Review of eLogii, Supply Chain Software Vendor

By Léon Levinas-Ménard

Last updated: November, 2025

eLogii is a cloud-based platform for route optimization and delivery or field-service execution operated by Brisqq Ltd, a UK company that originally built its own last-mile delivery network before spinning eLogii out as a standalone SaaS. The software models depots, vehicles, drivers, schedules, skills, capacities and customer tasks (pickups, deliveries, or field jobs) and then computes constrained vehicle routes using built-in optimization engines that solve variants of the Vehicle Routing Problem (VRP) with time windows and rich operational constraints. Once routes are computed, they are exposed to dispatchers via a web dashboard, executed by drivers using iOS/Android apps, and surfaced to end customers through tracking links with live ETA updates and proof-of-delivery capture. The platform also offers a REST API and webhooks for integration into ERP, WMS, CRM and e-commerce systems, and uses traffic-aware travel times with some machine-learning adjustments to refine ETAs and service durations over time. Commercially, eLogii appears as a small but established SaaS vendor (roughly a dozen employees and low single-digit million USD revenue), positioned squarely in last-mile routing and execution rather than broader supply chain planning or inventory optimization.

eLogii overview and positioning

At its core, eLogii is a multi-tenant SaaS application for route optimization and mobile workforce execution. The public product pages describe it as delivery management and route optimization software that lets fleets plan single-day, weekly, date-range or multi-day operations, with flexible optimization modes, multi-depot support, and advanced configuration of constraints and objectives.¹² The terms of service make it explicit that the service is provided by Brisqq Ltd, which does business as “eLogii” and licenses access to the eLogii website and related applications.³⁴

From a technical standpoint, eLogii is best understood as:

A data model for depots, vehicles, drivers, schedules, capacities, zones and tasks.
A pair of optimization engines (Base and Advanced) that construct routes under constraints and adjustable objectives.⁵⁶⁷
A web planning UI where dispatchers import tasks, configure optimization, and review routes and KPIs.¹⁸⁹
Driver mobile apps on iOS and Android to execute tasks and capture proof-of-delivery.¹⁰
A REST API + webhooks used to ingest orders and push status updates to external systems.¹¹¹²¹³
Tracking & notifications that expose live ETA, driver location and task status to end customers.¹¹⁴¹⁵¹⁶¹⁷

The platform is deployed purely as cloud SaaS (no on-premise offering is advertised) and is also listed in the UK government’s G-Cloud framework as an off-the-shelf cloud delivery optimization solution, with Brisqq Ltd identified as the supplier.¹⁴

Commercially, independent SaaS directories and profiles (Tracxn, CB Insights, IRONPROS, GetLatka) convergently describe eLogii as a small route optimization vendor founded around 2019–2020, with roughly 10–20 employees and revenue on the order of USD 1.5 million per year as of 2025.¹⁸¹⁹²⁰²¹ Brisqq itself has operated since 2014–2015 as a technology-enabled last-mile delivery provider, serving hundreds of brands in London and other cities.²²²³ eLogii’s client base spans logistics, 3PLs, retail and distribution, food & grocery, healthcare and field service operations.¹²¹⁶

Technologically, the evidence indicates a fairly standard modern web stack (Node.js + MongoDB back-end, React front-end, Cordova-based hybrid mobile apps) combined with a configurable VRP optimization engine and auxiliary machine-learning models for ETA refinement.²⁴⁷¹⁴ Relative to academic state-of-the-art in combinatorial optimization and probabilistic decision-making, eLogii’s approach appears competent and configurable but not demonstrably beyond typical commercial VRP solvers; its “AI” claims are mostly centered on improving ETA and analytics rather than end-to-end learned decision policies.¹⁷¹⁴²⁵²⁶

eLogii vs Lokad

Although both eLogii and Lokad market themselves around “optimization” in supply chain contexts, they address very different problem layers and use markedly different technical architectures.

