Products, use cases and customer evidence — in one place.
A consolidated client-facing view of the InsAI portfolio: what each product does, where it applies, how each workflow operates and the evidence behind the published outcomes.
7
enterprise products
26
documented use cases
10
case studies
Products
Construction intelligence
Foresight on every project
A vertical AI operating system that turns the project, cost and field systems you already run into foresight — and re-routes the programme itself when something slips, so the handover date holds.
Core capabilities
Predict delays before they happen. The forecast diverges from your baseline the moment risk appears, with the window, the cause and a confidence score attached.
Cost forecasting against escalation. Budget, committed and forecast in one view, with price escalation tracked against the published index so the claim is evidenced at valuation instead of reconstructed after it.
Procurement and supplier optimisation. Buy-now, wait or lock-price recommendations per material, with every supplier scored on delivery before you commit the order.
It acts on its own. Most decisions close without a human. When a delay lands it generates recovery paths, prices each in days and cash, and commits the one that holds the date.
A voice agent that listens in the language and dialect your caller actually uses, answers from the same governed knowledge base, and speaks the reply back — at any hour, at any volume.
Core capabilities
Speak naturally. Language and dialect are detected automatically. No mode to switch and nothing to declare before the caller starts talking.
Real-time transcription. The question is transcribed as it is spoken and interpreted in the context of the conversation so far.
The same grounded answers. Replies draw on exactly the knowledge channels that power RagINS — one corpus, one source of truth, one permission model.
Natural spoken replies. Answers come back as fluent generated speech, with the full transcript kept alongside for the record.
Every policy, contract, manual and report becomes answerable in plain language — with the source attached to every reply, so the answer can be checked rather than trusted.
Core capabilities
Layout-aware ingestion. Tables, figures and section hierarchy survive the upload instead of being flattened into a wall of raw text.
Organised knowledge channels. Give each team, product line or policy set its own isolated corpus, with its own list of who may read it.
Precision retrieval. Semantic search shortlists candidates, then a reranking pass reorders them so the answer at the top is the right one.
Answers you can check. Every response points back to the document it came from — and admits when the answer simply is not there.
The intelligent operating system for manufacturing
A vertical AI operating system that connects the ERP, MES, historian, quality and maintenance systems you already run, and predicts machine failure, quality loss and OEE drops before the shift feels it — each with the governed action to take.
Core capabilities
Predictive maintenance. A remaining-life estimate for each asset, so the fix lands in a planned window instead of an unplanned stoppage.
Quality prediction and traceability. SPC and inline vision catch drift at the source rather than at the end of the line, with lot and serial traceability behind every finding.
Demand and production planning. AI demand forecasting and a production plan sequenced against real capacity, changeovers and the material on hand, with procurement weighed against supplier risk.
Real-time OEE, from shop floor to boardroom. Production against plan, equipment effectiveness and machine health in one live command center, with critical assets flagged.
A vertical AI operating system that unifies your ERP, WMS, supplier and logistics data into one forecast — then says what to buy, hold, transfer or expedite, and flags the supplier or shipment about to slip while there is still time to act.
Core capabilities
Demand forecasting and inventory. Demand forecast per SKU and location with the drivers exposed, and safety stock and reorder points that protect service without trapping cash.
Supplier risk and procurement. Delivery, quality and price scored per supplier with a breach warning before it hits the line, and material price forecasts with a priced buy-now, wait or lock recommendation.
Shipments, ports and customs. Shipment, port and customs visibility end to end, with routes and modes compared on true landed cost and lead time.
Control tower. One exception feed — the stockout, the late PO, the breached lane — ranked by cost, with OTIF tracked in real time.
Build, orchestrate, govern and measure AI agents that do real business work across the apps you already use — in Sales, Finance, HR, Procurement, Operations and IT — with human approval and an audit trail on every action that matters.
Core capabilities
Agents that do real work. Agents understand a goal, decide, execute and report — across Sales, Finance, Customer Service, HR, Procurement, Operations, Marketing, Legal and IT.
