Almadarek gives institutions an AI operating model — not another chatbot. Agent workforces that carry your organization's know-how, execute your workflows under explicit human authority, and leave the institution smarter after every single run.
Filmed on the live platform and a real engagement — Arabic narration, bilingual captions. From the first idea, through the human gates, to the knowledge that compounds after every mission.
Boards have approved the budgets. Teams have run the pilots. Individuals are quietly using copilots everywhere. And yet — ask most institutions what AI actually operates for them today, with what authority, under whose accountability… and the room goes quiet. That gap is not a technology problem. It is an operating model problem.
The institutions that build the operating muscle first will set the pace for everyone else.
These are measurements from live engagements, not projections. Expert hours shift from producing deliverables to directing and approving them — and because every mission enriches your knowledge graph, the next deliverable costs less than the last. That is the opposite of how professional services economics have always worked.
A delivery stream running on expert effort costs roughly SAR 75K a month and produces around twelve deliverables. The governed workspace running beside it costs about SAR 5K a month — a ~15× cost differential on the effort it absorbs. Your experts don't disappear; they move up the stack, from producing deliverables to directing missions and signing gates — which is exactly where their judgment was always worth the fee.
Your entity, department or engagement runs in its own isolated workspace — tiered licensing per workspace, not per seat. Predictable, and it doesn't tax you for inviting the whole team.
You pay for missions actually executed — cost that tracks the work, and falls per deliverable as the knowledge graph makes every next mission cheaper to run.
Custom blueprints, codified domain skills and a private knowledge graph — built with you, owned by you, and reusable across every mission that follows.
The clincher is the direction of the curve: every mission enriches your knowledge graph, so subsequent runs are faster and smarter. Deliverable number fifty costs a fraction of deliverable number one — the exact opposite of consulting economics, where every deliverable costs the same expert-hours as the last.
Everything on this page already exists — fully architected, fully designed, fully developed, running live missions right now. The film is a screen recording of the product, not a render. The numbers are measurements, not projections. And the platform can be opened in front of you, live, in your first session.
Workspaces, the mission engine, gates, the knowledge hub, repository workspaces, the connector fabric, the model-agnostic intelligence layer — architecture that is documented and load-bearing in production, not a target-state diagram.
A coherent, bilingual design system across every surface — the amber rule, human phrasing, Arabic-first by construction. What you see in the film is the shipped interface, pixel for pixel.
The complete platform is implemented and versioned — and new capability ships continuously through its own governed discovery and delivery loops, under the same gates your work would get.
Production workspaces across four sectors — government, research, enterprise and consulting — with governed missions running on schedules, and every irreversible action waiting for a named human, every day.
If a claim on this page sounds bold — ask for the screen. Every one of them can be shown live.
One picture: capabilities and the flows between them — no product names, no infrastructure trivia. Challenge any box in this diagram — it exists in production, and every arrow is a real, audited path.
Hard isolation per entity or engagement — its own members, agents, data and governance. Nothing bleeds across clients.
Agents are named staff with scoped powers; skills are your methods and standards, versioned and attached to every agent that acts for you.
Blueprints run multi-step work on schedules, on demand, or on events — and every irreversible step stops at a named human. No exceptions.
Deliverables are filed and versioned where your team lives; knowledge is captured typed and linked, so every run starts from everything learned before.
Agent teams work on real repositories — code or structured knowledge — on branches, through a human-gated merge queue, with staging before anything ships.
Scoped, audited access to the systems where your work already happens; intelligence swappable per workload — cloud, or fully local and sovereign.
Agents work inside the systems your teams already run, and reach the outside world when the mission needs it. Every connection is scoped per agent, audited, and revocable — the same governance as everything else on this page. Mail is live today: agents read and send mail under governance, in production.
Each agent carries only the connections its role requires — the meeting agent can't touch finance, the finance agent can't send mail.
Every read and every write lands in the mission's evidence trail — who, what, where, under whose approval.
Connections are grants, not integrations you're stuck with. Withdraw one and the agent simply loses that power — nothing breaks.
Seven ideas, one coherent whole. Each layer is simple to say — together they are what almost nobody else has built: an operating system for institutional AI.
Each entity, department or engagement gets its own governed world — members, roles and data cleanly isolated.
Named AI agents organized into teams with leads, specialties and scoped powers — mirroring how your institution already works.
Your procedures and standards as versioned, reusable assets every agent follows. Institutional memory, made executable.
Multi-agent workflows that pause at the moments that matter and wait for a named human to decide. Authority, by construction.
Every mission enriches a connected map of what your organization knows. The system gets smarter with use — and the value compounds.
Email, documents, project tools, data platforms, line-of-business systems. We integrate with what you have. We replace nothing.
Your cloud or your data center. Your choice of AI models — swappable, never locked. Your keys, your audit, your jurisdiction.