Scope of decisions. eLogii focuses on execution-level routing and dispatch: deciding which driver should visit which stops in which order, subject to vehicle capacities, driver schedules, time windows and geographic constraints.¹⁵⁶ Its optimization engines solve a variant of VRP with time windows and capacities, then push these routes to driver apps and customer tracking pages.⁵⁶⁷¹⁰ Lokad, by contrast, focuses on tactical and strategic supply chain decisions such as demand forecasting, inventory replenishment, multi-echelon distribution planning, production scheduling and, in some cases, pricing.¹¹¹⁸²⁷²⁸ Rather than route individual trucks, Lokad computes probabilistic demand forecasts and then optimizes order quantities, stock allocations and other decisions to maximize expected economic outcomes (profit, working capital, service levels) across entire networks.

Treatment of uncertainty. eLogii’s public documentation describes deterministic optimization engines enhanced with traffic-aware travel times and “machine-learning improved” ETAs and service durations, but does not expose probabilistic models or full demand distributions.⁷¹⁴¹⁵¹⁰ The engines aim to minimize route duration, distance or vehicle count and balance workloads, using user-configured objectives and time limits.⁶⁷ Lokad’s published material emphasizes probabilistic forecasting as its “dominant predictive paradigm”, with full demand distributions generated using differentiable programming, and highlights objective evidence from the M5 forecasting competition, where its team ranked 6th overall and achieved #1 accuracy at SKU level out of 909 teams.²⁹¹¹¹⁸³⁰³¹³² These probabilistic forecasts feed into stochastic optimization algorithms designed to minimize the monetary cost of errors in supply chain decisions rather than purely statistical forecasting error.

Architecture and programmability. eLogii exposes a configuration-driven SaaS: users configure depots, fleets, constraints and optimization objectives via the dashboard or API; the underlying optimization logic is not programmable in a general sense.¹⁸⁵⁶⁷ Its stack is conventional (Node/React/Mongo, mobile apps via Cordova), and there is no public DSL or programmable modeling layer. Lokad, by contrast, centers its platform on a domain-specific language (DSL) called Envision, executed on a custom distributed VM, where all forecasting and optimization logic is expressed as code.²⁷³⁰²⁸ This allows clients (with Lokad’s assistance) to encode complex constraints and economic drivers (e.g., holding costs, stock-out penalties, BOM dependencies, maintenance schedules) and to adapt the models as business requirements change.

Depth of “AI”. eLogii and several third-party listings describe its platform as “AI-powered” or “machine-learning powered”, especially around ETA predictions and analytics.¹²²⁵²⁶ However, the only clearly documented ML use is for refining ETAs and service time estimates based on historic and traffic data; the route construction itself is portrayed as a constraint-based optimization process.⁷¹⁴¹⁵ Lokad’s “AI” claims are backed by more detailed technical materials, including probabilistic forecasting, differentiable programming pipelines and custom stochastic optimization algorithms, as well as public benchmarks like M5 and documented case studies (e.g., Air France Industries) showing large-scale applications.²⁹¹¹¹⁸³⁰³¹³³ In short, eLogii uses ML at the per-route level; Lokad uses ML and probabilistic modeling at the network-wide decision level.

Place in the supply chain stack. eLogii sits at the last-mile execution layer: it assumes orders/tasks already exist and focuses on delivering them efficiently. It does not forecast demand, compute replenishment plans, or optimize multi-echelon inventory. Lokad operates upstream as an analytical “brain” that generates demand distributions and decision recommendations (orders, allocations, production plans, pricing), which are then pushed into ERPs, WMS and, in some contexts, down into execution systems like TMS or routing engines.¹¹⁴¹⁸²⁷³³ In a stacked architecture, eLogii would be closer to a downstream routing engine; Lokad would be a mid/upper-layer decision optimizer feeding high-level plans.

From a buyer’s perspective, these tools are therefore complementary rather than interchangeable. eLogii is appropriate when the main challenge is “we have many vehicles and stops; how do we route them efficiently and execute with good ETAs and PODs?” Lokad is appropriate when the challenge is “what should we buy, where should we stock it, and how should we schedule production or maintenance under uncertainty to maximize profit or service?” Mixing them up would lead to misaligned expectations: eLogii is not a full supply chain planning system; Lokad is not a real-time routing engine.