Grounded in your knowledge and permissions. An agent draws on your company knowledge and can only touch what it is granted; permission is checked before any data reaches the model.
Human-in-the-loop, set by risk. Auto, notify, approve, dual-approve or forbid — per agent and per action. Reversible steps run inside policy; high-impact ones wait for a named person.
Measured in business terms. Every run is traced, costed and verified, so hours saved, cost reduced and value created are reported as they happen.
See financial risk before it reaches your cash flow
Connects the customers, invoices, payments, cash, credit, contracts and FX you already track across ERP, accounting and CRM into one live picture — then surfaces exposure early, quantifies it in EGP and recommends the governed action, with a named approver.
Core capabilities
Credit-risk monitoring. Every customer's exposure tracked against limit and payment behaviour, with the accounts turning risky flagged before they default.
Receivables and collections. Predicts who will pay late, prioritises the book by risk and value, and runs the collection sequence that recovers cash first.
Cash flow, liquidity and FX. Cash forecast weeks ahead with the gap surfaced before it opens, and unhedged USD and EUR positions quantified in EGP.
Anomaly and fraud detection. Transactions that do not fit the pattern — duplicate, out-of-band or unusual — flagged for review, with the reason explained.
Contractors & developersDesigned for: Main contractors, developers and project directors
Protect 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.
Sample data
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
1. Connect baseline schedule, cost plan, commitments and site progress.
2. Detect delay, cost and procurement signals continuously.
3. Generate recovery options priced in days, cash and resource impact.
4. Approve, execute and verify the selected intervention.
Infrastructure & utilitiesDesigned for: Infrastructure owners, utilities and delivery alliances
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.
Sample data
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
1. Unify package schedules, interfaces, permits and material dates.
2. Track leading risk across contractors and work fronts.
3. Simulate downstream time and cost effects before intervention.
4. Coordinate the selected response and monitor recovery.
Real estate & PMOsDesigned for: Portfolio PMOs, real-estate owners and development teams
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.
Sample data
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
1. Map project systems into one portfolio data model.
2. Calculate comparable health and exposure signals.
3. Drill from portfolio exception to its package-level cause.
4. Assign, approve and monitor the corrective action.
Executive reportingDesigned for: Executives, investment committees and project boards
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.
Sample data
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
1. Consolidate current schedule, cost, procurement and field signals.
2. Prioritise changes by time, cash and confidence.
3. Explain root cause, forecast consequence and available options.
4. Record the decision and track whether the expected recovery occurs.
Contact centresDesigned for: Customer service and contact-centre operations
Resolve 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.
Sample data
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
1. Answer immediately and detect language, dialect and intent.
2. Retrieve the approved answer or complete the permitted workflow.
3. Confirm the outcome with the caller in natural speech.
4. Escalate exceptions with transcript, intent and completed steps.
Field crewsDesigned for: Technicians, inspectors and mobile operations
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.
Sample data
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
1. Identify the worker, asset and active job.
2. Accept a spoken question in the worker's natural language.
3. Read back the relevant procedure, warning or checklist step.
4. Capture confirmation, notes and any escalation in the transcript.
Utilities & telecomDesigned for: Service providers with high-volume customer operations
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.
Sample data
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
1. Recognise the caller's issue and preferred language.
2. Check the permitted live service or account context.
3. Explain status, complete an approved request or collect details.
4. Transfer sensitive or unresolved cases with full context.
Healthcare intakeDesigned for: Hospitals, clinics and patient-access teams
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.
Sample data
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
1. State the purpose, privacy notice and non-clinical boundary.
2. Collect approved identity, appointment and administrative details.
3. Read back key information and ask the patient to confirm it.
4. Route urgent, clinical or uncertain cases to qualified staff.
AccessibilityDesigned for: Inclusive service and public-access teams
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.
Sample data
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
1. Greet the user and explain the available voice service.
2. Understand the request in natural language and dialect.
3. Guide one short step at a time and confirm important details.
4. Offer human assistance without forcing the user to restart.
Policy & complianceDesigned for: Compliance, legal and risk teams
Give 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.
