Epsilon AI Global · Enterprise intelligence since 2015
InsAI product ecosystemExplore seven live platforms for construction, customer conversations, company knowledge, manufacturing, supply chain, financial risk and workflow automation. Find the platform that fits your business and explore what it can do.
InsAI — enterprise AI platforms by Epsilon AI Global
+30%
Operational efficiency
Rowad Modern Engineering · construction management engagement
86.7%
Calls contained
Mogassam call centre · resolved without reaching a person
−80%
Retrieval time
Government sector · enterprise knowledge assistant
Seven platforms on one intelligence layer, one administration console.
Concrete delivery risk may delay Level 14
Palm District · Tower ADelay risk
0 days
Re-routed automatically — the handover date held.Review
Open the demo to explore the project, inspect its risks and review the available actions.
Explore the ConstructINS demoOrganizations served across AI delivery and professional training
Choose a platform
All seven platforms are live and available to explore. Choose the one that matches your business need — each is specialized around a different enterprise problem, and all of them share one intelligence foundation and one permission model.
What would you like to improve?
For contractors, developers and project teams
For customer-service and contact-centre teams
For legal, compliance, government and technical teams
For plant, maintenance and quality teams
For supply-chain, procurement and inventory teams
For finance, HR and operations teams
For risk, credit and finance teams
Explore the platforms
Each platform has a public page with real screens on sample data that shows what the product does. Access to the working application is arranged by the Epsilon team after a guided demo — there is no self-serve sign-in.
Proof
Don't judge InsAI by what we say we can build. Judge it by what has already been built.
Delivered, measured, still running. 10 engagements, each with the document and page its figures come from. Where a client is not named, the reason is on the page.
Showing 4 of 10 cases
Client: Rowad Modern Engineering
Client engagement: live pilot · Demo available
Fragmented project data hid delay, cost and safety risk until it was too late.
Client: Mogassam
Client deployment: in production · Demo available
Inbound call volume tied up agents needed for the calls that truly required a person.
Client: Government sector (confidential)
Engagement delivered · Demo available
Retrieval across disconnected departments was slow.
Client: Zylo3D
Engagement delivered · Demo available
Unpredictable equipment failures drove up production and maintenance costs.
Named-client cards show a product screenshot on sample data; confidential engagements show an illustrative interface, not the real platform. Where a client provided a signed recognition letter, it appears once on that engagement's case study. “AI project” marks a wider delivery that is not one of the seven current platforms.
Signed client recognition
Signed recognition letters from clients across Epsilon's delivery. They reflect the team's track record — not every organization is a deployment of this product.
Rowad Modern Engineering
Construction
Signed letter
Mogassam
Contact centre
Signed letter
Zylo3D
Manufacturing
Signed letter
NUT Botanicals
Retail & recommendations
Signed letter
AIT
Real-time data
Signed letter
Figures as published by Epsilon AI Analytics.
Use-case library
Where each product is applied, who it is for, and how the workflow runs. Open one to see the challenge, the approach and what it needs from you.
4 Use casesProtect programme, margin and handover with one operating view of the project.
ConstructINS connects schedule, cost, procurement and field progress so emerging risk is priced and acted on before the monthly report.
Portfolio command center: weighted health 89 and 26 open decisions across projects.
Business challenge
Project truth is split between planning, commercial, procurement and site systems. By the time a variance reaches leadership, the practical recovery window may already be gone.
Solution approach
Build a shared project intelligence layer that detects divergence, tests recovery paths and routes only the decisions that need approval.
Workflow
Expected outcomes
Required inputs
Coordinate long-running programmes where one dependency can move every downstream date.
ConstructINS monitors packages, interfaces, permits, materials and field progress as one connected delivery system.
A schedule slipping +9 days with ten critical-path activities out of float — the re-baseline reclaims 9 days and EGP 2.1M.
Business challenge
Infrastructure programmes span contracts, geographies and public interfaces. Delay often begins in an upstream permit, material or handover that no single dashboard owns.