Company background and history

Corporate identity and origins

The eLogii brand is operated by Brisqq Ltd, a company incorporated in England and Wales (company number 09226265) that originally offered crowdsourced last-mile deliveries in London.⁴²²²³³⁴ Brisqq’s own site describes it as a logistics-as-a-service enabler connecting businesses to a crowdsourced delivery fleet with one-hour delivery slots, serving close to 1,000 retail brands.²³ The eLogii terms of service and UK G-Cloud conditions state that Brisqq Ltd, doing business as eLogii, grants customers a license to access and use the eLogii website and related applications.³⁴

The eLogii word mark is registered as a UK trademark, filed March 2020, with Brisqq Ltd listed as the owner; the trademark covers software and related services.³⁵ Independent startup directories (EU-Startups, IRONPROS) describe eLogii as part of the “Brisqq Group”, with Brisqq operating the logistics-as-a-service business and eLogii providing a cloud delivery management and route optimization platform.³⁵³⁶²⁰

Several profiles indicate that Brisqq started operations around 2014–2015, while eLogii was launched as a SaaS product later, around 2019.²³¹⁸ Tracxn lists eLogii as founded in 2019,¹⁸ while CB Insights uses 2020 as the founding year.¹⁹ This discrepancy is plausibly explained by the difference between initial product launch (2019) and later corporate branding and trademark events (2020).

Founders and leadership

Multiple independent sources identify:

Andrew Mukerjee – Founder & CEO of Brisqq and co-founder / CEO of eLogii.²³¹⁸
Saagar Shah – Co-founder and commercial lead at eLogii, also Chief Commercial Officer at Brisqq, previously at Capco, Morgan Stanley and McKinsey.¹⁸³⁶
Leonard Budima – CTO of eLogii (and previously of Brisqq), with prior experience in power system and grid optimization software.²⁴

Public org charts and professional profiles confirm these roles and show a small core team of engineers and product staff.²⁰²⁴

Funding and ownership

Public funding information for eLogii itself is sparse and somewhat contradictory:

Tracxn describes eLogii as “unfunded”, with no recorded funding rounds.¹⁸
GetLatka lists an “M&A Offer” in April 2025, but does not show a completed transaction; it also reports eLogii as having raised $0 in disclosed funding and a team size of about 14.²¹
Another investment directory labels eLogii as “Seed stage” but without specifying investors or amounts.¹⁸

By contrast, Brisqq’s earlier operations were supported by several million dollars in funding according to older press coverage, though precise round details are not captured in the mainstream startup databases used here.²³

Given the lack of verifiable filings or press releases, the safest conclusion is that no publicly documented priced rounds exist for eLogii; it is likely financed primarily through Brisqq’s cashflows and earlier investments rather than traditional venture capital. Any labels such as “seed” should be treated as directory metadata, not confirmed financing events.

M&A activity

Tracxn notes that Brisqq has made no investments or acquisitions, and there are no public announcements that eLogii has acquired or been acquired by another company.¹⁸²² GetLatka’s “M&A Offer” entry for eLogii in April 2025 remains uncorroborated by any independent filings or news coverage, and should therefore be treated as an indication of interest or negotiation rather than a completed deal.²¹

Size and organization

Employee counts across sources are small but fairly consistent:

Tracxn and IRONPROS indicate a range of 11–50 employees.¹⁸²⁰
Contact-based tools and GetLatka suggest a team of ~14 people as of 2025.²¹²⁴

Combined with revenue estimates around USD 1.5M per year, this places eLogii squarely in the small SaaS vendor category, with a focused product and modest but non-trivial commercial traction.²¹

Product scope and functional capabilities

Problem domain

eLogii’s core domain is route optimization and delivery / field-service execution. The product pages and help center documents emphasize:

Industries: logistics and 3PL, retail and distribution, grocery and restaurant delivery, healthcare and pharma, postal and courier services, and field service operations such as pest control or facilities management.¹²¹⁶
Use cases: last-mile delivery, multi-drop distribution, scheduled service visits, pickup & delivery missions, and various forms of mobile workforce routing.¹²⁸

