Frequently Asked Questions

LayersIQ is a full-service AI development agency. We design, build, and ship production-ready AI products — from MVP prototypes to enterprise-scale platforms. Our work spans AI agent systems, RAG pipelines, custom LLM integrations, and full-stack web and mobile applications powered by modern AI stacks.

LayersIQ operates as a remote-first team with talent distributed globally. Our core engineering leadership is based in the UK, with specialists across North America, Europe, and Southeast Asia. We work across all time zones and have delivered projects for clients in the US, EU, Australia, and the Middle East.

The fastest path is to book a free discovery call or submit our Get a Quote form — both take under 5 minutes. We'll schedule a 30-minute scoping session, define your goals, and send you a detailed proposal within 48 hours. No long sales cycles, no hidden process — just straight to the work.

We work across a wide range of industries including financial services, healthcare and life sciences, legal tech, e-commerce and retail, and SaaS platforms. Our AI solutions are adapted to the compliance, data, and UX requirements specific to each vertical.

The MVP Sprint is a fixed-price $15K engagement that delivers a production-ready MVP in 4 weeks. It includes one core AI feature (agent or RAG), a Next.js frontend and API backend, one round of UX revisions, deployment to Vercel or AWS, and 30 days of post-launch support. It's designed to give you something real to pitch investors or test with users — fast.

Yes. For the MVP Sprint and AI Product Build tiers, we offer milestone-based payment schedules — typically 40% on project kickoff, 40% at the midpoint delivery, and 20% on final handover. Enterprise engagements are structured according to scope and duration. Contact us to discuss what works for your budget.

We handle scope changes transparently. Fixed-price tiers (MVP Sprint and AI Product Build) include a defined scope in the contract. If new requirements emerge, we produce a change order with cost and timeline impact for your approval before any work begins. We never surprise you with overruns.

Yes. We offer monthly retainer packages for post-launch maintenance, feature iteration, model fine-tuning, and infrastructure monitoring. Retainer clients receive a dedicated Slack channel, priority response SLAs, and regular performance reviews. Reach out via the contact form to discuss retainer options.

Our default stack is Next.js (App Router) for the frontend, Node.js or Python for the backend, and PostgreSQL or Supabase for data. For AI, we work with OpenAI, Anthropic, Google Gemini, Mistral, and open-source models via HuggingFace. Infrastructure is deployed on Vercel, AWS, or GCP depending on scale. We also have deep experience with LangChain, LlamaIndex, Pinecone, and Weaviate for RAG pipelines.

Absolutely. A significant portion of our engagements involve extending or modernising existing systems — adding AI capabilities to a legacy codebase, migrating from a monolith to a microservices architecture, or integrating a new AI agent layer into an established product. We always start with a thorough codebase audit before proposing a technical approach.

Security is built into every engagement. We follow OWASP best practices, enforce least-privilege IAM roles, encrypt data at rest and in transit, and never store sensitive user data beyond what's required. For regulated industries (healthcare, fintech, legal), we offer SOC 2-compatible infrastructure configurations and can work within HIPAA or GDPR compliance frameworks upon request.

Yes. We offer supervised fine-tuning on OpenAI models and open-source LLMs (Llama, Mistral) using your proprietary data. We also design RAG pipelines that give models access to your knowledge base without the cost of full fine-tuning. The right approach depends on your data volume, latency requirements, and budget — we'll recommend the optimal path during scoping.

Timelines vary by scope. The MVP Sprint is a fixed 4-week delivery. The AI Product Build typically runs 8–12 weeks. Enterprise engagements are scoped individually but usually range from 3–9 months. We provide a detailed timeline in the project proposal, broken down by milestone, so you always know what's being built and when.

We follow a structured three-phase process: (1) Discovery & Architecture — 1-week sprint to define requirements, design system architecture, and align on success metrics; (2) Build — iterative two-week cycles with demos at the end of each sprint; (3) Launch & Handover — deployment, documentation, knowledge transfer, and post-launch monitoring. You have visibility at every stage via a shared project board and regular check-ins.

We assign a dedicated project lead as your single point of contact. Communication happens via a shared Slack channel (AI Product Build and Enterprise tiers) or async email updates (MVP Sprint). We hold a weekly 30-minute sync call to review progress, discuss blockers, and align on the next sprint. All decisions and change requests are documented in writing for full traceability.

Yes. Every project includes technical documentation covering architecture decisions, API references, environment setup, and deployment runbooks. For AI-specific components, we document prompt engineering decisions, model selection rationale, and evaluation benchmarks. A handover session with your internal team is included in all tiers to ensure a smooth transition to self-managed operations.