Engineered for the real world.
We design, build, and run software for businesses, from AI products and voice agents to the systems and automations behind them.
The people who design it are the people who build and run it
Most projects stall somewhere between a working demo and a system a business can depend on. Everything about how Valtair is set up is aimed at closing that gap.
See what we do- Team
One team, no handoffs
Product, engineering, and AI specialists work in the same team. There is no account layer between you and the people building the system.
- Scope
Still there after launch
We take a system from problem definition through architecture and delivery, then keep watching how it behaves in production: quality, reliability, and cost.
- Depth
The whole system
The interface is the part people see. We also build the APIs, data pipelines, integrations, and platforms underneath, which is where most projects actually stall.
- Independence
Chosen for the problem
Models, vendors, and infrastructure are chosen for the problem, and built so they can be swapped when something better comes along.
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 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.
Built for how your industry works
The same engineering, shaped around the workflows, systems, and customers of a specific industry.
All industries- HealthcareAI voice and chat that turn missed calls into booked appointments for US medical, dental, chiropractic, and physical therapy practices.
- Real estateVoice and chat agents, viewing booking, CRM, and lead automation for portals, developers, agents, and rental and leasing businesses.
- Home servicesVoice and chat agents, job booking, and follow-up automation for trades and home services businesses.
Systems you can use, and the kind of work we build
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 demo site for a fictional Dubai estate agency, 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 fictional 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.
Cited answers in place of searching across scattered sources.
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.
Routine inbound scheduling calls handled by the agent, 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
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
Alongside client work, we 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.
Voice AI agent architecture: from phone line to response
The layers of a production voice agent, what each one does, and how to choose between a cascaded pipeline and speech-to-speech.
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.
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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