The central concept is the Task, representing a pickup, a delivery, or a field job with a specified location, time window, service duration, capacity consumption and optional customer attributes.⁸⁹ Tasks are assigned to drivers and ordered into Routes, where each route is a sequence of tasks to be executed by a single driver during a planning horizon.⁹⁶⁷

Master data and operations modelling

eLogii’s configuration model includes:

Depots – locations where vehicles can start/end routes, reload, or store inventory (warehouses, central kitchens, hubs, etc.).⁸⁹
Vehicles – defined with capacity (possibly multi-dimensional), attributes, and assignments to depots and drivers.⁸⁹²⁹
Drivers – with working schedules, team membership, skills, credentials and assigned vehicles.⁸⁹²⁹
Dimensions – generic capacity units such as weight, volume, pallets, boxes or order count used in capacity and utilization calculations.⁸
Zones – geographic partitions of the service area (postcodes or polygons) used for zoning constraints and driver assignments.⁸⁹

These entities can be configured through the dashboard, imported via CSV, or managed via the API.⁸⁹¹¹¹²¹³

Route optimization & dispatch

The Optimization help article and related documentation define optimization as the process of assigning tasks to drivers while respecting constraints and minimizing resource usage.⁹⁶ Key points:

Optimization can be run for:
- A single day.
- A range of dates.
- Multi-day routes (tasks spanning several days).
- Selected teams or subsets of tasks/drivers (e.g., “optimize for selected”).⁹⁶¹⁵²³
Two engines are exposed:
- Base (default) engine: aims to minimize total route duration, using as few drivers as possible while serving as many tasks as possible, considering constraints like vehicle specs, skills, zones and working hours.⁷¹⁵²³
- Advanced engine: exposes finer control over optimization objectives (e.g., minimize cost, distance, vehicles, route end time; balance workload by jobs, distance, duration; prioritize clusters, task priority/value) and includes tunable optimization time factors, at the cost of longer runtimes.⁷²⁹
Both engines support manual re-optimization and auto-optimization triggered by actions such as adding tasks or selecting routes and clicking “Reoptimize”.⁹³⁷¹⁷

The high-level goal is to complete as many tasks as efficiently as possible using the fewest drivers, subject to constraints such as capacity, time windows, zones and driver schedules.⁹⁶⁷ This is classic VRP with time windows and rich constraints.

Execution and driver workflow

Drivers interact through the Driver App, available on iOS and Android.¹⁰ The app:

Lets drivers log in with an organization ID, username and password.
Displays assigned tasks and routes in list and map views.
Provides navigation via external mapping apps (Google Maps, Waze, HERE, Yandex).
Allows drivers to update task status (arrived, completed, failed, etc.) and capture proof-of-delivery (signatures, photos, notes).¹⁰³⁸

Task status updates feed back into the central system, enabling live tracking and ETA recalculation.¹⁴¹⁵

Tracking, notifications and customer UX

eLogii supports email and SMS notifications triggered by task lifecycle events (e.g., route started, driver near, completed), containing tracking links where end customers can view:

Live ETA.
Live driver location.
Task history/events.
Proof-of-delivery.
Rating/feedback forms.¹¹⁴¹⁵¹⁶¹⁷

The tracking pages can be white-labelled with the client’s branding and restricted to show only certain fields.¹¹⁶¹⁷

Analytics and reporting

Marketing materials and comparison pages reference analytics and cost calculations as part of the platform, including:

Operational KPIs (completed tasks, on-time performance, driver utilization).
Cost estimation and fuel usage.
Historical performance comparisons (e.g., planned vs actual ETAs).¹²¹⁶³⁹

The documentation itself focuses more on operational configuration than on analytics, but customer reviews mention dashboards and reporting as part of the product.⁴⁰

Optimization engines, “AI” and technical depth

Deterministic optimization core

The Optimization Engines article provides the clearest look at the solver internals.⁷ The Base engine:

Uses historical and current route information (traffic conditions, average travel speeds) to compute travel times.
Constructs routes under constraints (vehicle specifications, skills, zones, driver schedules, time windows, capacity).
Minimizes total route duration and therefore tends to use the smallest set of drivers that can serve all tasks.
Supports multi-day routes and automatic depot reloads when capacity is exceeded.⁷²⁹¹⁵