Solution approach
Model dependencies across packages, watch leading indicators and simulate the effect of each intervention across the programme before committing it.
Workflow
Expected outcomes
Required inputs
See portfolio risk across projects before it becomes a board-level surprise.
ConstructINS standardises project signals across a portfolio while preserving the detail needed to act at package and site level.
The cross-project decision queue — 26 open decisions ranked by cash at stake (EGP 87.9M), showing what to action first.
Business challenge
Each project reports in a different format and cadence. Portfolio teams spend the reporting cycle reconciling numbers instead of deciding where intervention will protect value.
Solution approach
Normalize schedule, cost and delivery indicators across projects, then rank attention by exposure, confidence and remaining recovery window.
Workflow
Expected outcomes
Required inputs
Turn project-system data into a decision brief, not another status pack.
ConstructINS explains what changed, why it matters, the forecast exposure and which decisions can still alter the outcome.
Scheduled board packs and client reports — executive portfolio pack, cost summary, site progress — by audience and cadence.
Business challenge
Leadership receives polished reports built from lagging data. The material decision is buried between activity updates, and different functions defend different versions of the truth.
Solution approach
Generate an exception-led brief from the connected project record, with every forecast linked to its drivers and every requested decision stated explicitly.
Workflow
Expected outcomes
Required inputs
5 Use casesResolve routine calls end to end — and hand complex calls over with context.
VoiceINS answers in the caller's language and dialect, retrieves governed information, completes approved requests and escalates when a person is needed.
28,541 calls this month at 86.7% containment, with live conversations and per-channel dialects.
Business challenge
Peak demand creates queues while skilled agents spend time on repetitive enquiries. Traditional menus frustrate callers and lose context when a call is transferred.
Solution approach
Use a conversational voice agent for approved intents, grounded in the same knowledge base as RagINS, with explicit transfer rules and a full transcript.
Workflow
Expected outcomes
Required inputs
Let field teams ask, confirm and record work while their hands stay on the job.
VoiceINS gives mobile workers a spoken interface to procedures and job context, then records the interaction for the operational trail.
A searchable log of every call and chat — caller, channel, intent, sentiment and CSAT on each conversation.
Business challenge
Field staff cannot always stop, remove protective equipment and navigate a portal. Calls to supervisors interrupt both sides and leave little structured record.
Solution approach
Expose approved procedures and task data through a hands-free voice flow, with confirmation for consequential steps and escalation when the answer is uncertain.
Workflow
Expected outcomes
Required inputs
Handle outages, bills and service requests clearly when call volume spikes.
VoiceINS combines live service context with governed knowledge to answer common enquiries and route account-specific exceptions safely.
Ten dialects in production across the speech, model and voice stack, with the vendor behind each turn.
Business challenge
Outages and campaigns create sudden demand that fixed staffing cannot absorb. Callers need a useful answer, not a long menu or a generic status message.
Solution approach
Connect service status, approved account workflows and customer guidance to a multilingual voice layer with strict authentication and transfer boundaries.
Workflow
Expected outcomes
Required inputs
Collect structured patient intake by voice — without replacing clinical judgment.
VoiceINS handles approved administrative intake, appointment preparation and service navigation, then routes clinical or urgent matters to qualified staff.
Dialect coverage with word-error-rate and the speech, model and voice provider for each language.
Business challenge
Access teams repeat the same administrative questions while language barriers and long forms create incomplete records before the patient reaches the right service.
Solution approach
Use a clearly bounded voice flow for non-diagnostic intake, consented data capture and service routing, with immediate human escalation for clinical decisions and emergencies.
Workflow
Expected outcomes
Required inputs
Offer a spoken route to service when screens, forms or typing create a barrier.
VoiceINS lets people navigate information and approved service flows through natural speech, while preserving a clear path to human assistance.
Every conversation transcribed and scored — channel, intent, sentiment and outcome in one view.
