Every project below is our own product. We built each one because we hit the problem in our own operation first, which is why we can tell you exactly what it does and exactly where it stops.
No client logos and no testimonials on this page. Client work here is mostly under confidentiality, so instead of a wall of marks we cannot substantiate, this is work we own and can talk about in full, including the parts that do not work.
An inbound agent that reads a real WhatsApp or email enquiry, qualifies it against your criteria, handles the ordinary objections and books the meeting, in seconds rather than hours.
Most inbound leads are lost to response time, not to price. The business that replies first usually wins the meeting, and a human team cannot cover evenings, weekends and the gap between a campaign going out and someone opening the inbox.
Multi-tenant SaaS. A classifier model handles intent and guardrails, a larger model writes the replies, and anything it is not confident about is escalated to a person rather than guessed at.
It does not close deals. It qualifies and books. If your sales process depends on a long consultative first conversation, this shortens the path to that conversation rather than replacing it.
Prospect discovery, personalised sequencing, reply classification and hot-lead escalation, in one system instead of four subscriptions taped together.
Outbound tooling is usually assembled from a data vendor, a sequencer, an inbox warmer and a CRM, none of which agree with each other. The reply handling is where it breaks: a positive reply sitting unread for a day is the same as no reply at all.
Multi-tenant, FastAPI and React on Supabase. Model calls route through a provider-agnostic layer, so the cheap classification work and the expensive writing work do not have to run on the same model.
It is not a database of leads you can buy. It works your market and your criteria, and it will not rescue an offer the market does not want.
Audits how AI assistants find, read and cite a business, then fixes what is blocking them, measured against a score you can re-run.
Assistants increasingly answer the question instead of listing links. Being invisible to them is a different failure from ranking badly, and most sites are invisible for dull mechanical reasons: crawlers blocked in robots.txt, no entity data, and content that never plainly answers the question being asked.
Crawler access checks, entity and schema modelling, content structure scoring, then a re-audit. The output is a numeric score with the log attached rather than a narrative report.
It cannot make an assistant cite you for a claim you cannot support. It fixes discoverability and structure. The substance still has to be there.
Strategy, scripting, shoot and edit, run in-house on gear we own rather than subcontracted and marked up.
The standard agency arrangement splits strategy, shoot and edit across three vendors, and the brief degrades at every handoff while nobody owns the result.
One team from brief to publish. Consistent grading across a series, so episode nine looks like episode one.
We are a studio, not a broadcast production company. Large-crew commercial shoots with big talent budgets are not our shape.
Ingests long-form footage, finds the moments most likely to hold attention from transcript analysis, and cuts platform-ready verticals with captions.
An agency came to us already paying a heavy recurring per-seat fee for a well-known clipping tool and wanted the capability built for them and owned outright. At volume, a subscription priced per seat per month stops competing with a system you own, and the build pays for itself against what the licences already cost.
Transcription, segment scoring, cutting and captioning, running on infrastructure the owner controls, so footage never leaves it. The scoring model is tuned to the client's content rather than an average of everyone else's.
It finds candidate moments. If the underlying footage is not interesting, no clipping engine rescues it, and a human still reviews before anything publishes.
A property platform built for a single brokerage and owned by them: listings, agent profiles, enquiry routing and an editorial layer, on their own database.
Brokerages in this market are usually renting a portal that also serves their competitors, on terms that make their own listing data hard to get back out. Owning the platform changes who the audience belongs to.
Single-tenant by design. One brokerage, one database, one set of settings, deliberately not a multi-tenant product with a client switcher bolted on.
It is a one-off build per client, not a SaaS subscription. That means a real project cost up front, which is the right trade only if you intend to own the platform for years.
Because they are the ones we can describe honestly and in full. Client work in this market is mostly covered by confidentiality, and we would rather show you systems we built, run and can talk about in detail than publish a logo wall and an unattributed quote. Everything on this page is live and most of it runs our own business day to day.
Not on a public page, but yes on a call. We will introduce you to someone running a comparable system rather than publish a testimonial you have no way to verify.
Yes, and it is the model we prefer for anything that becomes core to how a business runs. The Real Estate Intelligence Engine and OmniClip were both commissioned that way. You own the code and the data, and the economics beat a per-seat subscription once you are past a certain volume.
It depends on the surface area, but the honest range for a system of the size described on this page is measured in weeks to a few months, not days. Anyone quoting you days for this is quoting you a prototype.
The first conversation is diagnostic. If an off-the-shelf tool would serve you better than a build, we will say so, and you will have saved a budget cycle finding out.