The Advanced engine:

Builds upon the same data model but exposes custom objective functions, where users can prioritize different metrics: number of tasks, cost, distance, duration, vehicles, route end time, clustering, task priority, etc.
Includes load balancing modes: “most efficient route”, “balance number of routes” (by jobs, duration or distance), or “use all drivers / finish as soon as possible”.
Has controls for optimization time factors and time limits, letting users trade solution quality against runtime.⁷²⁹

This configuration is consistent with a metaheuristic VRP solver (e.g., large neighborhood search, tabu search, genetic algorithms), though the specific algorithms are not disclosed. The presence of time limits and multiple objectives strongly suggests heuristic search rather than exact mixed-integer optimization.

ETA computation and traffic

eLogii’s ETA documentation describes a separate layer for ETA scaling and route ETA calculation.¹⁴ Key elements:

Travel times can include static or dynamic traffic data, depending on configuration and map provider.
The system maintains both planned ETAs (at planning time) and live ETAs, updating the latter as drivers progress and new information comes in.¹⁴¹⁵
Users can disable live ETA updates if they prefer to keep original estimates constant.¹⁵

The main marketing site claims that “eLogii enables the most accurate ETAs on the market [and] ETAs are constantly improved by Machine Learning as real-life data flows through the platform.”¹² CB Insights similarly summarizes eLogii as providing “machine learning-powered ETA predictions.”¹⁹ Together, these indicate that machine learning is primarily applied to calibrate travel times and service durations based on historical execution data, not to replace the core combinatorial optimization engine.

“AI-powered” claims

Third-party listings such as Omdena and Daidu.ai describe eLogii as an “AI-powered logistics platform” with intelligent route optimization and analytics.²⁵²⁶ However, no public source (documentation, papers, patents, open-source code) details the architecture of these AI components beyond the ETA and analytics hints above.

A cautious technical reading is therefore:

The central optimization remains a constraint-based VRP solver, with classical OR heuristics.
Machine learning appears to be used at the level of ETA prediction and performance analytics, not as a general policy learner for route construction.

As a result, while it is accurate to say that eLogii uses ML-enhanced routing, it would be misleading to treat it as a deeply AI-native decision system in the same sense as platforms that implement end-to-end probabilistic decision optimization.

Architecture and technology stack

High-level architecture

From the API docs, help center, and general behaviour of the platform, we can infer:

Multi-tenant cloud backend:
- REST API over HTTPS (api.elogii.com and sandbox subdomains) with resource-based URLs and JSON payloads.¹¹¹²¹³
- Resources for tasks, customers, depots, drivers, vehicles, routes, zones and optimization jobs.¹¹¹²¹³
- Webhooks for event-driven integration with external systems.¹¹¹³
Web dashboard:
- Single-page application at dash.elogii.com or similar, used for configuration, planning, and analytics.¹⁸⁹
- Provides map and timeline views of tasks, driver routes and optimization results.¹²⁸
Mobile apps:
- iOS/Android apps built using Cordova and JavaScript frameworks, used by drivers to execute tasks and capture PODs.¹⁰²⁴
Mapping providers:
- Integration with external map and navigation engines (Google Maps, Waze, HERE, Yandex) for geocoding and navigation.¹⁰³⁸
Optimization service:
- Backend service invoked when users click “Optimize” in the planning screen or via the API; uses the Base or Advanced engine along with any custom optimization options or presets.⁹⁷²⁹¹³¹⁷

This overall pattern is broadly aligned with other modern logistics SaaS platforms.

Languages and frameworks

eLogii does not publish an official tech stack, but multiple developer profiles on independent sites (e.g., TheOrg) indicate that engineers working on eLogii used:

Node.js with the Hapi framework and Mongoose (MongoDB ORM) on the backend.
MongoDB as a primary data store.
React for the single-page dashboard UI.
Cordova for hybrid mobile applications.²⁴

These hints, combined with the observable behaviour of the application, support the inference that eLogii runs on a JavaScript-heavy stack (Node + React + MongoDB) with Cordova-based drivers apps—technically mainstream and well-understood, rather than exotic.