Sample data
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
1. Ingest approved policies, procedures and regulatory circulars.
2. Retain owner, version, jurisdiction and effective-date metadata.
3. Answer staff questions in plain language from the current corpus.
4. Return the source and page, and log the interaction for review.
Contract reviewDesigned for: Legal, procurement and commercial teams
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.
Sample data
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
1. Load agreements, amendments, annexes and approved templates.
2. Preserve document hierarchy, parties, dates and contract family.
3. Ask for clauses, obligations, renewals or deviations in plain language.
4. Review the answer beside its exact contractual source.
Technical manualsDesigned for: Maintenance, engineering and operations teams
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.
Sample data
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
1. Ingest OEM manuals, service bulletins and internal procedures.
2. Tag each source by asset, model, revision and operating context.
3. Ask a fault, maintenance or specification question on site.
4. Follow the cited procedure and escalate exceptions to an expert.
Support desksDesigned for: Internal service desks and customer support
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.
Sample data
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
1. Connect approved FAQs, runbooks, product docs and resolved tickets.
2. Separate knowledge by service, customer and agent permission.
3. Retrieve a proposed answer while the request is being handled.
4. Verify the citation, respond and retain the interaction for learning.
Government recordsDesigned for: Government departments and public authorities
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.
Sample data
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
1. Connect departmental archives and scan image-based records with OCR.
2. Preserve classification, department, date and retention metadata.
3. Search across permitted collections in Arabic or English.
4. Return a cited answer and retain an auditable request record.
Predictive maintenance on a critical lineDesigned for: Plant & maintenance managers
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.
Sample data
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
1. Connect the historian/SCADA and the CMMS
2. Score remaining useful life per asset
3. Alert when life drops below the parts-lead-time threshold
4. Raise a work order to a named owner for the planned stop
Autonomous invoice matching, with approvalDesigned for: Finance & operations leads
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.
Sample data
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
1. Connect the ERP and document store
2. Agent matches invoice to PO and receipt
3. Clear clean items inside policy
4. Hold exceptions / above-limit for a named approver
Early-warning on customer creditDesigned for: Credit, risk and finance leaders
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.
Sample data
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
1. Connect your receivables ledger and payment history
2. Score credit and payment risk per customer
3. Flag accounts crossing the early-warning threshold
4. Route the account with its drivers to a named owner
Every published result stays connected to its source. Recommended delivery and measurement plans are identified separately from documented engagement evidence.
Unpredictable equipment failures were driving downtime that hit both production output and maintenance cost. The objective was to forecast failures rather than react to them.
What was built
A predictive maintenance system that forecasts issues, interprets technician feedback in natural language, and returns step-by-step repair instructions in real time.
Technical approach
Data collection
Machinery manuals digitised into JSON for metadata indexing.
Retrieval-augmented generation
BM25 retrieval over the manual corpus, generating repair instructions on demand.
Natural language understanding
BERT and RoBERTa models interpret what the technician typed and route to the right procedure.
Evidence status
The published case study documents an operational predictive-maintenance system combining equipment telemetry, failure prediction and grounded repair guidance.
Indicative delivery plan
Connect historical and live equipment data, validate failure thresholds, pilot alerts with the maintenance team, then expand to the approved asset population.
Recommended baseline
Recommended: the 90-day pre-deployment average for unplanned downtime hours, maintenance labour per work order and maintenance-related operating cost, normalised by equipment operating hours.
Recommended measurement method
Compare matched equipment over equal operating-hour windows before and after deployment. Exclude planned shutdowns, reconcile events with CMMS and production logs, and report monthly with the same asset population.
What the engagement delivered
The platform surfaces emerging equipment risks earlier and gives technicians the context to act before downtime escalates.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 5
Technology & data infrastructureAITReal-time data warehousing and machine learning platform−70%query latency+50%analytics accuracy+40%customer behaviour prediction
Published results
−70%query latency
+50%analytics accuracy
+40%customer behaviour prediction
Business challenge
A legacy SQL Server estate could not support real-time analytics or machine-learning-based decisions at the scale the business had reached.