Business challenge
Digital-first services can exclude people with visual, motor, literacy or situational barriers. A compliant interface alone does not always make a complex process usable.
Solution approach
Add voice as an equivalent service channel, use short confirmable steps, and allow the user to request a person at any point.
Workflow
Expected outcomes
Required inputs
5 Use casesGive every team the current policy answer — with the evidence attached.
RagINS turns policies, procedures and regulatory guidance into a governed answer service that cites the exact source and page.
The vendor-onboarding policy answered in three steps, with two cited procurement sources.
Business challenge
Policies change across business units and jurisdictions. Staff often find an old copy first, while compliance teams repeatedly answer the same questions and rebuild the evidence trail during every audit.
Solution approach
Create controlled knowledge channels by policy domain, preserve version and effective-date metadata, and answer only from material the user is permitted to see.
Workflow
Expected outcomes
Required inputs
Find obligations, exceptions and commercial exposure across every agreement.
RagINS makes executed agreements searchable by meaning, so reviewers can compare clauses and verify every answer against the signed text.
Ten connected sources feeding the index — SharePoint, Drive and databases — with sync status, volume and freshness.
Business challenge
Important obligations are buried in long agreements, amendments and schedules. Manual review is slow, and a missed notice period or liability exception can become a costly surprise.
Solution approach
Index the complete contract set, keep documents separated by entity and permission, and let teams ask focused questions that return the governing clause and page.
Workflow
Expected outcomes
Required inputs
Turn dense manuals into field-ready answers technicians can verify.
RagINS retrieves the right procedure, warning or specification from the approved manual set without making technicians search page by page.
Governed enterprise knowledge — users by role, connected sources and compliance status at a glance.
Business challenge
Manuals are long, equipment-specific and often stored in several revisions. Under time pressure, teams rely on memory, informal messages or whichever PDF appears first.
Solution approach
Organise manuals by asset and revision, preserve tables and warnings, and deliver concise procedural answers with the original page attached.
Workflow
Expected outcomes
Required inputs
Give every support agent one governed answer across every knowledge source.
RagINS unifies runbooks, FAQs, product documentation and resolved cases into a cited assistant for faster, more consistent support.
An HR question answered — 21 days of annual leave — cited to the employee handbook.
Business challenge
Answers are spread across portals, tickets and experienced colleagues. New agents take longer, repeat escalations and may give different answers to the same request.
Solution approach
Create permission-aware knowledge channels around each service and surface the best approved answer with its source before the agent responds.
Workflow
Expected outcomes
Required inputs
Search public-sector records at scale without moving sensitive knowledge outside policy boundaries.
RagINS brings multilingual records, circulars and departmental archives into one permission-aware retrieval layer deployable on-premises.
Scoped assistants answering only from their own collections, each with a measured answer accuracy.
Business challenge
Records sit across departments, formats and legacy repositories. Finding one defensible answer depends on knowing who owns the file and how it was named.
Solution approach
Use OCR and semantic retrieval across approved repositories while enforcing department-level access and returning the originating record with each answer.
Workflow
Expected outcomes
Required inputs
Fix the asset in a planned window — before it fails mid-shift.
ManufactINS watches vibration, load and temperature on tracked assets, estimates remaining useful life, and raises the work order before the breakdown.
CNC-04 flagged: 72% chance of spindle-bearing failure within 11 days (11 days of useful life left).
Business challenge
Unplanned failures stop the line at the worst time and cost far more than a planned repair.
Solution approach
Stream machine signals, score remaining life per asset, and act inside the next maintenance window.
Workflow
Expected outcomes
Required inputs
Stop the batch before it turns to scrap.
SPC and inline-vision signals are scored in real time; a run trending out of spec is flagged at the source with the correction to make.
First-pass yield 96.8% and defect rate 3.2%, broken down by defect category.
Business challenge
Defects surface at final inspection, after a whole run is already lost.