Deployment and roll-out methodology

Onboarding flow

The Getting Started guides outline a typical implementation sequence:⁸⁵⁹

Account setup and login on the web dashboard.
Organization settings, especially timezone configuration (critical for correct ETAs and planning horizons).
Master data configuration:
- Define depots, dimensions, vehicles, drivers and teams.
- Configure driver working hours and exceptions.
Task ingestion:
- Create tasks manually, import CSVs with addresses (geocoded on import), or send tasks via API from ERPs, WMS, CRM, e-commerce, etc.⁸⁹¹¹¹³
Planning and optimization:
- Use the planning screen for a specific day or range, view unassigned tasks and available drivers, and trigger optimization with the Base or Advanced engine.⁹⁷²⁹
Execution:
- Drivers install and log into the app, follow their route, update task statuses and collect PODs.¹⁰
Tracking and notifications:
- Configure notification templates and tracking links; monitor live ETAs and task completion from the dashboard.¹⁴¹⁵¹⁶
Integration and automation:
- Use API keys, webhooks and auto-optimization to integrate with upstream systems and automate parts of the flow.¹¹¹³³⁷¹⁷

Deployment model

eLogii is sold exclusively as a cloud service; no on-premise deployment options are advertised. This is consistent with:

G-Cloud documentation describing the service as “Software as a Service”.⁴
Public pricing and plan comparison pages showcasing subscription tiers with different feature bundles rather than perpetual licenses.²³

Implementations are thus primarily about data integration and configuration rather than software installation.

User-reported experience

User reviews on major software directories (Capterra, SoftwareAdvice, SourceForge) consistently highlight:

Usability of the interface.
Ease of implementation compared to legacy tools.
Ability to scale from small fleets to multi-location operations.⁴⁰

These are anecdotal but broadly corroborate eLogii’s positioning as a relatively lightweight, quickly deployed SaaS rather than a heavy enterprise suite.

Clients, sectors and geographies

Named clients (self-reported)

eLogii’s marketing materials and job ads list several named customers:

Northern Care Alliance / NHS (UK) – NHS trust quoted as using eLogii to improve facilities logistics.¹²⁴⁰
Porcelanosa – Spanish ceramics/tiling group, cited as a global client.⁴⁰
Ananas – E-commerce player featured in case material.²¹⁶
Vergo Pest Management (formerly Terminix UK) – Subject of a case study about field-service optimization.¹⁶¹⁷
Richburns and Baycorp – Debt collection and field-service clients, featured in case studies focused on route efficiency and digitalization.¹⁶⁴¹

A senior content writer job ad at Brisqq explicitly states that eLogii’s technology is “trusted by global clients, including Porcelanosa, Ananas and the UK National Health Service.”⁴⁰

Important caveat: these references are self-reported by eLogii/Brisqq; independent confirmation via client press releases or third-party news coverage was not found in the sources reviewed. They are therefore plausible but not externally verified.

Sector and geography

Across sources, eLogii is consistently positioned as:

Serving retail, e-commerce, grocery, logistics/3PL, healthcare, postal/courier and field-service industries.¹²¹⁶³⁵²⁰²⁵
Headquartered in London, UK, with operations that can support multi-country routing.¹²²³⁵¹⁹²⁰

Inclusion in the UK G-Cloud framework and presence in public-sector marketplaces suggests at least some adoption in UK government or quasi-government contexts.⁴ eLogii is also listed by logistics technology analysts and comparison sites as one of several route optimization SaaS offerings.¹⁶³⁵²⁵³⁹

Customer scale

No official numbers of customers are disclosed, but:

Brisqq claims close to 1,000 brands using its logistics-as-a-service operations.²³
GetLatka estimates eLogii revenue at around USD 1.5M in 2025.²¹
Major software review platforms host dozens of reviews for eLogii, implying at least dozens (likely more) of paying or trial customers.⁴⁰

Overall, this supports a picture of moderate commercial adoption: eLogii is neither a tiny experimental product nor a large enterprise vendor, but a small, active SaaS player.

