All 4 AI Deployment Case Studies in One Place - From Audit Tooling to Continuous AI-Visibility Monitoring
Synthesised.ai builds production AI systems from a client’s own expertise, not from a generic template, and these four case studies demonstrate the method across four different businesses: a search-visibility audit tool, a multi-tenant content platform, a merged revenue-and-operations platform, and a continuous AI-visibility monitor, each with a client-reported outcome.
Three of the four engagements, LLM MediaOS, Perispark and RefinedAI, are named with each client’s written consent; the search-visibility consultancy is not named.
Four Systems, Four Client-Reported Outcomes
| Case study | Client type | The gap before Synthesised | Client-reported outcome (not independently measured) | Delivery |
|---|---|---|---|---|
| Search-visibility audit tool | Search-visibility consultancy (Australia, the Pacific, and Asia) | Couldn’t audit enterprise sites for SEO, AEO, and GEO at scale | Client-reported estimate: ~12 hours saved per prospect audit, 60%+ cost cut against off-the-shelf tools | 12-week build |
| LLM MediaOS: multi-tenant content platform | Digital-marketing and content-production operation | Content production spread across disconnected point tools per client account | Client-reported estimate: ~80% production-time cut per finished piece | 13-week build |
| Perispark: revenue-and-operations platform | Management consultancy advising local service businesses | Advice with no infrastructure for clients to act on it | Client-reported estimate: ~200 consultant hours returned monthly across a 20-client roster | 10-week build |
| RefinedAI: continuous AI-visibility monitor | Management consultancy advising mid-market and enterprise brands | No systematic in-house way to measure how AI assistants describe clients to buyers | Client-reported: a day of manual checking replaced by an unattended sweep costing under a dollar in API and compute per sweep | 12-week build |
Every system below started the same way: Synthesised.ai encoded the client’s own working method into software, rather than selling them a generic off-the-shelf tool. The examples below describe systems delivered for clients. Outcome descriptions draw primarily on qualitative client feedback and have not been independently verified through a consistent before-and-after measurement process. Each specific figure is client-reported or a modelled estimate, labelled as such at its point of use below, and none is an independently measured result.
Search-Visibility Audit Tool
A search-visibility consultancy serving enterprise clients across Australia, the Pacific, and Asia had no way to audit prospect and client websites at scale for SEO, AEO, and GEO, whether sites were set up to rank in traditional search engines and in AI answer and generative engines such as ChatGPT, Perplexity, Claude, and Gemini. In the firm’s own workflow at the time, that gap blocked it from prospecting new clients, setting benchmarks, or diagnosing problems on sites running into the hundreds or thousands of pages. Synthesised.ai built a custom audit tool directly from the consultancy’s own SEO, AEO, and GEO methodology, combining a mix of web crawlers chosen for cost and block-resistance with deterministic Python components and LLM components used only where language interpretation genuinely mattered. The tool ingests thousands of pages from a CSV input, pulls standard site-quality metrics including Core Web Vitals, and scores structure and content extractability for answer- and generative-engine purposes, alongside an LLM prompt-tracking system that benchmarks share of voice and flags brand hallucination across AI assistants. The consultancy reports that the finished system saves about 12 hours per prospect audit and cuts costs by more than 60% against off-the-shelf tools (client-reported estimates, not independently measured), and it gave the consultancy a proprietary scoring layer built around its own definition of site quality rather than a vendor’s generic rubric, delivered across a 12-week build from stakeholder scoping through to production handover.
LLM MediaOS: Multi-Tenant Content Platform
A Singapore-based digital-marketing and content-production operation serving a multi-brand client roster across APAC ran AI-assisted content production as a chain of manual handoffs across disconnected point tools per client account, with no context carried from research into the brief and no context carried from the brief into the finished visuals. Synthesised.ai turned that production method into a multi-tenant, white-label platform: a drag-and-drop canvas encodes the research-to-brief-to-visual pipeline as the product’s canonical workflow, an AI gateway makes 100+ text, image, and video models available behind one metered bill (as configured at deployment), and isolated, custom-branded workspaces let one platform serve every client account instead of multiplying per-seat subscriptions. The client reports that production time per finished content piece fell by roughly 80% (a client-reported estimate, not independently measured), and one platform replaced the client’s per-seat AI tool stack with a single priced, attributable margin line per account, delivered across a 13-week build. Beyond the numbers, the client’s proprietary production method, which research feeds which brief, how brand governance constrains generation, now exists as their own branded platform rather than as billable hours run through other vendors’ tools.
