Case studies : Work we have done, with a named lead on each.

The challenge

Customers required ISO 9001 and a key contract now required ISO 27001. Two parallel systems meant duplicated policies, audits, and reviews.

What Golonex did

• A named Golonex lead owned both standards as one system, built on the structure they share
• Merged the common parts once: scope, leadership, document control, internal audit, corrective action, and management review
• Built what is unique to each: process control for 9001; risk assessment, Statement of Applicability, and Annex A controls for 27001
• Prepared the organization for its certification audit

The outcome

• Far less duplicated paperwork than two separate systems
• One team, one calendar, one review
• Evidence ready for the certification body

The challenge

A prime’s flow-down clause required CMMC Level 2. The self-assessed score could not be defended, and nobody inside owned the controls.

What Golonex did

• A named seat-holder (vCISO) took ownership of the program and the evidence
• Scoped where Controlled Unclassified Information lives and drew the assessment boundary
• Closed gaps against the 110 controls and wrote the System Security Plan and POA&M
• Ran a mock assessment, then stayed on to keep the evidence current

The outcome

• A score backed by evidence, not guesswork
• An evidence pack the assessor expects
• An owner who faces the assessor with the client

The challenge

EU customers sent data protection, AI governance, and security questions the company could not answer. Data flowed to the US without a documented transfer mechanism.

What Golonex did

• A named seat-holder took ownership of the EU obligations and customer conversations
• Mapped EU personal data, built the record of processing, and documented every transfer
• Inventoried AI systems, classified risk, and put transparency, oversight, and documentation in place
• Answered flowed-down security requirements from one control program

The outcome

• EU questionnaires answered from the same evidence every time
• A documented position on every AI system
• Procurement reviews that no longer stall deals

The challenge

A managed service provider grows one client at a time, and every new contract needs engineers ready to work. Hiring, training, and keeping system, cloud, and DevOps engineers in step with client demand is slow and expensive.

What Golonex did

• Staffed managed teams of system, cloud, and DevOps engineers, hired and supervised by Golonex
• Put a named Golonex supervisor over the team, owning quality, review, and communication
• Worked inside the provider’s own tools, ticketing, and processes
• Scaled the team with the provider’s client base

The outcome

• Capacity to take on new clients without a hiring cycle
• One point of accountability for the whole team
• Engineers who work the provider’s way from day one

The challenge

A consulting house wins projects faster than it can hire. Each project needs the right engineers at the right time, and the house’s own leaders cannot spend their time managing individual developers.

What Golonex did

• Staffed managed teams of software and AI engineers for the consulting house’s client projects
• Put a named Golonex lead on each team, owning delivery, code review, and communication
• Matched team size to the project pipeline
• Worked as part of the consulting house’s own delivery, with its methods and its clients

The outcome

• Projects staffed without a hiring cycle
• Delivery quality the consulting house can put its name to
• All code and IP kept with the consulting house and its clients

The challenge

The firm needed a capability center staffed with skilled people it could not find locally. Each hire meant sourcing abroad, agreeing terms across borders, and securing a business visa, with employer, candidate, and Golonex kept aligned.

What Golonex did

• Defined with the client the roles the capability center needed
• Sourced and vetted international candidates for those roles
• Ran the tripartite interactions between employer, candidate, and Golonex: interviews, offers, terms, and start dates
• Coordinated each candidate’s business visa application and supporting documents

The outcome

• A capability center staffed and working
• One coordinator for three parties, from first interview to first day
• Visa paperwork handled without burdening the client’s team

The challenge

Metro stations run their operational systems over one network. A link or switch failure during service hours is not an inconvenience, it is a station going dark.

The managed provider responsible for the stations needed an engineering partner who could design for fast failover and keep the network running to that standard.

What Golonex did

• Designed and engineered redundant station networks built for fast failover
• Tuned convergence so traffic recovers in milliseconds when a link or switch fails
• Separated traffic so busy systems cannot starve critical ones
• Supported the provider with monitoring, change control, and escalation

The outcome

• Station networks that recover from failures in milliseconds
• A managed provider backed by a dedicated engineering partner
• Fast failover engineered into the design, not left to chance

The challenge

For a SaaS company the infrastructure is the product. An outage, a slow release, or a failed restore is felt by every customer at once, and small teams cannot watch production around the clock.

What Golonex did

• Designed production environments for high availability, with no single point of failure
• Ran monitoring and alerting on every live service
• Managed patching, scaling, and change control without interrupting customers
• Tested backup and recovery, so restores are proven rather than assumed

The outcome

• SaaS products that stay up for their customers
• Releases and changes that do not cause outages
• Recovery that has been tested before it is needed

The challenge

Production planning, stock, purchasing, and customer orders lived in different places. Off-the-shelf ERP did not fit how the plant worked, and nobody owned the system end to end.

What Golonex did

• A Golonex lead owned the platform and roadmap, with a dedicated team
• Built the ERP around the plant’s process: bills of materials, work orders, inventory, purchasing, and dispatch
• Built the CRM for the customer side: accounts, enquiries, quotes, and orders, connected to production and dispatch
• Integrated it with the finance system so data is entered once
• Delivered it as a fixed-scope build priced per milestone, then stayed on to maintain it

The outcome

• One system for the plant, stores, purchasing, sales, and finance
• Customer orders visible from quote through to dispatch
• Manual re-keying removed between departments
• A platform the client owns and Golonex keeps improving

The challenge

Callers waited for routine requests. Any automation had to meet GDPR and the transparency the EU AI Act expects of AI that talks to the public.

What Golonex did

• Built a custom agent harness that orchestrates each call: Twilio for telephony, ElevenLabs for natural speech, and a language model to decide and act
• Connected it to the client’s systems so the agent completes the task, confirming before it acts
• Handed calls to a person, with context, whenever the agent was unsure
• Managed privacy and compliance: AI disclosure at the start of every call, a lawful basis for recording, an impact assessment before launch, processing agreements with each vendor, and retention limits

The outcome

• Routine calls handled without waiting for an agent
• A full audit trail of every call and action
• A deployment the client can defend to a regulator under GDPR

The challenge

The team answered the same shopper questions all day, about products, stock, sizing, and orders, and questions outside working hours went unanswered.

What Golonex did

• Built an AI chatbot connected to the Shopify store, answering from the live catalog and real orders
• Helped shoppers find products, check stock, and track orders
• Shared order details only after verifying the customer
• Handed conversations to staff with full context when needed, and kept improving the answers after launch

The outcome

• Shoppers answered at any hour
• Staff time freed from repeat questions
• Conversations that move shoppers toward checkout

The challenge

Users wanted answers in Telegram, not on a website or by email, and the team could not keep up with the messages by hand.

What Golonex did

• Built an AI chatbot on the Telegram Bot API
• Connected it to the client’s information so answers are specific, not scripted
• Handed conversations to a person, with context, when the bot was unsure
• Logged conversations and kept improving the answers after launch

The outcome

• Users answered in Telegram without waiting
• Repeat questions handled automatically
• A clear path to a person when it matters