Discover: mapping a system across a wall of diagramsDiagnose: reading dashboards to locate the problemDesign: wireframes and interface work for the solutionDeliver: shipping the finished productEvolve: continuous improvement after launchAn AI agent working through a task inside a live system
Evaluate · Build · Monitor

Applied AI

AI that is held to a number before it reaches a customer. We start by writing the test set and the baseline, because a model with no baseline beside it cannot be judged, improved, or honestly defended in a meeting six months from now.

  • Set
    Written before build
  • Base
    The number to beat
  • Human
    In the loop by design
  • Watch
    After it goes live

Sectors that trust us

What is applied AI, and what does it change?

Applied AI is using a model to do one job inside a process that already exists, and being able to say how well it does it. The model is the smallest part. The work is the evaluation set, the fallback when the model is unsure, and the monitoring that tells you when its accuracy has quietly moved.

Applied AI or research?
Research asks whether something is possible. Applied AI assumes it is and asks what it costs per call, how often it is wrong, and what happens on the occasion when it is. Those three answers decide whether it ships.

Key takeaways

  • Evaluation set first: written from your own records before any model is chosen or even priced.

  • Baseline always: every accuracy claim arrives beside the baseline number it had to beat.

  • Fallback by design: low confidence routes to a person, decided before launch rather than after.

  • Cost known up front: the cost per call is in the proposal, not discovered on the first invoice.

Why pilots stall

Where pilots die, and how we ship instead.

How pilots die

  • Judged on the examples picked to demo it.

  • No baseline, so no change can be proven.

  • Confident and wrong, with no fallback path.

  • Cost per call discovered on the first invoice.

How we ship it

  • Test set agreed first, from your own records.

  • A baseline measured before a model is chosen.

  • Low confidence routes to a person, by design.

  • Cost per call stated in the proposal itself.

What we build

Applied AI, with its numbers.

Each ships with an evaluation set, a measured baseline and a defined fallback; without those a model is a demo.

  • Evaluation sets

    Built from your own records, with the hard cases kept in, never quietly dropped.

  • Document handling

    Extraction with a confidence score, and a review queue for everything beneath it.

  • Arabic language work

    Dialect, diacritics and mixed script tested as first-class cases, not an afterthought.

  • Retrieval

    Answers that cite the document they came from, so a wrong one can be traced back.

  • Fallback design

    What happens when the model is unsure, decided before launch rather than after it.

  • Drift monitoring

    Live accuracy on a dashboard, read against the baseline the feature shipped with.

The build · held to a number

How we build AI that holds up after the demo.

A demo proves the model can do the job once. Production proves it does the job on your traffic, at a known cost, with a person ready for the cases it gets wrong.

Discover: mapping a system across a wall of diagrams
01Test set

Your own records become the evaluation set, hard cases deliberately kept in.

What we build with

Model-agnostic, on a stack your team can operate.

  • OpenAI
  • Anthropic
  • LangGraph
  • Hugging Face
  • PostgreSQL
  • pgvector
  • Azure AI
  • Amazon Bedrock

How it works · our method

The SiaSol five-phase method, applied to this service.

  1. 01

    Discover

    Most technology projects fail long before the first line of code — they fail at the question.

  2. 02

    Diagnose

    Every slow process has an owner and a cost.

  3. 03

    Design

    Architecture is a set of decisions you live with.

  4. 04

    Deliver

    Software not in production is just cost.

  5. 05

    Evolve

    The handover is the point, not the end.

Where we work

Close where it counts, bilingual everywhere

  • Egypt: Cairo (HQ) and nationwide delivery

  • Saudi Arabia: Riyadh, with teams on client time zones

  • Gulf and beyond: Arabic and English, one delivery standard

Cost & engagement

Applied AI: engineering quoted, model usage sized before you commit.

Engineering is quoted as a fixed number; model usage is paid by you directly, in your own account, and sized from the evaluation before you commit to anything. The evaluation itself is priced on its own, so the decision to go further is made on measured numbers rather than on a demo.

  • Fixed · two to three weeks

    Evaluation

    A test set from your records, a measured baseline, and a go or no-go.

  • Per feature · after evaluation

    One feature

    The model inside a process, with its fallback, its monitoring and its cost per call.

  • Monthly · dedicated

    Embedded team

    A standing team for a roadmap of several AI features, reviewed quarterly.

Why teams in the Gulf and Egypt bring us the AI work

Our engineers sit in Cairo, Riyadh and Dubai, inside your working day and your language. Arabic is tested as a first-class case, not a translation layer, and the evaluation is run on your own records under your data rules. When the baseline says the model is not the answer, we say so and improve the process instead.

FAQ

What to ask about Applied AI.

01Can you work with our own documents?

Yes, and we prefer to. An evaluation set built from your own records is the only one that predicts how the feature will behave on your traffic. Public benchmarks tell you about the model in general, which is rarely the question being asked.

02What if the evaluation says do not build it?

Then it says so, and that is the evaluation working. Twice the measured baseline, the existing manual process, was good enough that the honest advice was to improve the process instead. Both clients came back for different work.

03How do you handle Arabic?

As a first-class case in the evaluation set rather than a variant tested at the end. Dialect, missing diacritics and mixed Arabic-English input each get their own rows, because a model that scores well on formal Arabic can fail on all three.

04Who pays for the model usage?

You do, directly to the provider, on your own account. We size it during the evaluation so the number is known before you commit, and the account is in your name from day one, so nothing is transferred later and there is no margin on top.

05What happens when the model is unsure?

It routes to a person, and that path is designed before launch rather than added after the first bad outcome. The confidence threshold is a setting your team owns, so the balance between automation and review stays yours to move.

06Do you fine-tune or use hosted models?

Hosted first, almost always. A fine-tune is only worth it once a baseline exists to prove it helped, and in most projects the accuracy gap closes with better retrieval and a better prompt long before it closes with training.

07How do we know the accuracy has not drifted?

The live accuracy sits on a dashboard beside its baseline, and a sample of real traffic is scored on a schedule. Drift is normal when a provider updates a model; the question is whether you learn it from a chart or from a complaint.

08Is our data used to train anything?

No. The provider terms we select exclude training on submitted data, and we write the chosen configuration into the handover document so it can be checked rather than remembered. Retention settings are part of the same record.

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From the journal

Ready to hold the model to a number?

Tell us the decision you want made faster and how often it is made today. We come back with a test set drawn from your own records, a measured baseline and a number for the first feature.

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