We turn hard problems into production software.
AI strategy, software engineering, and our own SaaS products. We come in at any point between the first architecture decision and the system running under load — and we have walked that whole line ourselves, on our own product.
Hazri Book is in production in Saudi Arabia. Agent Labista is in development. Engineering built for the GCC, India, the UK, Singapore and the US.
From technology strategy to software that ships.
One delivery line, three places to join it. Most companies come in at one point and stay there; some walk the whole thing with us.
Work out what the technology is actually for.
Architect the system, and the AI inside it.
Engineer it to production standards.
Ship it, integrate it, put real users on it.
Run it, watch it, keep improving it.
- Think
Work out what the technology is actually for.
ConsultProduct - Design
Architect the system, and the AI inside it.
ConsultBuildProduct - Build
Engineer it to production standards.
BuildProduct - Deploy
Ship it, integrate it, put real users on it.
BuildProduct - Operate
Run it, watch it, keep improving it.
Product
AI & IT Consulting
Work out where technology creates a real advantage — and, just as often, where it does not.
Software Development
Take a complex idea from architecture through to a system carrying real load, real users and real money.
Proprietary Products
Software we designed, built, shipped and still operate — which is why we know what the last two stages actually cost.
Deep engineering. Practical outcomes.
We work across the whole stack, but the part that decides whether a system survives is rarely the part on the brochure. It is isolation, evidence, and what happens when the network drops.
Intelligence
Where a model earns its place in the architectureProduct surface
What the customer's customer actually touchesCore systems
The part that has to be right at three in the morningPlatform
How it gets built, shipped and watchedThese are not features bolted on before launch. They are decisions made in the first week, because retrofitting any of them means rewriting the data model.
- Multi-tenancy
- Audit trails
- Data residency
- Access control
- Cost ceilings
We don't just build software. We build products.
One is in production in Saudi Arabia, carrying a real payroll deadline every month. The other is still in the lab. We build few, and we say which is which.
Agent Labista
AI sales agent for real estate
Every WhatsApp enquiry answered the minute it arrives, in the buyer's own language, from the builder's own brochures — then qualified, scored and booked into a site visit.
- WhatsApp Business API, live on the builder's own number
- Eight languages, matched to the buyer's script and register
- Answers grounded in the builder's documents, with citations
- Progressive lead capture — name, area, budget, timeline
- Site visits booked into the sales team's calendar
- Source attribution preserved across direct, CP and paid channels
Hazri Book
Verified attendance for field workforces
Replaces the paper hazri register. Every shift carries a photo, a GPS reading and a server-set timestamp — and anything missing is flagged on the record rather than quietly dropped.
- Photo, location and server-set time captured at every punch
- Gaps flagged as exceptions, never silently discarded
- Works offline, syncs when the site gets signal
- Arabic and English, right-to-left throughout
- Overtime authorised before it is payable
- Month-end lock and payroll-ready exports
More products are in the lab. We announce them when customers are using them, not before.
One system, still running.
Described the way we would describe it to another engineer: the problem, what we decided, and what changed. One entry, because one is what we can evidence.
Turning an attendance register into evidence
A register records a claim. It does not record a fact.
A contracting company's payroll ran on a paper hazri register and a supervisor's memory. Attendance was routinely marked from the site office rather than at the gate, overtime was agreed after it had been worked, and every month-end produced the same argument: hours the company could not prove and the client would not pay for.
In production, not a pilot. The company closes the month on records that carry their own proof, and overtime is authorised before it becomes payable rather than argued about afterwards.
Six reasons that are actually specific to us.
Not quality, not customer focus, not an experienced team. Those are table stakes claimed by everyone and verified by no one.
Business first
We start with the problem and the money attached to it. The technology decision comes second, and sometimes the answer is that you do not need the technology.
Engineering depth
Multi-tenant isolation, offline sync, audit trails, data residency. The unglamorous parts that decide whether a system survives its second year.
AI-native, not AI-decorated
A model goes into the architecture where it creates leverage that conventional code cannot. Everywhere else we write ordinary, boring, reliable software.
End to end
Strategy, architecture, engineering, deployment, operation. We can take any part of that line, and we can take all of it.
Product mindset
We run our own SaaS in production, in regulated markets, for paying customers. We have been on the wrong end of our own architecture decisions and it changes how we make them.
Built for more than one market
Arabic and right-to-left, Gulf labour rules, Indian payment and compliance regimes, data residency, eight-language conversational AI. Already built, already in production.
Five stages. No mystery in any of them.
You will always know which stage we are in, what comes out of it, and what it costs. Engagements go wrong in the gaps between stages, not inside them.
- 01
Discover
Understand the business problem, the constraint behind it, and the outcome that would count as success. Written down, agreed, and short.
- 02
Architect
Design the system — data model, service boundaries, where AI belongs, where it does not, what it runs on and what it costs.
- 03
Build
Engineer it. Tests, review, CI, observability from the first commit rather than bolted on before launch.
- 04
Launch
Deploy, integrate with the systems already in place, and put real users on it. The first week in production teaches more than the previous month.
- 05
Evolve
Watch it under load, fix what the real world found, and keep shipping. Or hand it over cleanly, documented, to your own team.
Built in India. Designed for the world.
Our engineering day covers a Gulf working day almost end to end and reaches a UK morning. That is the reason to be here — not the rate card.
- United StatesUTC-5
- United KingdomUTC+0
- Saudi ArabiaUTC+3
- UAEUTC+4
- IndiaUTC+5.5
- SingaporeUTC+8
In production. Our own products serve customers here today.
Built for. Language, compliance and data-residency work already done — not a claim about customers.
Arabic is not a translation layer
Right-to-left through the whole interface, Hijri-aware reporting, and Gulf labour rules in the overtime engine rather than bolted on top of generic defaults.
Eight languages, matched not translated
Devanagari, Roman-script Hinglish, Tamil, Telugu, Gujarati, Marathi, Arabic and English — the agent holds the buyer's register across the whole conversation.
Residency is a configuration
Where the data lives, who may read it and how long it is kept are deployment decisions, because in the GCC and India they have to be.
Have a problem worth solving?
Tell us what you are trying to build, automate or improve. We will work out the technology with you — and say so if the honest answer is that you do not need us.
Or write to contact@astalabista.com. A person reads it, and replies within one working day.