AI
systems
built
for
the
data
you
actually
have.
Models, pipelines, and automations for teams who don’t have time for guesswork.
Build
Handover documented
[ 02 — What We Build ]
Four lines. One build standard.
same spec, same handover.
[ 02 ]
LLM Integrations
Retrieval and permissions layers that let a model work against live business data.
[ 04 ]
AI Consulting and Strategy
funding, build versus licence,
and in what sequence.
[ 03 — The Constraint ]
Most AI projects fail before the model does.
The hard part is the retrieval layer, the permissions model, and the approval chain, all of which have to exist before a model touches live business data. Without them, a model produces output somebody retypes. We build that layer first.
100+
CLIENTS DELIVERED
96%
CLIENT RETENTION
10 million+
RECORDS PROCESSED PER DAY
[ 04 — The Build Log ]
Two builds, documented.
standard: the decisions made under constraint.
[ 01 ]
Pakistan Customs
Centralized Command and Control
THE BRIEF
Unify three vehicle-tracking feeds into one national view.
THE CONSTRAINT
Falcon-i, V-Track and NLCSS each exposed a different schema. None could be changed.
The architecture
A normalisation layer ahead of the command platform, with an AI predictive engine for risk and anomaly detection.
The decision
We normalised on read rather than on write. Writing to a shared schema would have required three vendors to ship in step, and none of them answered to us.
The output
282 active trips monitored in one interface.
[ 02 ]
IoT-Konnect
Fleet platform performance
THE BRIEF
The fleet platform was slowing under its own volume.
THE CONSTRAINT
10 million+ records a day, against queries written for a schema that assumed thousands.
The architecture
Query paths rebuilt, indexes restructured, the API surface reduced.
The decision
We rewrote the query layer instead of sharding. Sharding would have moved the problem and added an operations burden the team could not carry.
The output
10 million+ records processed per day.
[ 05 — How We Work ]
Spec it. Build it. Ship it.
Five steps. The same five on every build, including the ones nobody sees.
01 · SPEC
We define exactly what is being built, for whom, and under what constraint, in writing, before anything else starts.
02 · BUILD
Engineers build
against the spec.
Scope changes get
logged and re-
quoted, not absorbed
silently.
03 · REVIEW
Work is reviewed
against the original
spec, not against
how it feels in the
room. Decisions
get recorded as
they’re made.
04 · SHIP
The system goes live
on the date set in
step one. If it can’t,
you hear that the
week it becomes
true, not the week it
was due.
05 · MAINTAIN
We hand off
documentation built
for whoever
maintains the system
next, including teams
that aren’t us.
[ 06 — The Standard ]
The standard is the brand.
Four things that hold on every build. None of them is a promise.
Documented by default
Every scope, decision, and trade-off is written down as it happens, not reconstructed for a final report.
No padded timelines
beating them.
Senior engineers, not account managers
The people writing the architecture are the people you talk to.
Built to be maintained
Every system ships with the documentation the next engineer, ours or yours, will actually need.
[ 07 — Who Make Us Stronger ]
Who Make Us Stronger
[ 08 — Talk to the team ]
Talk To An Engineer First
No sales call before the technical one. Tell us what you’re building, and the person who answers can speak to how we’d build it.
What happens next
01
An engineer reads the brief. Not a sales queue.
02
We come back with questions about scope, not a proposal.
03
You get a spec with line items, and a date we set from the scope.
[ 09 — FAQS ]
Frequently Asked Questions
Do you build models, or integrate existing ones?
Both. The choice is a cost decision, not a technical one: we compare build cost against licence cost at your volume, over three years. For most business problems a hosted model behind a retrieval layer we build is cheaper.
What happens if the data is not ready?
We say so in the spec, before the build starts. Incomplete, unlabelled or inconsistent data produces output nobody trusts, and that failure surfaces after launch. Where the data needs work, it is scoped as its own line item.
Who owns the model and the pipeline after handoff?
You do. The code, the weights where we trained them, the pipeline definitions, and the decision log. Every system ships with the documentation the next engineer needs, ours or yours.
How do you price an AI build?
Scope sets the price. We quote against a written spec, so the number reflects the actual build rather than a package tier. Ask for line items and you get them.
What if the use case turns out not to be worth funding?
Then we say that, and the engagement ends there. Identifying which use cases are not worth building is most of what the advisory work is for. Cheaper than discovering it in month five.
Which model vendors do you work with?
Anthropic, OpenAI, and Google, plus open-weight models where the data cannot leave your infrastructure. The choice follows the constraint: data residency, latency, and cost per call, in that order.
