AI products, engineered for the real world.
We help founders, SaaS companies, and product teams turn AI opportunities into reliable, commercially viable products.
One team owns the product, the AI, and the system it runs on
Most AI work stalls somewhere between a working demo and a system a business can depend on. Everything about how the studio is set up is aimed at closing that gap.
See what we do- Team
One team, no handoffs
Product, AI, and backend engineering sit in the same team. There is no account layer between you and the people writing the system.
- Scope
Concept to production
We carry a product from problem definition through architecture and delivery, then keep watching how it behaves: quality, latency, cost, reliability.
- Depth
More than the model
The model is one component. We also build the APIs, data pipelines, and multi-tenant platforms it runs on, which is where most AI projects actually stall.
- Independence
No lock-in, by design
Models, vendors, and infrastructure are chosen for the problem and built so they can be swapped when something better ships.
Three ways to start, one accountable team
Pick the one that matches where you are. These are entry points rather than stages, and each opens onto the engineering underneath it.
Build a new AI product
You have a product to build, from first validation through launch.
Add AI to what you have
You know the capability your product needs, and it has to be built properly.
Make it hold up in production
It works in a demo. Now it faces real users, real cost, and real load.
Systems you can use, and products we have shipped
Client work is the proposition. Two of these are live demonstration builds you can open right now; the rest are representative examples of what we design and build.
Aurelia Properties
Conversational property advisorA Dubai estate agency site with an assistant, Ayla, that answers questions against the live listing set and qualifies an enquiry before a human picks it up.
Web chat, embedded in the site and in each listing
Cedarline Home Services
Multi-channel AI agent suiteA seven-trade contractor with agents on web chat, WhatsApp, voice, and email, all answering from the same coverage, scheduling, and pricing rules the website publishes.
Web chat, WhatsApp, voice, email
A grounded knowledge assistant for a revenue platform
Support and sales teams could not find answers across scattered docs, tickets, and product data.
Median answer time reduced from minutes to seconds across a high volume of monthly queries.
Distinctive. Graph and vector retrieval with strict permissions and inline citations for every claim.
An inbound voice agent for high-volume scheduling
Missed and abandoned calls were losing bookings during peak hours.
Automated resolution of most routine inbound calls, with clean handoff to staff.
Distinctive. Low-latency speech orchestration wired directly into the existing scheduling and CRM stack.
From MVP to a production-ready product in one quarter
A promising prototype had no evaluation, no monitoring, and no path to reliable scale.
Shipped a monitored, evaluated production system ready for the first paying cohort.
Distinctive. Evaluation harness and cost controls built in from the first architecture decision.
One operating model, from idea to production
A single, dependable path that carries a product from a defined problem to a monitored system running in production.
- 01
Define
Clarify the product, the user problem, the commercial case, and whether AI is the right fit.
- 02
Design
Create the product experience, system architecture, data model, and evaluation approach.
- 03
Build
Implement the product, backend, AI workflows, integrations, and infrastructure.
- 04
Operate
Monitor quality, latency, cost, reliability, and product adoption in production.
Product builders, not presentation consultants
We are engineers who ship and operate real software. That shapes every decision, from architecture to what we choose not to build.
Where the engineering gets tested first
Client work is the proposition. We also run a product of our own and two Valtair Labs experiments, which is where architecture choices, cost controls, and evaluation approaches get proven before a client depends on them.
LeadVector
AI sales intelligenceSales teams waste hours qualifying accounts by hand instead of talking to buyers who are ready.
For B2B revenue and sales teams
AI Visibility
Answer engine optimisationBrands lose visibility as buyers move from search results to AI-generated answers they cannot measure or influence.
For Marketing and content teams
AI CMO
Autonomous marketing operationsEarly-stage teams need senior marketing judgement and execution long before they can hire for it.
For Founders and lean marketing teams
Field notes from building AI products
Practical engineering and product thinking, not daily AI news.
Evaluating grounded answers in RAG systems
A practical approach to measuring whether a retrieval system is actually answering from its sources.
How we kept LeadVector's inference costs predictable
The routing, caching, and evaluation decisions that stopped model spend from scaling with usage.
Designing voice agents for latency people can live with
Where the seconds go in a voice pipeline, and the architecture choices that get them back.
Practical intelligence for people building with AI.
A concise briefing on important AI developments, product engineering lessons, emerging use cases, and what technical and product teams should do next.
Twice per month. No noise.
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