Proven in live operation, not on a roadmap slide. (Integration, sovereignty and evidence get their own sections below — they've earned them.)
every AI win depends on one enthusiast and dies with their calendar.
Agents are institutional staff: named, role-scoped, organized into teams with leads, each carrying exactly the tools and permissions its job requires — nothing more.
AI capacity that scales like an org chart and reports like one, too.
your best people's judgment lives in their heads — and resigns when they do.
Skills capture how your institution does the work — the method, the standards, the red lines — as versioned assets attached to every agent that acts for you.
Consistent quality at any scale, and an expertise library that appreciates instead of evaporating.
"human in the loop" is a slogan in most tools — a checkbox, not a mechanism.
Here it is architecture: workflows pause at consequential moments and wait for a named person to approve, redirect, or partially approve with instructions. The mission resumes only on their word.
Delegation without abdication — speed of automation, authority of your chain of command.
every project starts from zero; the last engagement's learning is in a folder nobody opens.
Every mission feeds a living knowledge graph — findings, decisions, documents and their relationships — that the next mission builds on automatically.
An institution that is measurably smarter each quarter — your data becomes a moat, not a liability.
One discipline runs through every screen: colour answers a single question — "does this need me?" Blue means working, green means done well, and amber is reserved, absolutely and exclusively, for one thing: a human must decide. Read your whole AI operation at a glance — nothing irreversible ever hides in the noise.
Our founding conviction: agents, teams and their interactions should be designed the way you design an organization — deliberately, structurally, once — not improvised one prompt at a time. The result is a rare combination: deterministic where your institution demands certainty, autonomous where intelligence pays.
Classic RPA and rule engines: perfectly predictable, zero judgment. The moment reality deviates from the script — a new document format, an unusual case — it breaks, and humans clean up.
Autonomous AI with no designed structure: impressive demos, unrepeatable outcomes. Nobody can say what it will do next run — which is exactly why it never leaves the sandbox.
A deterministic skeleton — who acts, in what order, within what boundaries, gated where — carrying autonomous muscle: real judgment inside every step. Same mission structure every run; intelligence where it matters.
Design the structure once. Delegate the judgment every run.
That's how AI graduates from demo to institution.
Every capability travels two governed loops. Discovery: agents study the product and the field evidence, and turn friction into a scoped proposal — which becomes work only when a human approves the brief. Delivery: agent teams work on a real repository — branches, adversarial review, QA, a human-gated merge queue, staging — before anything ships. No brief, no build. No human, no ship.
The enterprise-architecture knowledge portal from a live engagement is developed inside a repository workspace — an agent team working on its actual codebase, under exactly the same gates your work would get. A real, current cycle:
Users hit real UX friction in the portal: search highlights that don't persist, no navigation history, no filtering by type or edge. Logged as structured knowledge — not lost in a chat thread.
Agents study the product and the field evidence, and draft a scoped proposal: what to change, why it's worth it, and where the boundaries are.
A human approves the proposal into a build brief. No brief, no build.
Planning agents derive the epic and stories; a developer agent implements them on a real branch of the portal's repository.
A second agent attacks the code looking for specific defects; QA verifies against the acceptance criteria in the brief.
A named human promotes the work; it rides the merge queue to staging, gets tried by humans for real, then ships. Field feedback on the shipped feature starts the next loop.
Beneath every workspace runs a living knowledge graph: missions, documents, findings, decisions and the systems they touched — captured as connected, typed knowledge, not files in a folder. This is the growth engine of your operating model: efficiency (the next mission starts from what the last one learned), intelligence (connections emerge across domains no silo could see), and maturity (evidence density becomes an asset you can audit). Press play — this is a year of operation, compressed.
Wire in your internal systems and external services, and every mission closes a loop: connect → operate → capture → sharpen. Outputs and decisions flow back as structured knowledge, so the same mission runs sharper next month than it did this month. Tools depreciate the day you buy them. This appreciates.
This is the heart of the pitch — not a slide about governance, but governance happening. Pick a mission drawn from our live deployments, run it, and when it reaches the amber gate, the decision is yours.
A standing mission: scan the world's science, synthesize what matters, deliver a branded bilingual brief to leadership.
The brief is composed. Nothing leaves this institution until you say so.
Adoption dies in fragmentation: prompts in personal chatbots, results in email threads, files on individual machines — invisible, unshareable, ungoverned. Almadarek replaces the scatter with one surface where your people and your AI workforce work together: shared libraries per team and project, artifacts previewed and versioned in place, and conversations with agents where you can watch the work happen — every tool step visible, every output filed.
Most vendors answer governance questions with a policy PDF. We answer with architecture — each guarantee below is enforced by the platform itself, demonstrable in front of your risk committee.
Nothing irreversible without a person's recorded, non-delegable approval.
