiar, you are in the company of most UK mid-market Consumer Products and Logistics leaders, where the majority of AI pilots never reach production and most AI investment never shows up in the P&L.
This article does not add to that pile of failure statistics. It does the opposite. Below are four AI products we actually built and shipped what each one solved, how long it took, and the result it delivered. Not claimed. Demonstrated.
Quick Summary: AI Navi's 30-Day Production Track Record
Don't have time to read the full case studies? Here is the direct proof of how AI Navi takes UK mid-market operations from stalled pilots to working AI in production:
| AI Build & Case Study | Industry Sector | Timeline to Delivery | Proven Business Result |
|---|---|---|---|
| 1. AI-Powered Talent Matching | Finance Marketplace | 5 Days (Prototype) | Sourcing reduced from days to seconds; automated vetting. |
| 2. Self-Service Diagnostics App | Digital Health | 4 Weeks (Full Build) | Rapid rebuild; featured at major AI healthcare exhibition. |
| 3. SalesGenius.ai (Live SaaS) | B2B Sales Agent | Weeks to Live | 80% reduction in manual outbound sales research time. |
| 4. ApplyGenius.ai (Live SaaS) | Recruitment / Careers | Under 30 Days | From zero to public launch with active, live users. |
The Operational Takeaway for Logistics & CP/FMCG Leaders: > These results prove that a working AI system in production does not require a 4–6 month agency mobilization cycle. By combining data engineering, strategy, and rapid execution into one embedded team, you can secure margin protection, demand forecasting, and 5–10% cost-per-delivery savings in less than a month.
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Why “Demonstrated, Not Claimed” Matters
The hard part of AI in the mid-market was never the model. It is getting a working system into production and into the hands of a team that uses it. That is the gap most pilots die in.
AI Navi exists to close that gap, and we do it from lived experience rather than theory. The team behind offer 25+ years of enterprise AI and digital delivery, $3B of CPG revenue under AI transformation, and a track record of first working AI in production in 30 days. Haja led AI and data at pladis, a $3B+ global CPG business. Abhishek is ex-Deloitte, an ex-CPG AI lead who ships 30+ AI products a year with lean teams.
The four builds below are what looks like in practice.
Case Study 1 — AI-Powered Talent Matching: Idea to Prototype in 5 Days
Sector: Finance marketplace
The problem: A mid-sized firm was spending days manually sourcing freelancers, with little visibility and frequent mismatches. Every new requirement meant another round of slow, manual searching.
What we built: A working AI-powered prototype that instantly matches candidates by skills, availability, and rate, with built-in vetting for work style, communication, and problem-solving.
The result: Manual sourcing dropped from days to seconds, and screening interviews were largely eliminated.
The takeaway: Speed to a working product comes from method, not from cutting corners. Five days was possible because the problem was scoped to a clear outcome before any code was written.
Case Study 2 — A Diagnostics App Rebuilt for Self-Service in 4 Weeks
Sector: Digital health diagnostics
The problem: A diagnostics provider’s legacy app was outdated and hard to use. Patients struggled to find tests and track their results.
What we built: A mobile-first, self-service app for browsing, booking, and viewing results — plus an AI assistant that recommends tests and completes bookings through conversation.
The result: The prototype was delivered in under two weeks and went on to be featured at a major AI healthcare exhibition.
The takeaway: A lot of AI value sits in rebuilding the workflow around the user, not just in the model itself. The assistant only worked because the underlying experience was rebuilt first.
Case Study 3 — SalesGenius.ai: An AI Sales Agent, Live in Weeks
Sector: B2B sales (live product)
The problem: Manual prospecting was slowing sales teams down — hours lost to research before any selling happened.
What we built: SalesGenius replaces manual prospecting with AI-driven research, personalised outreach, and automated follow-ups.
The result: An 80% reduction in time spent on outbound research, with pipeline generated from day one.
The takeaway: This is a live product with real users today — not a demo. It is direct proof that the delivery model produces durable systems, not throwaway prototypes.
Case Study 4 — ApplyGenius.ai: Zero to Launch in Under 30 Days
Sector: Careers / recruitment (live product)
The problem: Job seekers were spending too much time tailoring CVs and cover letters for every application.
What we built: ApplyGenius automates CV rewriting, generates personalised cover letters, and provides real-time application coaching.
The result: Built and shipped from zero to launch in under 30 days, with active users.
The takeaway: A second live product that proves the 30-day-to-production claim is repeatable, not a one-off.How quickly can AI Navi build a working AI product?
Our live examples have shipped in as little as five days for a working prototype and under 30 days for a fully launched product with active users.
Are these real, live products? Yes. SalesGenius.ai and ApplyGenius.ai are live products with real users today. The talent-matching and diagnostics builds were delivered as working prototypes for clients.
The Pattern Behind All Four: Navigate, Execute, Land
Four different sectors, four different use cases, one common thread and it is not the technology. It is the delivery system.
Every build followed the same three-pillar method. Navigate: the work started with a clear outcome and leadership alignment, not a tool. Execute: a data foundation was put in place and a real system was shipped to production, not a prototype left on a shelf. Land: the product was built around how people actually work, so it got used.
That repeatable system is why a five-day prototype and an under-30-day launch are not lucky exceptions. They are what happens when strategy, data engineering, and change management are delivered by one embedded team instead of handed off between three.
What This Means for a UK Mid-Market CP/FMCG or Logistics Business
These four builds are deliberately varied to show the delivery model works across very different problems. The same approach applies directly to the operational challenges mid-market Consumer Products and Logistics leaders face every day.
For a logistics operation, that can mean connecting fragmented operational data and putting working AI against routing, inventory, and cost-per-delivery the kind of foundation that creates the opportunity for 5–10% cost-per-delivery savings. For a CP/FMCG business, it can mean AI that supports demand forecasting, promotional analysis, and margin protection. The point is not the specific number; it is that a working system in production, owned by your team, is achievable in weeks rather than the 4–6 month mobilisation cycle agencies quote.
The leaders who win with AI will not be the ones with the biggest budgets. They will be the ones who build the right thing.
How to Get Started
If you want to know where your AI programme actually stands, the fastest first step is AI Navi’s free AI Readiness Scorecard. It takes three minutes, benchmarks you against comparable UK mid-market businesses, and comes with no pitch attached.
If you are ready to move, the AI FlightCheck diagnostic produces a board-ready 90-day action plan — and flows directly into the AI FlightPath Sprint, where your first working AI goes into production.
Book a Discovery Call with AI Navi → Ask us about the AI Check.
Frequently Asked Questions
What results has AI Navi delivered?
Demonstrated outcomes include an 80% reduction in outbound sales research time, manual freelancer sourcing reduced from days to seconds, a diagnostics app rebuilt in under two weeks, and a product taken from zero to launch in under 30 days.
Does AI Navi only build SaaS products, or also AI for FMCG and logistics operations?
Both. The same delivery model applies to operational AI in Consumer Products and Logistics — demand forecasting, inventory, routing, and cost-per-delivery — underpinned by data engineering.
How is this different from an agency that delivers a strategy deck?
Agencies typically deliver an 80-page strategy with no implementation, over a 4–6 month cycle. AI Navi delivers a working system in production in weeks, with strategy, data engineering, and adoption handled by one embedded team.
How do I find out if my business is ready?
Start with the free AI Readiness Scorecard — three minutes, instant benchmark, no pitch.\