Commercial maturity and market position

Synthesizing the evidence:

Age: eLogii has been on the market since roughly 2019–2020.¹⁸¹⁹
Team: around 14 employees, expanding slightly over time.²⁰²¹²⁴
Revenue: approximately USD 1.5M/yr as of 2025, according to one external estimate.²¹
Funding: no publicly documented rounds; likely founder/parent-funded rather than VC-backed.¹⁸¹⁹²¹
Customer base: spread across multiple industries, with a mix of SMB to mid-market deployments plus some larger names claimed but not independently validated.¹²¹⁶⁴¹⁴⁰

This profile is consistent with a small but established SaaS vendor with a clearly defined niche (route optimization and execution) and several years of real-world usage, but without the capital or breadth of a major enterprise planning software provider.

Gaps, uncertainties and discrepancies

A few points remain uncertain or contradictory:

Founding year – Tracxn and some bios say 2019; CB Insights and some directories list 2020.¹⁸¹⁹ This likely reflects the difference between initial product launch and later corporate/trademark milestones.
Funding and M&A – Directories disagree on whether eLogii has “seed” funding or is fully unfunded; GetLatka reports an “M&A offer” with no evidence of completion.¹⁸¹⁹²¹ In absence of formal filings, it is safer to treat these as unverified.
Client references – Named clients (NHS, Porcelanosa, Ananas, Vergo, Richburns, Baycorp) are only found in eLogii’s own marketing and job materials; there are no independent press releases confirming specific deployments.¹⁶¹⁷⁴¹⁴⁰
Depth of AI – eLogii and third-party sites frequently use “AI-powered” language,¹²²⁵²⁶ but the only concretely described ML use concerns ETA and service time estimation; there is no public technical detail or benchmarking for any deeper AI components.

These uncertainties do not undermine the core characterization of eLogii as a route optimization and execution platform, but they do limit how far one can go in assessing its claims about funding, marquee clients, and AI sophistication.

Conclusion

From a strictly technical and evidence-based perspective, eLogii delivers a competent, configurable SaaS platform for last-mile route optimization and mobile workforce execution, operated by Brisqq Ltd out of London and used across a range of logistics, retail and field-service contexts. The platform’s capabilities—multi-day VRP, rich constraints, driver apps, tracking links, notifications and integrations—are well documented and roughly on par with contemporary commercial route optimization offerings.¹⁵⁶⁷¹⁰

The optimization engines are clearly designed around classical operations research: a base heuristic VRP solver and an advanced engine with multiple objectives and load-balancing modes, both adjustable with time limits and presets.⁶⁷²⁹ Machine learning appears to play a targeted role in refining ETAs and service durations rather than driving core route construction.⁷¹⁴¹⁵ As a result, eLogii’s “AI-powered” branding should be interpreted as OR-plus-ML-enhanced routing, not as a fundamentally AI-native decision system.

Architecturally, eLogii uses a mainstream Node/React/Mongo stack with Cordova-based mobile clients, packaged as a multi-tenant SaaS with REST APIs and webhooks.¹¹¹²¹³²⁴ This design is pragmatic and familiar, but not unusual. Commercially, the company has grown to a modest but stable scale (low millions in annual revenue, roughly a dozen employees, several dozen or more customers) without publicly visible venture funding.¹⁸²⁰²¹

Relative to Lokad, eLogii operates at a different layer of the supply chain technology stack: it optimizes vehicle routes and orchestrates execution, whereas Lokad optimizes what to buy, stock, produce or price under uncertainty using probabilistic forecasting and custom decision models.¹¹⁴¹⁸²⁷³⁰³¹³³ For organizations that already have robust demand and inventory planning but lack modern routing and execution tools, eLogii can fill an important last-mile gap. For those seeking end-to-end, uncertainty-aware optimization of their entire supply chain, a tool like Lokad targets a much broader problem class.

In summary, eLogii is best characterized as a focused route optimization and delivery execution vendor with a solid OR-based engine, modest ML enhancements, and small but real market traction. Its strengths lie in operational flexibility and execution features; its limitations are lack of transparency into algorithmic internals, limited evidence for deep AI capabilities, and a commercial footprint appropriate to a small independent SaaS rather than a large enterprise platform.