Everything you need to know about how CyberCore works — from what we build to how engagements are structured, priced, and delivered across global markets.
CyberCore is a boutique AI consulting firm that helps businesses design, build, and deploy AI systems that work in production. CyberCore works with companies across the US, UK, Australia, Singapore, Germany, Netherlands, Canada, and the UAE to implement AI agents, RAG systems, workflow automation, and AI platform integrations.
Unlike generalist IT agencies, CyberCore operates with a limited client roster per quarter — trading breadth for depth. Every engagement is structured to produce working, production-ready AI, not slide decks or prototypes that never ship.
CyberCore provides six core AI services:
Every engagement begins with a discovery and scoping phase to ensure the right solution is designed before any build work starts.
CyberCore is a consulting firm, not an agency. The difference matters. Agencies operate at volume — many clients, faster delivery, templated playbooks. CyberCore operates at depth — fewer clients per quarter, longer engagements, architecture designed for each client's specific systems and data.
This means CyberCore is the right fit if you need a thinking partner who will get deep into your business before recommending an approach — not if you need a fast production run of a standard solution.
CyberCore works primarily with SMEs, growth-stage businesses, and technology-forward teams within larger organisations. Typical clients include:
CyberCore is not the right fit for businesses that need a quick demo, a low-cost offshore team, or a fully managed out-of-the-box product. The work is custom and collaborative by design.
CyberCore has delivered AI implementations across legal services, insurance, financial services, property and real estate, logistics, manufacturing, healthcare administration, and B2B SaaS. CyberCore does not restrict to a single sector — the common thread is businesses where AI can be applied to knowledge management, decision support, or high-volume operational workflows.
You can reach CyberCore via the contact page or by booking a discovery call directly at cybercore.in/book. The initial discovery call is a 30-minute conversation to understand your problem and assess whether CyberCore is the right fit. There is no sales pressure — if a different type of partner serves you better, CyberCore will say so.
AI consulting is the structured process of identifying where AI can create genuine business value, designing the right system architecture, and guiding implementation to production. At CyberCore, consulting engagements typically include: an AI readiness assessment, use case prioritisation, technology selection, build-vs-buy analysis, and a phased implementation roadmap.
Consulting is often the starting point for clients who are not yet sure what to build — it results in a clear scoped proposal for the implementation phase.
An AI agent is a system that can perceive inputs, reason about them, and take actions — across tools, APIs, and workflows — without step-by-step human instruction for each task. Unlike a chatbot that responds to a message, an agent can research a CRM, draft an email, check a calendar, and send a follow-up as a single autonomous workflow.
You need an agent (rather than simpler automation) when the work involves judgment — handling exceptions, selecting between paths, synthesising multiple data sources, or operating in contexts where the inputs vary significantly from case to case.
RAG (Retrieval-Augmented Generation) is a technique that connects a language model to your own documents, databases, or knowledge bases — so the model answers questions grounded in your actual content rather than relying on its general training data alone. The result is accurate, source-attributed, auditable AI responses.
CyberCore designs and builds RAG systems for knowledge management portals, internal document Q&A, customer-facing search, contract analysis, compliance checking, and similar knowledge-intensive applications. See our guide on RAG vs fine-tuning for help deciding which approach your use case needs.
CyberCore's workflow automation service covers end-to-end automation of business processes that span multiple tools, data sources, and human handoffs. This includes intake and triage automation, document processing pipelines, approval chain automation, multi-system data synchronisation, and hybrid human-AI workflows where AI handles the volume work and humans handle escalations.
CyberCore distinguishes intelligent automation (using AI judgment) from rule-based automation (using fixed logic). Most valuable implementations combine both.
Yes. CyberCore offers structured AI training for teams — designed to build genuine working proficiency, not surface-level awareness. Training programmes are tailored by role: what a finance team needs from AI differs substantially from what a customer operations team or a product team needs.
Training typically runs as 2–4 structured sessions with practical exercises grounded in the team's actual workflows. It can be delivered standalone or as part of a broader implementation engagement to ensure successful adoption.
A typical CyberCore engagement runs 6–10 weeks from discovery to production deployment, structured across six stages:
Billing follows a 40/30/30 milestone structure: 40% on project start, 30% at build completion, 30% on production handover.
Most CyberCore implementation engagements run between 4 and 10 weeks in the active build phase. Typical ranges by scope:
Timeline depends significantly on the complexity of integrations, quality and accessibility of the client's data, and availability of stakeholders for validation checkpoints. CyberCore provides a delivery estimate with each scoped proposal.
CyberCore does not publish standard pricing because every engagement is scoped individually. The cost of an AI implementation varies significantly based on system complexity, the number and type of integrations required, data readiness, and the level of ongoing support needed after deployment.
After an initial discovery call, CyberCore provides a detailed proposal with a transparent cost breakdown and delivery timeline. There are no retainer lock-ins or recurring fees unless the client opts into a post-delivery support arrangement.
Yes. CyberCore frequently structures initial engagements as a focused pilot: one workflow, one use case, one team — built and validated with real data before expanding. This approach reduces risk, produces demonstrable results quickly, and builds internal confidence before a larger commitment.
A well-scoped pilot is typically more valuable than a broad proof of concept that never reaches production, so CyberCore prefers to build something that genuinely works at smaller scope rather than demonstrate something that won't survive real-world use.
Yes. CyberCore signs NDAs before substantive project discussions and treats all client information — business context, data, architecture decisions, and proposed use cases — as strictly confidential. Data handling protocols are defined in the scoping agreement at the start of each engagement.