What was built
Migration to a cloud warehouse with change-data-capture streaming, and custom models deployed on managed infrastructure.
Technical approach
Migration
SQL Server to Google BigQuery, with Kafka Connect carrying change data capture.
Kubernetes
A cluster hosting the Kafka installation so ingestion scales independently of query load.
Machine learning
Custom models built and served on Google Vertex AI.
Evidence status
The source documents a working real-time data and machine-learning platform, supported by reported improvements in query performance and analytics accuracy.
Indicative delivery plan
Benchmark the existing warehouse, introduce the streaming layer, migrate priority workloads, validate models on a frozen dataset, then complete a phased production cutover.
Recommended baseline
Recommended: median and 95th-percentile query latency on a fixed production-representative benchmark, plus model accuracy on a frozen, labelled validation set.
Recommended measurement method
Run the same query and model benchmark before and after migration at equivalent concurrency and data volume. Version the dataset and code, separate infrastructure gains from model gains, and have the data owner sign off the comparison.
What the engagement delivered
Teams reach current data faster, run analytics with greater confidence, and move machine-learning work into production with far less operational friction.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 6
E-commerce & retailNUT BotanicalsProduct recommendation engine+25%average order value+40%customer retention−20%acquisition cost
Published results
+25%average order value
+40%customer retention
−20%acquisition cost
Business challenge
Generic merchandising was limiting conversion and engagement. The objective was recommendations tied to each customer's actual behaviour.
What was built
A recommendation engine built on behavioural segmentation rather than catalogue similarity alone.
Technical approach
RFM clustering
Recency, frequency and monetary segmentation driving which products surface for whom.
Data preparation
Transaction and browsing history cleaned and joined into a single customer view.
Evidence status
The published engagement documents personalised recommendations operating on customer-behaviour data, supported by reported commercial uplift.
Indicative delivery plan
Unify catalogue and behavioural data, establish the commercial baseline, launch a controlled recommendation pilot, evaluate cohort results, then scale to eligible traffic.
Recommended baseline
Recommended: eight to twelve weeks of average order value, repeat-purchase retention and acquisition cost for eligible traffic before personalisation, segmented by channel and customer cohort.
Recommended measurement method
Use a randomised A/B test comparing existing merchandising with personalised recommendations. Keep pricing and campaign exposure balanced, report lift with confidence intervals, and measure retention on the same cohort window.
What the engagement delivered
Personalised recommendations make the shopping journey more relevant, lifting order value, strengthening retention and using acquisition spend more efficiently.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 7
Customer feedback was scattered across social platforms, marketplaces and surveys, with no way to read it as one signal or act on it in product decisions.
What was built
A sentiment classification engine that aggregates every channel and reports it against product lines.
Technical approach
Aggregation
Social media, e-commerce platforms and survey responses collected into one pipeline.
Classification
Naive Bayes, SVM and BERT models trained on the client's own labelled feedback.
Evidence status
The source documents a deployed feedback-intelligence capability spanning customer channels, supported by reported changes in satisfaction, product quality and complaint volume.
Indicative delivery plan
Connect feedback channels, agree the sentiment and issue taxonomy, validate labelled samples, route insights to product and service owners, then monitor corrective actions.
Recommended baseline
Recommended: a matched pre-deployment period for customer-satisfaction score, product-quality indicators and complaint volume across the same social, marketplace and survey channels.
Recommended measurement method
Use a phased rollout or matched-period comparison with stable category definitions. Audit a stratified sample for precision and recall, then link operational changes to dated product or service actions rather than model output alone.
What the engagement delivered
One consistent view of customer feedback lets product and service teams identify recurring issues and respond with focused improvements.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 8
Construction & engineeringRowad Modern EngineeringAI-based construction management system+30%operational efficiency−25%resource downtime+28%on-time delivery
Published results
+30%operational efficiency
−25%resource downtime
+28%on-time delivery
Business challenge
No real-time visibility across sites, and scheduling that could not respond to how resources were actually moving.
What was built
A project management layer with dynamic workflows, scheduling automation and tracking dashboards. This engagement is the lineage ConstructINS was built from.
