SOFIAH NICHOLE SALIVIO

News Editor

Peer Software has launched PeerGFS v6.4, targeting organisations that manage distributed file infrastructure across multiple sites.

The update to its Global File Service platform focuses on faster file synchronisation and replication across SMB and NFS workloads. It also aims to improve resilience during large file transfers, strengthen auditing of configuration and management activity, and enhance edge data management in environments with limited bandwidth.

The release is aimed at businesses handling large and growing volumes of file data across on-premises, cloud and hybrid environments. Peer Software highlighted sectors such as semiconductors, healthcare and life sciences, and AI and machine learning as examples of industries facing heavier data demands and more complex application workloads.

Many of these organisations are distributing compute workloads across multiple locations and turning to cloud infrastructure for additional capacity. That increases the need to keep file data synchronised and accessible across sites so applications and users can work from current versions of the same information.

Performance changes

PeerGFS v6.4 introduces faster scheduled scans and real-time replication for both SMB and NFS environments. These protocols remain widely used in enterprise storage estates, particularly where businesses need to share file-based data between teams, applications and locations.

The release also aims to reduce disruption when connectivity drops between distributed sites. In those cases, the software is designed to resume large file transfers more effectively instead of forcing organisations to restart data movement from the beginning.

The update also expands auditing, with upgraded tracking of configuration and management activity. That may matter to companies that need stronger oversight of operational changes across complex storage environments.

Edge data management is another focus in the new version, with improvements to performance, reliability and manageability in distributed environments where bandwidth constraints can make data movement and synchronisation harder to control.

Distributed workloads

The release comes as more businesses place data and applications closer to available computing resources rather than keeping everything in a single data centre. That shift has increased pressure on IT teams to maintain file consistency across dispersed infrastructure without creating delays for engineers, researchers and other users working with large data sets.

In sectors such as chip design and life sciences research, file-based workflows often involve teams in different locations accessing and updating shared data. This can make synchronisation software an important layer for preventing version conflicts, reducing downtime and maintaining continuity when workloads move between on-premises and cloud systems.

Jimmy Tam, Chief Executive Officer of Peer Software, described what the company sees as the challenge facing customers.

“As organisations scale data-intensive workloads across data centres, edge locations and cloud environments, the ability to keep data synchronised, accessible and close to compute has become a critical operational imperative,” said Jimmy Tam, Chief Executive Officer of Peer Software.

He said the product is intended to reduce friction in distributed file management for customers operating global infrastructure.

“PeerGFS helps customers eliminate the friction of distributed file management by improving performance, increasing resiliency and giving IT teams greater control over how data moves across their global infrastructure. The result is a more efficient foundation for supporting modern engineering, semiconductor, life sciences, and AI-driven workloads at scale,” Tam said.

Peer Software sells file services and data management software used to synchronise, replicate and share file data across on-premises, cloud and hybrid environments. Its Global File Service platform is used by enterprises seeking data consistency, compliance support and business continuity across multi-site infrastructure.

ArvatoConnect has appointed Richard Maynard as Chief Operating Officer and Amjad Khan as Director of Operations as it targets regulated organisations reviewing customer service models in the AI era.

Maynard joins with senior leadership experience at Vodafone, while Khan brings experience across financial services, utilities, insurance and housing. The appointments are intended to strengthen ArvatoConnect’s UK operations as routine customer service work shifts to digital agents and businesses reassess the balance between automation, compliance and human support.

The company, which employs about 800 people in the UK, is positioning itself around a model that combines AI-led processes with UK-based teams handling more complex interactions. The approach is aimed at sectors where customer operations are closely tied to regulation, treatment of vulnerable customers and brand risk.

Research cited by ArvatoConnect suggests attitudes to reshoring are changing. It found that 73% of UK customer experience leaders would bring customer service operations back to the UK if cost were not a factor, 34% plan to do so within 12 months, and 46% already run some customer service activity in the UK.

The shift reflects a wider change in contact centre economics. Work once sent offshore because it was repetitive and high-volume is increasingly being handled by AI systems, reducing the labour cost advantage that underpinned many offshoring decisions.

Operational focus

Maynard has more than 20 years of experience in large-scale UK and international contact centre and business process outsourcing operations. In his new role, he will oversee operational standards and service delivery as the company expands.

“Customer service is moving beyond a simple volume model. AI can increasingly handle routine activity and improve the customer journey, but the interactions that remain human often matter more to the customer and to their brand,” said Richard Maynard, Chief Operating Officer, ArvatoConnect.

“I was drawn to ArvatoConnect by the opportunity to strengthen operational discipline while building on an already strong culture – one that clients rely on when service is not just about efficiency, but about protecting relationships, reputation and long-term value,” said Maynard.

Khan brings more than 15 years of experience in customer operations transformation, including work on AI, automation and digital change programmes. He will oversee customer experience and business process outsourcing delivery across ArvatoConnect’s UK sites.

His background also includes experience with Financial Conduct Authority regulation and Consumer Duty requirements, areas that have become more prominent as regulated firms face closer scrutiny of customer outcomes and service governance.