Perispark: Revenue-and-Operations Platform
A management consultancy advising local service businesses on revenue and operations kept hitting the same wall: the advice was sound, but clients had no infrastructure to act on it, so consultant hours went into per-client dashboard exports and website audits instead of advisory work. Synthesised.ai built a single platform that productises the consultancy’s own methodology in two connected layers: an automated crawl-and-rebuild pipeline that restructures a client’s website into a governed, AI-discoverable content schema, and a conversational revenue analyst that syncs ad-platform data hourly and enforces the consultancy’s own reporting discipline, plain-English first and money-quantified, with metrics resolved to approved source fields so an unsupported request returns an error rather than an invented number. The two layers connect directly: when the analyst surfaces a finding that implies a website change, that change goes from insight to AI-drafted edit to one-click owner approval on the same platform, instead of waiting weeks on a developer queue. The platform returns roughly 200 consultant hours monthly across a 20-client roster (a client-reported estimate, not independently measured) and surfaces a modelled opportunity to recover an estimated 5-10% of managed ad spend through continuous auditing, a modelled estimate reported by the client rather than a reconciled realised saving, delivered across a 10-week build. The consultancy’s own rebuild standards, reporting discipline, and approve-before-execute governance became the product’s enforced defaults, not a documented process someone has to follow by hand.
RefinedAI: Continuous AI-Visibility Monitor
A management consultancy advising mid-market and enterprise brands on growth and positioning was watching a core deliverable lose ground: buyers had started asking ChatGPT, Claude, Gemini, and Perplexity who to shortlist, and the firm’s own available method was a consultant manually typing questions into each assistant and screenshotting the answers, a method that produces no trend line, no competitor baseline, and no defensible number for a client presentation. Synthesised.ai built a continuous AI-visibility monitoring platform that encodes the consultancy’s own review discipline, what counts as a mention versus a citation versus a genuine recommendation, into an always-on measurement system running on a 15-minute scheduling cadence across four AI assistants. Every check stores its exact prompt, model, response, and a versioned methodology fingerprint, so any number in a client report traces back to the precise question and method that produced it. A monitoring sweep that once took a consultant the better part of a day now runs unattended for well under a dollar in API and compute cost per four-assistant sweep (excluding fixed infrastructure; as reported for the deployed configuration, not independently measured), and the platform’s first production runs surfaced a previously unproven finding: within the tested prompt set, models and dates, a tracked brand was not recommended by any of the four assistants for its core category questions in two national-market configurations. The build ran 12 weeks from measurement-standard scoping to production monitoring going live.
Relay: Conversational AI CRM
Relay is a conversational AI build by Synthesised.ai that brings WhatsApp, Slack and Gmail into one CRM view of each customer. The AI answers WhatsApp enquiries from documents supplied as its source of truth, runs more than one AI agent across the connected numbers, and hands the conversation to a person when it lacks the information to answer. Email replies are planned and are not live yet. Relay sits outside the four case studies above because it has no client-reported outcome to publish yet.
Where to Start by What You Need
Benchmarking or auditing prospects and clients at scale? Start with the search-visibility audit tool above. Running AI content production across many brand accounts? Start with the multi-tenant content platform. Advising local service businesses on revenue and operations? Start with the revenue-and-operations platform. Needing to know whether AI assistants recommend your clients? Start with the continuous AI-visibility monitor.
How These Four Systems Were Built
Every engagement above followed the same production method: scope the client’s own expertise and process first, make the architecture and model-selection trade-offs the client’s problem actually calls for rather than a default stack, design and build the workflow, then tune it against real data before handover. That sequence matters because it is the difference between automating a client’s business and turning the client’s own judgement into a product they can operate, sell, and improve without Synthesised.ai in the room. Every figure above is client-reported or a modelled estimate; the figures draw on qualitative client feedback and have not been independently verified through a consistent before-and-after measurement process, and no client publication permission is asserted here.
Ready to Scope Your Own Build
These four systems share one starting point: an AI Readiness Assessment that prioritises which parts of a client’s own process are ready to become software, and which need groundwork first.