Solution approach
Score SPC and vision live, flag drift, and correct the setpoint at the source.
Workflow
Expected outcomes
Required inputs
Re-sequence the moment reality changes — and keep the promised dates.
Sequences orders against real capacity, changeovers and material on hand, and re-plans when a machine or delivery slips.
Planned orders 8,100 against 9,600 capacity (96%), scheduled by line with AI recommendations.
Business challenge
A plan built in a spreadsheet breaks on contact with the floor.
Solution approach
Sequence against live capacity and materials; re-plan on disruption.
Workflow
Expected outcomes
Required inputs
Clear the clean invoices automatically; hold the rest for a person.
AgentINS runs a three-way match (invoice, PO, receipt), clears the clean items inside policy, and holds exceptions and above-limit items for a named approver — with a full audit trail.
A multi-step agent workflow on the canvas — trigger, research, scoring, human approval and send.
Business challenge
Matching is high-volume, repetitive work that ties up the finance team.
Solution approach
An agent runs the match end to end; anything risky waits for a human.
Workflow
Expected outcomes
Required inputs
Onboard a hire in minutes, not a day.
An agent creates accounts, assigns access, schedules training and files the paperwork — holding anything sensitive for approval.
The agent builder — identity, purpose, tools, permissions and autonomy, with a live preview.
Business challenge
Onboarding is manual, slow and error-prone across many systems.
Solution approach
An agent runs the checklist across systems; a person approves access.
Workflow
Expected outcomes
Required inputs
Triage every request and draft the reply for a person to send.
An agent classifies inbound requests, gathers the context from your systems, and drafts a grounded response for a human to review and send.
High-impact agent actions held for review — banded by risk, with the approval level each one needs.
Business challenge
High request volume buries the team in triage and lookups.
Solution approach
Agent triages and drafts; a person approves and sends.
Workflow
Expected outcomes
Required inputs
See the shortage days before the shelf is empty.
ChainINS reconciles demand and on-hand across sites, flags SKUs trending to stock out, and recommends the reorder or transfer to hold service.
9 SKUs at stockout risk within 21 days and below safety stock — flagged to reorder now.
Business challenge
Stockouts surface only when it's too late to protect the sale.
Solution approach
Forecast per SKU, compare to projected on-hand, and act inside the lead time.
Workflow
Expected outcomes
Required inputs
Buy in the window, not on the spike.
Forecasts material prices and FX-adjusted landed cost, and recommends buy-now, wait or lock inside a priced window.
Lead-time and price forecasts on volatile materials — a supplier out to 27 days, steel index +6% — the signals behind buy timing.
Business challenge
Buying at the wrong moment quietly inflates landed cost.
Solution approach
Forecast price and lead time; recommend the buy decision, priced.
Workflow
Expected outcomes
Required inputs
Know which supplier is about to miss — before it stops the line.
Scores every supplier on delivery, quality and price, and warns before a breach with the dual-source or expedite option.
A supplier scored 66 (high risk) at 71% on-time — 78% chance of running six days late.
Business challenge
A supplier slips without warning and empties the shelf.
Solution approach
Score suppliers continuously; warn early; recommend the mitigation.
Workflow
Expected outcomes
Required inputs
Catch a deteriorating customer weeks before they miss payment.
RiskINS scores each customer's credit and payment risk continuously, flags accounts crossing the early-warning threshold, and shows the drivers behind each — ready to review the limit, tighten terms or prioritize collection.
Customer 360: total exposure EGP 61.6M with high-risk customers flagged and the book sorted by risk.
Business challenge
Monthly reviews surface a bad debt only after it is already overdue.
Solution approach
Score continuously, rank by exposure, and explain every call.
Workflow
Expected outcomes
Required inputs
Catch the pattern static rules miss — ranked by exposure.
Scores transactions and entities for anomalies beyond static rules, opens ranked cases with the evidence, and cuts false positives.
