Complete portfolio

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.

3: enterprise products
14: documented use cases
10: case studies

Products

Document intelligence

Ask your documents anything

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.

RAG.INS

Conversational intelligence

Speak, and be answered

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 RAG.INS — 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.

VOICE.INS

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.

ConstructINS

Use-case library

5 Use cases

Policy & complianceDesigned for: Compliance, legal and risk teams

Give every team the current policy answer — with the evidence attached.

RAG.INS turns policies, procedures and regulatory guidance into a governed answer service that cites the exact source and page.

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.

Expected outcomes

Faster, consistent policy answers
Less reliance on superseded documents
A reconstructable evidence trail

Required inputs

Policies and procedures
Regulatory circulars
Control frameworks
Audit guidance

Open dedicated use-case page

Contract reviewDesigned for: Legal, procurement and commercial teams

Find obligations, exceptions and commercial exposure across every agreement.

RAG.INS makes executed agreements searchable by meaning, so reviewers can compare clauses and verify every answer against the signed text.

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.

Expected outcomes

Shorter first-pass review
More consistent clause comparison
Earlier visibility of obligations and deadlines

Required inputs

Executed agreements
Amendments and annexes
Clause libraries
Procurement standards

Open dedicated use-case page

Technical manualsDesigned for: Maintenance, engineering and operations teams

Turn dense manuals into field-ready answers technicians can verify.

RAG.INS retrieves the right procedure, warning or specification from the approved manual set without making technicians search page by page.

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.

Expected outcomes

Less time searching technical documentation
More consistent maintenance execution
Faster onboarding for new technicians

Required inputs

OEM manuals
Service bulletins
SOPs and checklists
Equipment specifications

Open dedicated use-case page

Support desksDesigned for: Internal service desks and customer support

Give every support agent one governed answer across every knowledge source.

RAG.INS unifies runbooks, FAQs, product documentation and resolved cases into a cited assistant for faster, more consistent support.

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.

Expected outcomes

Faster first response
Fewer avoidable escalations
More consistent service quality

Required inputs

Knowledge-base articles
Resolved tickets
Runbooks and FAQs
Product documentation

Open dedicated use-case page

Government recordsDesigned for: Government departments and public authorities

Search public-sector records at scale without moving sensitive knowledge outside policy boundaries.

RAG.INS brings multilingual records, circulars and departmental archives into one permission-aware retrieval layer deployable on-premises.

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.

Expected outcomes

Faster cross-department retrieval
Stronger access and residency control
Traceable administrative decisions

Required inputs

Departmental archives
Scanned records
Circulars and legislation
Forms and correspondence

Open dedicated use-case page

5 Use cases

Contact centresDesigned for: Customer service and contact-centre operations

Resolve routine calls end to end — and hand complex calls over with context.

VOICE.INS answers in the caller's language and dialect, retrieves governed information, completes approved requests and escalates when a person is needed.

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 RAG.INS, 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.

Expected outcomes

Shorter queues at peak
Consistent answers across shifts
Agents focused on calls that need judgment

Required inputs

Knowledge channels
CRM customer context
Approved call flows
Escalation rules

Open dedicated use-case page

Field crewsDesigned for: Technicians, inspectors and mobile operations

Let field teams ask, confirm and record work while their hands stay on the job.

VOICE.INS gives mobile workers a spoken interface to procedures and job context, then records the interaction for the operational trail.

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.

Expected outcomes

Faster access to field guidance
Fewer avoidable supervisor interruptions
A clearer record of work performed

Required inputs

Work orders
Asset manuals
Safety procedures
Inspection checklists

Open dedicated use-case page

Utilities & telecomDesigned for: Service providers with high-volume customer operations

Handle outages, bills and service requests clearly when call volume spikes.

VOICE.INS combines live service context with governed knowledge to answer common enquiries and route account-specific exceptions safely.

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.

Expected outcomes

Elastic capacity during incidents
Clearer multilingual service
Fewer repeat explanations after transfer

Required inputs

Outage and network status
Billing guidance
Service catalogue
CRM and ticketing context

Open dedicated use-case page

Healthcare intakeDesigned for: Hospitals, clinics and patient-access teams

Collect structured patient intake by voice — without replacing clinical judgment.

VOICE.INS handles approved administrative intake, appointment preparation and service navigation, then routes clinical or urgent matters to qualified staff.

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.

Expected outcomes

More complete intake records
Better access across languages
Administrative staff focused on exceptions

Required inputs

Approved intake scripts
Appointment information
Service directory
Escalation and emergency rules

Open dedicated use-case page

AccessibilityDesigned for: Inclusive service and public-access teams

Offer a spoken route to service when screens, forms or typing create a barrier.

VOICE.INS lets people navigate information and approved service flows through natural speech, while preserving a clear path to human assistance.

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.

Expected outcomes

A more inclusive service channel
Less abandonment on complex flows
Continuity when human help is needed

Required inputs

Accessible service scripts
Knowledge and FAQs
Approved service workflows
Human-support routes

Open dedicated use-case page

4 Use cases

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.

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.

Expected outcomes

Earlier recovery action
Better protection of project margin
One accountable view of delivery

Required inputs

Programme and look-ahead schedules
Budgets and commitments
Procurement and supplier data
Field progress and BIM

Open dedicated use-case page

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.

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.

Expected outcomes

Earlier visibility of interface risk
More coordinated programme recovery
Clearer accountability across packages

Required inputs

Package schedules
Permits and approvals
Interface registers
Material and logistics plans

Open dedicated use-case page

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.

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.

Expected outcomes

Comparable reporting across projects
Attention directed by exposure
Less time spent consolidating reports

Required inputs

Project controls systems
Cost and cash-flow data
Milestones and handovers
Risk and issue registers

Open dedicated use-case page

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.

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.

Expected outcomes

Shorter, decision-focused reporting
A shared version of project truth
Clear ownership of corrective action

Required inputs

Schedule and milestone data
Cost, forecast and cash data
Procurement exceptions
Risk and action registers

Open dedicated use-case page

Complete customer evidence

Every published result stays connected to its source. Recommended delivery and measurement plans are identified separately from documented engagement evidence.

Manufacturing & industrial automationZylo LabsPredictive maintenance for industrial equipment−35%equipment downtime+40%maintenance efficiency−30%operational cost

Published results

−35%equipment downtime
+40%maintenance efficiency
−30%operational cost

Business challenge

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

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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

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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

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Consumer feedback & marketingNUT BotanicalsMultilingual customer sentiment analysis+30%customer satisfaction+25%product quality−20%complaint volume

Published results

+30%customer satisfaction
+25%product quality
−20%complaint volume

Business challenge

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

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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

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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

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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 RAG.INS 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

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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

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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

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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 VOICE.INS 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

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