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    AI IMPLEMENTATION

    YOUR FIRST 90 DAYS
    OF REAL AI PROGRESS

    Stop asking "how do I implement AI?" and start shipping. We take you from idea to production in 90 days with a proven methodology: build fast, learn fast, and own everything you build.

    Working prototype in 2-4 weeks
    No pilot purgatory
    Your team owns it

    THE FIRST 90 DAYS

    Our proven methodology takes you from kickoff to production in 12 weeks. No multi-year roadmaps. No endless pilots. Just working AI that delivers value.

    Weeks 1-2

    Discovery & