Questions we hear
from enterprise clients

Both. We build greenfield systems when a client needs something built from scratch with a clean architecture — and we modernize, scale, or extend existing systems when that's the right approach. We assess the current state first and give honest recommendations on when a rebuild makes sense versus when incremental improvement is a better investment.

We are cloud-agnostic and have certified architects across AWS, Azure, and Google Cloud. We also have deep experience with multi-cloud and hybrid-cloud architectures for clients with compliance requirements that dictate data residency or redundancy across providers. We'll recommend the best fit for your workload — not the one we have a commercial incentive to push.

Yes. We work across the full engagement lifecycle — from technology strategy and architecture planning through hands-on engineering delivery and post-launch operations. Many clients come to us initially for advisory work, and expand into a full delivery engagement once they see how we think and work. Others come to us for delivery from day one. We adapt to what you need.

Our AI center of excellence works across the full stack of enterprise AI:

• RAG systems and LLM-powered applications
• Agentic workflows and tool-calling systems
• ML model development and MLOps
• Data pipeline engineering for AI workloads
• AI strategy and implementation roadmaps
• Fine-tuning and model evaluation

We focus exclusively on production deployments — not proofs of concept that never make it to production.

Every engagement begins with a discovery phase — regardless of how well-defined the brief is. We use structured workshops and technical assessments to understand your actual constraints, not just the stated requirements. This phase typically takes 2–4 weeks depending on complexity, and it produces an architecture plan and delivery roadmap before any engineering work begins. Most clients tell us this phase alone changes how they think about the problem.

We define clear milestones with explicit acceptance criteria at the start of each engagement. Scope changes are handled through a documented change management process — not ignored or absorbed silently until the project slips. We run two-week agile sprints with end-of-sprint demos, so clients can see working software at every stage rather than finding out at the end that something was built wrong.

Every engagement includes 60 days of post-launch engineering support at no additional cost. This covers bug fixes, performance optimization, and minor adjustments as the system sees real-world load. After that period, clients can move to a retainer arrangement for ongoing maintenance, or we can fully transition the system to their internal team with comprehensive documentation and a knowledge transfer program.

Yes, completely. All code, architecture documentation, and IP created during the engagement transfers to the client at delivery. We don't use restrictive licensing, proprietary frameworks, or any mechanism that would create future dependency on KKRF Tech to operate or modify what we built. You own what you paid for.

KKRF Tech is ISO 27001 certified and operates under a SOC 2 Type II audited information security framework. Our security practice team includes CISSP, CISM, and CISA certified professionals. We can provide current certification documentation to enterprise procurement teams on request under NDA.

Regulatory compliance is embedded in our architecture and development process — not bolted on at the end. For each engagement in a regulated industry, we:

• Map applicable regulations and data residency requirements at the discovery stage
• Design data flows with compliance boundaries built in from the start
• Apply appropriate encryption, access control, and audit logging standards
• Document all compliance-relevant design decisions for audit purposes

We have deep experience with GDPR (EU), HIPAA (US), PCI DSS, DIFC (UAE), and NHS Digital standards (UK).

Yes. We'll sign a mutual NDA before any detailed technical or business discussions if requested. This is standard for enterprise engagements and we have no hesitation about it. Just mention it in your initial contact and we'll have the paperwork to you within 24 hours.

Client codebases are stored in isolated, access-controlled environments. We use client-provisioned repositories whenever possible, apply least-privilege access policies, maintain audit logs of all access events, and require MFA for all team members working with client systems. We can provide a detailed security posture document for enterprise security reviews.

We offer three primary commercial models depending on the engagement type:

• Fixed-price: For well-scoped projects with clear deliverables
• Time & materials: For engagements with evolving scope or ongoing development work
• Dedicated team retainer: For clients who need a stable extended team billed monthly

We don't publish standard rate cards because team composition varies by engagement. We provide detailed, itemized proposals after the initial discovery conversation.

Our minimum project engagement is typically in the range of $150,000 USD, and our dedicated team engagements start at a minimum of three specialists for a minimum six-month term. This reflects the cost of the discovery phase and the team setup overhead we invest in every engagement. For smaller scopes, we recommend our advisory model, which can be structured as a shorter-term consulting arrangement.

Yes. For fixed-price engagements, we typically structure payments around four or five milestones tied to specific deliverables — not arbitrary calendar dates. This means payment is always tied to something tangible being delivered, and both parties have clear visibility into what triggers each payment. We're open to discussing alternative structures for large enterprise contracts.

This is exactly what our AI strategy engagements are designed for. Many organizations come to us with a vague directive to "do something with AI" and need a neutral assessment of where AI can create genuine value versus where it would be a solution in search of a problem. We run structured discovery workshops that map your data assets, existing processes, and business objectives — and produce a prioritized roadmap of AI initiatives ranked by feasibility and expected ROI. No commitment to a development engagement required.

Reliability engineering for AI systems is a distinct discipline from model development. Our approach includes:

• Evaluation frameworks that measure hallucination rates, factual accuracy, and output consistency before deployment
• Guardrails and output validation layers that catch problematic outputs before they reach users
• Human-in-the-loop design for high-stakes decisions that AI should not make autonomously
• Continuous monitoring of production model performance with drift detection
• Red-teaming and adversarial testing as part of the QA process

We work with client proprietary data in controlled environments with appropriate data handling agreements in place. This includes building RAG systems that query your internal knowledge bases, fine-tuning models on your proprietary datasets, and engineering pipelines that process and structure your data for AI workloads — all within your security and data governance requirements. Client data is never used to train models for other clients.