Regulated sectors

Businesses in banking, insurance, utilities and housing have been under pressure to cut costs while showing that automated and outsourced processes do not weaken compliance controls. That has increased demand for providers that can demonstrate how operational design, oversight and staff behaviour align with regulatory expectations.

“In regulated sectors, it’s not enough for providers to say they understand regulation and compliance. They have to prove it in how services are designed, governed, measured and continuously improved – as well as translating it into day-to-day behaviours,” said Amjad Khan, Director of Operations.

“What stood out to me about ArvatoConnect is its maturity in embedding regulatory thinking into everyday operations, so clients can protect customers in every interaction, support good outcomes and reduce operational risk,” said Khan.

ArvatoConnect is part of Bertelsmann and is headquartered in Datchet, with other offices in Swansea, Newcastle and Willerby. It works across private and public sector organisations, with an emphasis on customer and citizen service operations.

Debra Maxwell, Chief Executive Officer at ArvatoConnect, said the hires reflect a broader market shift away from traditional volume-driven contact centre contracts.

“Richard and Amjad join us at a pivotal moment for the customer service market, as clients look for more than a traditional contact centre or volume-based BPO model,” said Debra Maxwell, Chief Executive Officer, ArvatoConnect.

“Regulated organisations are under pressure to reduce cost, adopt AI responsibly and protect customer trust at the same time. Our difference is that we bring those priorities together through a UK-based model, deep regulatory expertise and the long-term investment needed to redesign customer journeys properly. Their appointments further support us in leading this shift,” said Maxwell.

Searchable found that 93% of London businesses in its study were described inaccurately by major large language models. The research focused on small and medium-sized enterprises and compared their results with those of larger companies.

It tested ChatGPT, Perplexity and Gemini more than 13,000 times on 165 London businesses, asking about services, contact details, staffing and other identifying facts. The responses were then checked against official LinkedIn profiles and Companies House records to determine whether the information was correct, incomplete or missing.

The results suggested a sharper problem for smaller firms. Half of SMEs received at least one false fact from an LLM, compared with 32% of large companies, a 56% higher rate of fabricated information for SMEs.

Across all prompts, 11 in 100 questions about SMEs returned false or missing information on key brand facts, compared with 7 in 100 for large companies. The study also found that LLMs were twice as likely to fabricate information about an SME, with a 5% fabrication rate versus 2% for larger brands.

Brand confusion was another issue. The findings showed that LLMs misattributed or confused SME brand names at a rate of 4%, compared with 0.7% for large companies, suggesting smaller businesses are more vulnerable when users rely on AI tools for basic discovery queries.

Discovery gaps

The weakest areas for accuracy were company size, website, founding year, phone number and services. These are among the details prospective customers often seek when deciding whether to contact a business.

Inaccurate responses included verifiably false facts, incomplete listings that covered at least 30% fewer essential services, or answers that failed to retrieve any verifiable existing information about a company.

The analysis adds to a wider debate about the reliability of consumer-facing AI tools as they become part of online search and recommendation behaviour. For smaller businesses, errors in basic facts could affect discovery when users are looking for a supplier, a contact number or confirmation that a company offers a specific service.

Chris Donnelly, co-founder of Searchable, linked the problem to the way language models are trained on public web data. He said smaller companies may be less visible to such systems because they are mentioned less often online than larger brands.

“Right now, if someone asks ChatGPT about a local company, there’s a very real chance the AI either makes something up or draws a blank on key information. That’s the equivalent of customers and revenue walking straight past businesses they should be connecting with.

The issue is related to how LLMs work. They’re trained on publicly available web data, which often skews toward larger, more widely referenced brands. A London SME with a smaller digital footprint is less likely to register among what AI is reading and citing to its users,” Donnelly said.

His comments reflect concern that AI systems may reproduce an existing imbalance in online visibility. Large businesses typically have broader media coverage, more backlinks, more directory listings and stronger digital footprints, all of which can make them easier for models and search systems to identify consistently.

At the same time, the findings suggest the gap is not fixed. Searchable argued that AI-driven discovery does not necessarily entrench incumbent brands in the same way as traditional search rankings, creating room for smaller companies to improve how they are represented.

“At the same time, AI doesn’t cement big brands as favourites in the way that traditional search engines do. The playing field can be levelled more quickly for a well-optimised SME that understands how to make itself visible to these systems,” Donnelly said.

How it was tested

The study covered 165 London businesses and generated 13,365 AI responses. Questions covered location, business size, services, specialties, website, founding year, phone number and other identifying facts.

A response was classed as inaccurate if it contained false information, omitted a significant share of essential services or failed to return verifiable information that existed in public records. That means the research measured not only outright hallucinations but also cases where AI systems appeared unable to surface established facts about a business.

The results indicate that while AI assistants are becoming a common route for gathering information, their performance on local business discovery remains inconsistent. For SMEs that depend on accurate digital visibility, the data suggests a missing phone number, a wrong service description or a mistaken identity can still appear when a potential customer asks an AI a simple question.