Which agent, which sources, which approval, what changed — on every mission.
Idle is never dressed as activity; failures are explicit and explained in plain language.
Work products merge only on human promotion — no silent writes to your systems of record.
Each world is separated at the infrastructure level — not by a policy filter a bug could bypass.
Swappable intelligence — commercial, open, or fully local. Never hostage to one provider.
Every agent carries exactly the connections its role requires — granted and audited separately.
Arabic-first surfaces and data that respects your borders — by construction, not localization.
The same platform, the same operating model — carried into radically different institutions. Sector names withheld here; full case walk-throughs available in a guided session.
PainLeadership needed a disciplined view of fast-moving global science across nine research domains — a full-time analyst job, done part-time, inconsistently.
What runsA standing agent team scans scientific databases, de-duplicates against everything previously covered, synthesizes per domain, composes a fully branded bilingual brief, and delivers it by email — on schedule, unattended.
OutcomeA weekly institutional product where there was an occasional heroic effort — every claim traceable to its source, zero fabrication tolerated by design.
PainOver a million physical documents across 17 sites; no annual inventory, weak Arabic OCR at publication, paper-based transfers, environmental risk with no monitoring platform.
What runsAn intelligence layer over the entire document lifecycle — Arabic-first digitization QA, chain-of-custody tracking, inventory reconciliation, environmental watch — integrating with the existing archive system, replacing nothing.
OutcomeA field-findings-driven adoption roadmap the ministry's own teams recognized as theirs — 15 documented operational pains, each mapped to a governed capability.
PainA major digital-transformation engagement needed a full enterprise-architecture picture — hundreds of systems, processes and gaps — normally months of consultant effort producing a snapshot that ages instantly.
What runsTwo agent teams — seven domain architects working in parallel, a chief architect reconciling, maintainers guarding graph integrity — running full maturity assessments in under an hour, every change gated through human review before it merges.
OutcomeA living knowledge graph that tripled its connected evidence in weeks — and assessments honest enough to score lower as evidence improved. That honesty is the product.
PainArabic-heavy institutions get an afterthought experience: broken right-to-left interfaces, transcription that garbles dialect, "bilingual" reports that embarrass in front of leadership.
What runsBilingual by construction — every surface, every report. Saudi-dialect meeting transcription running entirely on sovereign infrastructure: a two-hour executive meeting processed in minutes, audio never leaving the premises.
OutcomeBoard-grade Arabic deliverables, and the confidential meetings nobody would ever send to a foreign cloud — finally in scope.
PainGovernance meetings run for hours in Arabic; minutes arrive late, uncited and inconsistent — and the weekly status report is rebuilt by hand from scratch, every week, by whoever has time.
What runsTwo standing missions. One turns a recorded governance meeting into a cited transcript with numbered decisions, actions and risks — every fact carrying its timestamp. The other gathers the week's meetings, decisions and delivery movement into a branded bilingual report and status deck, on schedule, and stops at the project director for release.
OutcomeA one-hour-fifty-one-minute meeting became board-grade cited minutes in about thirty-six minutes — quality measured above the hand-made version — and the weekly report became a product that arrives whether or not anyone had a spare day.
PainTender packs land as hundreds of pages with days on the clock. Requirements get missed, compliance matrices are assembled by hand the night before, and the technical proposal quality depends entirely on who was free that week.
What runsAn agent team reads the tender pack and extracts every requirement, builds the compliance matrix, drafts the solution and estimate against your own method and rate card, then assembles a client-ready technical proposal and deck — with an adversarial QA pass over it before anything reaches a human.
OutcomeDays of senior effort compressed into a working session, with nothing submitted until a named partner signs the gate — and every claim in the proposal traceable back to the clause it answers.
A research pipeline, an archive intelligence layer, an enterprise assessment engine, a governance-reporting desk, a bid factory — none of these were custom builds. Each is the same seven layers, seeded with that institution's know-how, wired to that institution's systems, governed by that institution's authority.
Why it matters to youYour first use case is not a bespoke project with bespoke risk. It's an instance of an operating model that is already running — which is why we deploy in weeks, and why use case #2 costs a fraction of use case #1.
One question decides whether AI ever leaves the sandbox in this region: does the data stay inside the walls? Here it is answered by architecture, not by policy. (Integration is covered in Connects to your estate — this section is only about where the intelligence runs.)
The platform never marries a model. Commercial, open, or fully local models running inside your own infrastructure — swapped per workload, with the same skills, the same gates and the same evidence trail either way. The provider is a setting on the platform, not an architectural decision you are stuck with.
A disciplined arc, not a big-bang program. Each phase ships working capability — and each phase gives leadership something concrete to see.
A guided session walks your leadership through the live platform — your sector, your scenarios, your governance questions answered on screen, not on slides.
