Discover: mapping a system across a wall of diagrams

Diagnose: reading dashboards to locate the problem

Decide · Act · Explain

AI automation

Automation that decides, and can say why it decided. Automating a click saves seconds; automating a decision saves hours and creates an obligation, because the first customer affected by a wrong one will ask for a reason and deserve to get a real answer.

Why
Logged, every time
Undo
For every action
Human
Above the threshold
Shadow
Run before it acts

Sectors that trust us

What is decision automation?

Decision automation is letting software make a call that a person used to make, under rules or a model, and then act on it. The engineering is ordinary. What makes it different from other automation is that every outcome has to be explainable afterwards, to a customer, an auditor, or a regulator.

Rules or a model?: Rules wherever the policy is written down somewhere, because they are auditable by reading them. A model where the pattern exists but nobody can state it. Most useful systems are mostly rules with a model handling the residue.

Why automation bites

Where automation bites, how we hold it in check, and who stays in charge.

How it goes wrong

A wrong decision cannot be explained afterwards.
No undo, so one bad batch stays applied for days.
It is switched on everywhere, for everyone, on one day.
Nobody checks the output once the launch week is over.

How we contain it

Inputs, rule and reason are recorded for every decision.
Every action has an undo, and the undo is tested first.
It runs in shadow for weeks, then on a small slice.
A weekly sample of borderline cases goes to a person.

What we automate

Automation that can explain itself, undo itself, and ask a person.

Each is a decision, not a task, so each ships with a reason log, an undo and a threshold above which a person decides.

Document triage and data capture
Routing by what the document says, with the extracted basis kept for review.
Approval routing
Who approves what, set by written policy rather than by whoever is free.
Exception handling
The cases a rule cannot settle, queued for a person with context attached.
Pricing and discount decisions
Versioned rules, so last year's quote can still be explained today.
Reply drafting
A draft for a person to review and send; nothing goes out unattended.
Reconciliation
Matching across two systems, with the unmatched surfaced rather than hidden.

Three kinds of automation

Script, decision or agent: three different builds.

Scripted automation
Fixed steps on fixed rules, dependable right up until the input changes shape.
Decision automation
Software makes a call a person used to make, and records the reason it did.
AI agents
A model plans several steps towards a goal across your systems, within set limits.

From manual to automatic

How a manual process becomes a trusted automation.

We start from the decision as people make it today, not from the tool. The policy is written down, the automation runs in shadow against your team, and it is allowed to act only when the disagreements become boring.

We sit with the people making the call today and write the policy down as it is.

Where the automations run

Connected to the systems you already own.

Salesforce
Microsoft Dynamics 365
SAP S/4HANA
Odoo
Zoho
Slack
Microsoft 365
Google Workspace

Why SiaSol

We ship automation, and say what should stay manual.

The reason log and the undo are built before the decision logic, and we say plainly which decisions should stay with a person. Both are written into the proposal, so the scope of what the automation may decide on its own is agreed before a line of it is built.

Explainable outcomes
Inputs, rule version and reason text recorded for every single decision.
A real undo
Built and tested before anything is switched on, not after a bad batch.
Shadow first
Weeks of watching alongside your team before it is allowed to act once.
Yours to tune
Thresholds and rules your own team can change in an afternoon, no ticket.

How it works · our method

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

01
Discover
Most technology projects fail long before the first line of code — they fail at the question.
02
Diagnose
Every slow process has an owner and a cost.
03
Design
Architecture is a set of decisions you live with.
04
Deliver
Software not in production is just cost.
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

Automation: cost and engagement

Cost follows the number of decisions and how clearly the policy behind each one is written down; a decision with no written policy costs more, because writing it is the first deliverable. One decision is quoted end to end first and calibrates the rest.

Fixed · four to six weeks
One decision
Built, run in shadow, and reviewed against your own team's calls.
Per decision · quoted after
More decisions
Each added on the same pattern, with its own shadow run and undo.
Monthly · dedicated
Embedded team
A standing team tuning rules and thresholds as the business changes.

Proof

Decisions we have automated safely

How a clinic group running eleven sites made a double-booking impossible rather than merely unlikelyHealthcare · 2026

FAQ

What to ask about AI automation.

01How do you explain a wrong decision?

By reading the record. Each decision stores the inputs it saw, the rule or model version that applied and the reason text, so answering a complaint is a lookup rather than an investigation. Without that record it is unusable in a regulated process.

02Can a decision be reversed?

Yes, and the undo is built and tested before the automation is switched on rather than written after the first bad batch. For actions that cannot truly be undone, such as a sent message or a taken payment, the automation drafts and a person confirms.

03Will this replace people on the team?

That is your decision and we ask about it in week one, because the answer changes how the work is run. Teams that say so honestly get better rules, since the people who make the decisions today describe them accurately instead of defending them.

04What is the confidence threshold for?

It decides which cases a person still sees. It is a setting your team owns rather than something buried in the code, so the balance between throughput and review can be moved in an afternoon when the volume or the risk changes.

05Do you use rules or a model?

Rules wherever a written policy exists, because a rule can be audited by reading it. A model only for the residue a rule cannot express, and in that case the model output is recorded with its score so the decision is still traceable afterwards.

06How long is the shadow period?

Until the disagreements become boring, usually three to six weeks rather than a fixed number. The exit condition is agreed in advance and written down, so nobody decides under pressure whether a week of interesting disagreements is good enough.

07What if the policy is not written down?

Then writing it down is the first deliverable, and often the most valuable one. Several projects have found two teams applying different unwritten policies to the same case, which is worth knowing whether or not anything is automated afterwards.

08Who checks it after launch?

A weekly sample goes to a person on your team, and the automation is built so this takes minutes, not a project. The sample is drawn to include the borderline cases rather than at random, because a random sample of easy cases proves nothing.