CyberCore selects AI models and technologies based on each project's requirements rather than defaulting to a fixed stack. Current working technologies include:
CyberCore is model-agnostic — the right model for a given use case is the one that performs best on the client's actual data, not the one with the best marketing.
Yes. CyberCore provides a structured handover period after production deployment — including documentation, team knowledge transfer, and a post-launch window for issue resolution. For ongoing support beyond the initial engagement, CyberCore offers monthly retainer arrangements covering monitoring, prompt tuning, model updates, and feature additions. This is optional and agreed separately from the main project contract.
Yes. CyberCore works with clients across the United States and schedules discovery calls and delivery checkpoints across EST and PST timezones. US clients typically engage CyberCore for AI agent deployments, RAG-powered knowledge systems, and workflow automation that integrates with tools like Salesforce, HubSpot, Zendesk, and enterprise data platforms. See CyberCore's US market page.
Yes. CyberCore works with UK businesses across financial services, professional services, and technology sectors. UK engagements are conducted in GMT/BST and follow UK data protection standards including UK GDPR. See CyberCore's UK market page.
Yes. CyberCore works with Australian businesses and schedules delivery around AEST. Australian clients have engaged CyberCore across financial services, logistics, and professional services for AI agent and workflow automation implementations. See CyberCore's Australia market page.
Yes. CyberCore works with Singapore-based businesses across fintech, logistics, and professional services sectors. Singapore engagements are designed to meet PDPA (Personal Data Protection Act) requirements and align with the Infocomm Media Development Authority's AI governance frameworks. See CyberCore's Singapore market page.
Yes. CyberCore works with businesses in Germany, the Netherlands, and across the wider EU. European engagements are architected with GDPR-by-design from the first scoping session — covering data minimisation, lawful basis documentation, data residency, and right-to-erasure compatibility. For German Mittelstand clients, CyberCore has delivered AI implementations across manufacturing intelligence, document automation, and customer operations. See CyberCore's Germany page and Netherlands page.
Yes. CyberCore designs AI systems for European clients with GDPR compliance built into the architecture from day one — not added as an afterthought. In practice, this means: selecting models and infrastructure with appropriate data residency options, implementing data minimisation at the design stage, documenting lawful bases for AI processing, and building systems compatible with subject access requests and the right to erasure.
For clients subject to the EU AI Act, CyberCore also assesses where proposed AI systems fall within the regulation's risk classification tiers and designs accordingly.
Yes. CyberCore works with Canadian businesses across EST and PST timezones. Canadian engagements are designed with PIPEDA compliance (federally) and Quebec Law 25 requirements (for Quebec-based clients) addressed in the scoping phase. See CyberCore's Canada market page.
Yes. CyberCore works with businesses in the UAE and broader Gulf region, scheduling around GST. UAE clients have engaged CyberCore for AI implementations in financial services, professional services, and operations-heavy industries. See CyberCore's UAE & Gulf market page.
Most businesses are more ready than they think — and some are less ready than AI vendors will tell them. Genuine readiness depends on four factors: whether you have a specific problem (not just "we want AI"), whether you have accessible data relevant to that problem, whether you have a team with capacity to engage meaningfully with implementation, and whether leadership has realistic expectations about timelines and what AI can and cannot do.
CyberCore's discovery call is specifically designed to assess these factors honestly. If the answer is "not yet", CyberCore will tell you what needs to be in place first — rather than starting a project that won't succeed.
RAG (Retrieval-Augmented Generation) retrieves relevant content from your documents at inference time and provides it to the model as context. Fine-tuning modifies the model's weights by training it on your data — changing what the model knows permanently.
RAG is better when your information changes frequently, when you need source attribution, or when you want to avoid the high cost and data requirements of training. Fine-tuning is better when you need the model to adopt a specific style, vocabulary, or specialised reasoning pattern that cannot be conveyed through prompts alone.
The most common mistake is reaching for fine-tuning when RAG would solve the problem at a fraction of the cost and with better auditability. Read the full guide on RAG vs fine-tuning.
A chatbot takes input and produces output — typically text in, text out. An AI agent additionally takes actions: it can call APIs, read and write to systems, search the web, execute code, and chain multiple steps together based on intermediate results. Chatbots are stateless responders; agents are goal-directed actors.
You need an agent when the task involves operating across multiple systems, making decisions at intermediate steps, or completing multi-stage work without a human issuing each individual instruction.
The right model depends on your use case, not benchmarks. OpenAI's GPT-4o performs well for broad general-purpose tasks and has the widest ecosystem support. Anthropic's Claude performs particularly well for long-context tasks, document analysis, and instruction-following in complex reasoning chains. Google's Gemini is strong for multimodal applications and integration within Google Workspace environments.
CyberCore is model-agnostic and evaluates each client's use case against the models' actual performance on representative test data before committing to a stack. In some cases, routing between models provides the best outcome. Read the full comparison.
Data security is addressed at the architecture stage — before any code is written. CyberCore evaluates which data is required for the system to function, how it needs to be stored and accessed, which third-party services process it (and under what terms), and what access controls are appropriate.
For sensitive industries or regulated markets, CyberCore can design systems that keep data fully on-premise using open-source models, avoid sending data to third-party model providers entirely, or use enterprise API agreements with relevant data processing terms (such as OpenAI Enterprise or Anthropic's enterprise tier, which exclude customer data from training).
Five things to evaluate when choosing an AI consulting firm:
The red flags are: a firm that promises everything, never asks about your data, and can't explain the difference between RAG and fine-tuning. Read the full guide on choosing an AI consulting firm.
Book a 30-minute discovery call. No sales process — just an honest conversation about whether we're the right fit.