Fraud cases surfaced — duplicate invoice, vendor bank-change, split purchase — nine open.
Business challenge
Static rules catch yesterday's fraud and drown teams in false alarms.
Solution approach
Score for anomalies, rank by exposure, attach the evidence.
Workflow
Expected outcomes
Required inputs
See the gap open against appetite — before it breaches.
Tracks exposure and the forecast liquidity gap against limits, and models a rate move, a downturn or a name failure on demand.
Scenario simulator: 'customers pay 30 days late' cuts liquidity cover by about 1.5 weeks.
Business challenge
Exposure surfaces after it has already breached appetite.
Solution approach
Track live exposure vs limits; model scenarios on demand.
Workflow
Expected outcomes
Required inputs
Why InsAI
AI models are increasingly accessible. Enterprise AI capability is not. Building a prototype is one thing; building intelligence that operates inside a real organization — across its data, systems, permissions, languages, workflows and infrastructure — is another.
InsAI is built on a decade of Epsilon AI Global's applied-AI experience — since 2015 — across data and enterprise technology. Every implementation contributes to an engineering foundation that is reused, hardened and extended across the ecosystem.
We don't approach every enterprise problem from zero.
Seven disciplines that compound with every deployment.
Applied AI
Models chosen and tuned for the task, not the demo — retrieval, prediction, reasoning and generation put to real work.
Data Engineering
Pipelines that turn scattered, inconsistent enterprise data into something intelligence can actually run on.
Enterprise Integration
Intelligence wired into the ERP, scheduling, document and line-of-business systems you already run.
Knowledge Engineering
Institutional knowledge structured, governed and made answerable — with the source attached.
Production Engineering
The reliability, monitoring and scale that carry a system from a working demo to daily operations.
Governance
Access, audit, approval and traceability built in, so automation stays accountable.
Regional Intelligence
Arabic and English as first-class, dialects included — built for how the region actually works.
The technology changes. The capability compounds.
One foundation
ConstructINS, VoiceINS, RagINS, ManufactINS, ChainINS, RiskINS and AgentINS are specialized around different enterprise problems — but they share one evolving foundation.
Seven specialized platforms
InsAI intelligence foundation
Built from one evolving foundation
Experience compounds across InsAI.
Business before model
Start from the decision to be improved, not the model to be deployed.
Evidence before confidence
Every answer carries its source, so it can be checked rather than trusted.
Integrate before replace
Work with the systems an organization already runs, not against them.
Govern before automate
Permissions, audit and control come before anything acts on its own.
Production before presentation
Built to run in daily operations, not to survive a demo.
Outcomes before features
Measured against the business result, not the length of the feature list.
Humans remain accountable
People stay in command of the decisions that matter, with approval in the loop.
Different industries. Different workflows. One standard of enterprise AI engineering.
Enterprise AI becomes valuable when intelligence is connected to the context, systems, controls and decisions of a real organization.
That surrounding architecture — the knowledge, the integrations, the permissions, the governance and the measurement — is where much of the difficult engineering lives.
The architecture around it
InsAI is designed around the full enterprise decision cycle — not only Ask → Answer.
Sources
Every answer points back to the document and page it came from.
Confidence
The system states how sure it is, and says plainly when it doesn't know.
Context
Answers are scoped to the knowledge and permissions of the person asking.
Authority
Role-based access is enforced on every request, server-side.
Approval
Consequential actions wait for a human to sign off.
Auditability
What was asked, answered and done is logged and reconstructable.
Measurement
Accuracy and outcomes are tracked, not assumed.
Trust is not a marketing claim. It is a system property.
Security & deployment
Enterprise AI is not a demo decision — it is a committee decision. Security and legal shortlist the vendor long before the business unit judges the accuracy. Here are the answers up front.
CISO / Security
“Where does our data go, and who can reach it?”
Deploy fully on-premises and nothing leaves your network. Role-based access is enforced server-side on every request, with audit logging of what was asked and answered. Verified credentials include ISO 9001 quality management and ITIDA licensing.
