Work

Details anonymized. Client name and identifying specifics withheld. Boutique Wildlife Resort · Central India · 6 weeks, ongoing

A small resort a short walk from the gate of one of India's best-known tiger reserves had the location, rooms and food that safari travellers look for, but travellers had no easy way to find it. The website was a single page with no safari information, no distances and no prices. There were no booking-site listings. And searching for the resort's name brought up unrelated properties with the same name in other states.

Before touching the design, we audited the competing resorts around the same reserve, page by page, to see what they rank for and what they miss. Then we ran Surface Demand Radar, the market-intelligence tool we built, across the searches travellers actually make. It found search terms that no resort in the area was competing for, and we later built the Google Ads campaign on them.

Over six weeks we rebuilt the website from one page to a full site with rooms, dining, a safari guide, FAQs and a travel blog, structured so Google and AI answer engines can understand it. We also cleaned up conflicting business details across the web, launched the resort on two major booking platforms, and set up full tracking before the first ad ran. After that we built a Google Business Profile posting calendar, made the ad creatives and reels ourselves, and set up a simple lead sheet that follows each enquiry from ad click to quotation to booking. Every enquiry gets its own personalised quotation.

The first result came before any money was spent on ads: a large group found the resort through the new website and booked the entire property. The opening weekend of the season then sold out through the booking-platform listings we set up. Once the ads went live, enquiries started coming in on the first day and have kept coming since.

• A competitor audit and demand mapping came before any design or ad decision.
• Analytics, tag manager, Meta Pixel and Conversions API were live before the first campaign.
• Every enquiry is logged from first click to confirmed booking, so each result can be checked against a real record.

Live

Knowing what to write next is harder than writing it. We built a tool that reads everything a client's competitors have recently published, studies it, and hands back a content roadmap: what to write, in what order, for the next 1, 3, or 6 months. It's running on a real client, not a demo, producing a roadmap they execute.

• Kills the blank page. Research that took 2–3 hours per client now runs in minutes.
• Every client saved as a profile, rules and all: "never mention price" stored once, respected in every output.
• Shows its homework. Every idea is grounded in real articles competitors actually published, plus the gaps nobody's writing yet.

Details anonymized. Client name and identifying specifics withheld by agreement. Premium Surfaces (Tiles, Quartz, Marble) · Hyderabad, India · 2 months, ongoing

A premium design and surfaces studio in Hyderabad already had steady inbound interest through Google. People were finding them and calling. What was missing was proof: nothing online showed they were the specification-grade expert serious architects and designers trust, not just another tile shop.

We didn't take that gap on faith. We checked it ourselves. Instagram against comparable local studios showed reels pulling a fraction of the views similar accounts got with similar content. Their Google Business Profile carried a fraction of the reviews some direct competitors carried. And a market-intelligence pass across search, Maps, and AI answer engines, run with a tool we built for exactly this, showed no local dealer in the category, this studio included, had a single featured snippet or AI citation, even with competitors running near-perfect ratings and hundreds of reviews. Not a hunch. A mapped gap.

We built five connected systems off that gap: WhatsApp, so every enquiry gets a fast, verified reply; a founder LinkedIn presence giving his decade of expertise a public voice; a Reels-first creative system built around why the old posts weren't landing; Google Business Profile content built on real competitor and review data instead of guesses; and a walk-in intake form. That last one came after we realised sales from this activity couldn't be confirmed, because nothing at the showroom counter was logging them, so that exact gap doesn't repeat.

Two months in: WhatsApp, founder LinkedIn, and Google Business Profile are live and producing measurable activity. A full paid-media plan (channel mix, cost-per-lead benchmarks, month-by-month projections) is built and ready, pending budget sign-off. A 1,000+-contact architect and developer relationship database is built with a consent-first structure: nothing goes out until the client approves who, what, and how often.

• Every enquiry now gets a fast reply from a verified, blue-tick WhatsApp number, so it reads as a professional studio, not a random shop
• The founder's decade of hands-on expertise now has a public voice on LinkedIn, with consistent posting for two months that's grown his following by roughly half
• Audited Instagram against comparable local studios, found reels underperforming by a wide margin, and built a Reels-first creative system (shot list, content pillars, KPIs) to close that gap
• Google Business Profile content shifted from guesswork to evidence, with calls emerging as the strongest-converting contact method, enough to shift this month's content towards driving calls specifically
• When sales from that activity couldn't be confirmed because nothing was logged, fixed the gap at the source: a live walk-in intake form now records every visit's project type, budget, timeline, and referral source

The discovery data behind that activity: tens of thousands of profile views and search impressions, split roughly two-thirds mobile search and the rest Google Maps, with branded search terms making up most of the volume, exactly what you'd expect once local content starts working.

Self-initiated. Already proven on a real market. Market Intelligence · Any City, Any Category · First edition: Hyderabad, Premium Surfaces

Working on the Premium Studio of Surfaces project, we hit a wall: the studio's team could tell us GMB activity was converting into sales, but nothing at the counter was logging it, so we couldn't confirm exactly how much. Rather than build another tool that depends on a client's staff logging data (that had already failed once), we built one that needs none of it.

Surface Demand Radar takes a city and a category, nothing hardcoded, and shows a showroom what buyers already search, see on Maps, praise, complain about, and whether AI answer engines like ChatGPT, Perplexity, and Gemini even mention them or their competitors, before a rupee gets spent on ads. Every finding is tagged observed, inferred, or unknown against its source, so a claim never outruns its evidence.

The first real edition ran on Hyderabad's premium surfaces market, the same market the studio above competes in. Its findings, real competitor review complaints and unclaimed search territory, went straight into that studio's Google Business Profile content and produced a visible, same-week jump in profile activity. Broader release, self-serve requests and a paid tier, is the next phase.

• 100% public data: no client account access, no CRM, no dependence on anyone logging a lead correctly
• Built on the same collector → analyzer → report pipeline already proven on two other tools, not reinvented from scratch
• Checks whether AI answer engines even mention a business, not just where it ranks on Google, a channel almost no local competitor is contesting yet
• Every claim is evidence-tagged: observed fact, inference, or acknowledged unknown, so nothing gets published on a hunch
• Its first real output already changed a real showroom's content and moved a real number, before the product itself was ever sold