Technical approach
Automated scheduling
Resource allocation and timeline adjustment driven by site progress rather than the original plan.
Real-time dashboards
Progress and reporting surfaces for project oversight across concurrent sites.
Evidence status
The published case study documents schedule and resource intelligence embedded in construction-management workflows, supported by reported delivery and downtime improvements.
Indicative delivery plan
Connect programme and resource data, validate the approved baseline, pilot variance alerts on selected work packages, calibrate with project controls, then extend across sites.
Recommended baseline
Recommended: the approved baseline programme, plus the prior 90-day average for resource downtime and operational throughput across the same sites or matched work packages.
Recommended measurement method
Compare planned and actual milestone performance across matched sites and packages. Record scope changes, weather and client-caused events separately, and tie each claimed recovery to a dated intervention in the project system.
What the engagement delivered
Project teams get earlier visibility of schedule and resource risks, and can commit recovery actions while there is still time to protect delivery.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 9
Transportation, legal tech & insuranceGovernment & insurance sectorAccident analysis and liability assessment−70%manual investigation timehoursto process a claim, from weeks
Published results
−70%manual investigation time
hoursto process a claim, from weeks
Business challenge
Manual accident investigation was slow and inconsistent between investigators, with no standard basis for attributing fault.
What was built
A mobile-enabled system that validates the vehicles present, classifies damage, and applies liability rules to produce a report at the scene.
Technical approach
Vehicle validation
YOLOv8 detection confirming which vehicles are actually in frame.
Damage assessment
Deep learning classification of damage type and severity.
Liability analysis
Spatial analytics combined with the applicable legal and insurance rules.
Evidence status
The source documents a field-to-claim accident-assessment workflow, supported by reported reductions in investigation and processing time.
Indicative delivery plan
Define the evidence schema and liability workflow, validate outputs against adjudicated cases, conduct a supervised field pilot, then introduce a governed production rollout with human review.
Recommended baseline
Recommended: median investigator handling time and end-to-end claim cycle time for closed pre-deployment cases, segmented by case complexity and evidence volume.
Recommended measurement method
Compare matched case cohorts, timestamp each workflow stage and exclude cases awaiting external evidence. Quality-assure a blinded sample against the final adjudicated outcome; report time savings separately from decision accuracy.
What the engagement delivered
Evidence capture and review are standardised, moving cases forward faster while retaining human oversight for consequential decisions.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 10
Corporate intelligence, legal & healthcareGovernment sectorRAG-based enterprise knowledge assistant−80%research and retrieval time
Published results
−80%research and retrieval time
Business challenge
Finding an answer meant knowing which department held the document. Retrieval across the organisation was the bottleneck, not the analysis that followed it.
What was built
An assistant over large-scale multilingual document collections, returning answers grounded in the retrieved source rather than generated from memory. This engagement is the lineage RagINS was built from.
Technical approach
Retrieval-augmented generation
Retrieved passages constrain the answer, and the citation travels with it.
Document processing
OCR pipelines handling structured and unstructured material together.
Semantic search
Vector embeddings for indexing across departmental boundaries.
Enterprise integration
Secure connections into existing BI, CRM and internal systems.
Evidence status
The source documents a production knowledge assistant operating inside the government entity's infrastructure across multilingual document collections.
Indicative delivery plan
Inventory approved documents and permissions, build ingestion and indexing pipelines, validate a representative query set and citations, complete security review, then roll out by department.
Recommended baseline
Recommended: median elapsed time from a representative staff question to a source-confirmed answer before deployment, sampled across departments, languages and document types.
Recommended measurement method
Repeat the same approved query set after deployment. Measure time to a verified source, citation correctness and answer completeness, and separately test that permission boundaries prevent retrieval from restricted collections.
What the engagement delivered
Source-backed answers are found across departmental knowledge in far less time, while access controls keep restricted information within the right boundaries.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 11
Public safety & security analyticsSecurity & law enforcement agencyDeepSight — video intelligence platform−90%human review labour
Published results
−90%human review labour
Business challenge
CCTV review was manual and therefore retrospective: footage was examined after an incident, not while one was developing.