Legal / DPO
“Can we meet residency and give a straight answer to the regulator?”
Residency is a deployment choice, not an exception — Egypt, the Gulf or your own datacentre. Every answer is traceable to the source document it came from, so a decision can be reconstructed and defended after the fact.
CFO / Commercial
“What do we actually get, and when do we know it worked?”
Engagements run discovery → design → pilot → validation → scale. The pilot is scoped and measured before the commitment, so the business case is evidence rather than a projection.
CIO / IT
“What happens if we want out, and who runs it after go-live?”
It sits on the systems you already own — ERP, scheduling, BIM, document stores — rather than replacing them, and your documents stay yours. Your own people are certified to operate it through the academy, so the capability does not leave with a consultant.
Business owner
“Will people actually use it, in the language they work in?”
English and Arabic ship with full right-to-left, dialects included, and speech extends to whatever else your teams speak. No mode to switch and nothing to declare before someone starts talking.
Full security documentation and a DPA template are available on request during discovery.
The foundation
Whichever product your people use, the integration, governance and deployment story is the same one — and it is yours to control.
Deploy where policy requires
Cloud, on-premises or hybrid. Data residency and network isolation are configuration, not a special case — which is why regulated and government work is possible at all.
Governed access
Per-user product licensing and role-based administration, enforced on every request rather than hidden in the interface, with traceability on what was asked and answered.
Any language, natively
English and Arabic ship with full right-to-left layout, and the retrieval and speech layers extend to further languages and dialects as your operations do.
Built to be handed over
Administration, permissions, ingestion and usage reporting live in one console — and your own people are certified to run it.
Before you ask
No. We deploy in the cloud, on-premises or hybrid — all first-class, and the choice is yours. Regulated and government work usually runs fully on-premises, where no data ever leaves your network.
Every output is traceable. Answers cite the source and page they came from, predictions and scores show their drivers and a confidence level, and every product says plainly when something is uncertain or outside its evidence rather than inventing an answer — so results can be checked, not just trusted.
A scoped engagement on your own data with a measured outcome before any wider commitment: discovery, design, pilot, validation, then scale. You see the number before you sign for the rollout.
English and Arabic ship with full right-to-left layout and dialect handling. The retrieval and speech layers extend to further languages as your operations require.
Your team does — by design. Alongside every deployment, Epsilon's accredited academy transfers the skills to operate and extend the platform in-house, through a seven-level certification framework with 10,000+ professionals already trained. We stay on for support and continuous improvement, but you are never dependent on us to run it day to day.
Yes — most customers do. Every InsAI product is built on the same intelligence engine and one shared permission model, so you can start with the product that solves your most pressing problem and add others as you grow. Each new product joins your existing deployment as configuration rather than a fresh integration, sharing the same data and access controls.
The pilot
A scoped engagement on your own data, measured before any wider commitment.
Epsilon-managed access
Access to the working application is not self-serve. The Epsilon team arranges it after a guided walkthrough; accounts are individual and limited to an agreed evaluation period. The walkthrough starts on relevant sample data — preparing your own data or integrating it is agreed separately. Submitting the form does not create an account.
Explore the platform
Look through the public page — the screens and use cases.
Request a guided demo
Send the form with your company and your need.
Epsilon contacts you
We schedule a walkthrough tailored to your business.
Walkthrough on sample data
We demonstrate the relevant workflows on sample data.
Time-limited evaluation access
Individual credentials for an agreed evaluation period.
Follow-up on results
We review feedback, fit and requirements.
Implementation
Discovery → Pilot → Production — cloud, on-prem or hybrid.
Get started
Tell us the problem you are trying to solve. We will come back with a scoped walkthrough on your own documents, calls or projects — not a generic demo.
Start with the question you are asking today, or talk to the team about a scoped pilot on your own data. No generic AI presentation — start with the business problem.
