Case Studies
Proof, not promises.
Every case below is real, documented, and approved for publication. Client studies are anonymised by default — sector and substance, not name-dropping. Some numbers await client sign-off; we publish nothing we can't stand behind.
The Work
What we've built, problem to outcome
Marketing technology
Multi-tenant brand-to-website engine
- Problem
- Getting a complete, SEO-ready website for every brand normally means a separate design and development project per brand — weeks of work each time, and content updates that need a developer.
- What we did
- We built a multi-tenant rendering engine (Next.js + Supabase) that turns structured brand data — identity, tone, pages, media — into a full multi-page website: seven section types, two visual templates, per-page SEO metadata, sitemap and robots, and AI-generated on-brand imagery.
- Outcome
- Any brand managed in our platform gets a complete, indexable website rendered directly from its brandbook data, and new content such as blog articles publishes to the live site without touching code. This engine powers gravitnomad.com itself.
Professional services
The concierge you are talking to right now
- Problem
- Generic website chatbots either hallucinate answers or know nothing specific, so visitors with real questions leave without answers — and the company never learns what those visitors actually needed.
- What we did
- We built this site's own AI concierge: hybrid retrieval (vector + full-text) over the entire site content, persistent visitor memory across the conversation, honest "the site doesn't cover that" behaviour when retrieval is weak, and deterministic lead-intent scoring that hands hot conversations to a human.
- Outcome
- Every answer is grounded in real site pages with sources, returning visitors are remembered, and high-intent conversations reach our team immediately over WhatsApp — the same pipeline serving this exact conversation.
Content operations
One brief, three channels: automated publishing hub
- Problem
- Publishing one announcement across LinkedIn, a blog and Facebook meant reformatting the same content by hand in three different tools, with no central record of what went out where.
- What we did
- We connected our channels to a single authenticated machine-to-machine bus (around ninety tools), so one brief is adapted per channel and published to LinkedIn, the blog and Facebook from a single automated flow, with every delivery response logged centrally.
- Outcome
- One approved brief propagates to all connected channels without manual copy-paste — and the same hub delivers the WhatsApp lead alerts used by this website's concierge.
Educationanonymised client
An AI Control Tower for an education group
- Problem
- A private education group with multiple schools ran its operation on data scattered across systems that didn't talk to each other — leadership had no unified view, and decisions relied on manual, outdated reports.
- What we did
- Architecture design for an AI "Control Tower": a single layer aggregating operational and academic data and exposing it to decision-support agents, with clear governance over who sees what — designed in partnership with AI and edtech specialists.
- Outcome
- Full architecture and implementation proposal delivered to leadership — the blueprint that turns scattered data into decisions with context.
SaaS / E-commerceanonymised client
From tool to revenue engine: AI agents inside a B2B SaaS
- Problem
- A B2B SaaS with over a hundred active accounts faced rising churn and a team too small to track every risk signal — at-risk customers were only spotted when it was already too late.
- What we did
- An AI-agent architecture built on the operation's real data: deterministic SQL health-scoring (four risk tiers, with mandatory human gates before any action), a retention agent that prioritises at-risk accounts, and an AI voice agent for outbound contact — including the latency diagnosis and fix that made the conversation feel natural.
- Outcome
- Risk detection went from reactive to automatic and daily; the voice agent's response time dropped by over a second per turn after the endpointing diagnosis — the difference between a robotic call and a conversation.
Insuranceanonymised client
Process modernisation at an insurance company
- Problem
- An established insurer ran on undocumented core processes and historical data locked in legacy systems — critical knowledge lived in people's heads, and every modernisation or audit effort hit the same wall: nobody had the map.
- What we did
- Structured documentation of core business processes (underwriting, claims, policies) and migration of legacy data into a coherent, auditable base — the silent prerequisite for any automation or AI project the company wants to run next.
- Outcome
- Core processes documented end-to-end and data migration completed — the organisation now holds a single, auditable source of truth for its own operation.
Case Study Zero
This site is one of them
The assistant in the corner of this page is a production agentic AI system we built for ourselves: retrieval over every page and article on this site, persistent memory, deterministic lead scoring, and real-time handoff. Ask it about this page — or about your own project.
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