What was built
Frame-level object recognition with anomaly detection, turning footage into searchable indexed metadata.
Technical approach
Object detection
Real-time YOLO detection of people, vehicles and objects.
Metadata extraction
Footage converted into timestamped, searchable records.
VMS integration
Connects to third-party video management systems already in place.
Evidence status
The published engagement documents an operational video-indexing and search capability, supported by a reported reduction in human review effort.
Indicative delivery plan
Onboard representative camera feeds, agree event categories, index a controlled footage set, validate detection and retrieval with reviewers, then expand by site and use case.
Recommended baseline
Recommended: reviewer minutes per hour of footage and per investigated incident before deployment, using the same camera mix, retention window and event categories.
Recommended measurement method
Compare matched footage and incident volumes before and after indexed search. Record human review minutes, false positives and missed events, and validate a blinded sample with authorised reviewers before operational reliance.
What the engagement delivered
Instead of watching hours of footage sequentially, reviewers move directly to the moments relevant to an investigation and verify them in context.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 12
Forensics, visual search & legal techLaw enforcement & IP protectionImageTwin — image similarity analysis−90%manual image search time
Published results
−90%manual image search time
Business challenge
Comparing an image against a large database was a manual, sequential task, which put a hard ceiling on how much could be checked.
What was built
Vector-based similarity matching that returns visually similar images instantly, including from degraded originals.
Technical approach
Visual embeddings
CLIP and ViT embeddings representing each image as a vector.
Image enhancement
Preprocessing that recovers usable signal from occlusion, blur and low resolution.
Nearest neighbour search
FAISS and Annoy indexes for high-speed similarity lookup.
Evidence status
The source documents a working visual-similarity search capability, supported by a reported reduction in manual image-search time.
Indicative delivery plan
Prepare and govern the image corpus, generate and index embeddings, benchmark retrieval on known matches, run a blinded analyst pilot, then scale the approved index.
Recommended baseline
Recommended: analyst minutes per search, number of candidates manually reviewed and verified-match rate on a fixed pre-deployment query set.
Recommended measurement method
Benchmark the same query images against the same frozen corpus before and after vector indexing. Measure system latency, human review time and precision/recall at a fixed result depth, using blinded analyst verification.
What the engagement delivered
Visual search replaces a slow manual comparison process with a focused shortlist, so analyst time goes to validating the strongest matches.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 13
Contact centre operationsMogassamVoice agent for inbound call handling86.7%calls contained
Published results
86.7%calls contained
Business challenge
Inbound call volume meant routine enquiries occupied agents who were needed for the calls that genuinely required a person.
What was built
A voice agent that answers, understands the caller in their own dialect, and resolves the enquiry end to end — handing over to a person only when it cannot. This engagement is the lineage VoiceINS was built from.
Technical approach
Speech recognition
Dialect-aware transcription, so a caller is understood without switching to formal Arabic.
Call containment
The agent completes the enquiry itself; containment is measured as calls closed without reaching a person.
Escalation
Hand-off to a person carries the transcript, so the caller does not repeat themselves.
Evidence status
The published 86.7% containment result documents operation on eligible inbound calls, with outcomes recorded through the call-centre workflow.
Indicative delivery plan
Map eligible intents and escalation rules, integrate telephony and CRM records, validate conversations on test traffic, launch to a limited call share, then increase coverage under quality monitoring.
Recommended baseline
Recommended: eligible routine inbound calls previously handled by people, with containment defined as a completed customer intent without human transfer and with a clearly stated repeat-contact window.
Recommended measurement method
Divide calls resolved without transfer by all eligible calls. Exclude tests, abandoned connections and forced-routing events; reconcile outcomes with telephony and CRM records, and monitor repeat contact within seven days as a resolution-quality check.
What the engagement delivered
The assistant resolves a large share of routine calls end to end, giving callers faster answers and leaving agents the conversations that need human judgement.
Epsilon's summary of the documented problem, solution and results.
Source: Epsilon AI Analytics implementation record
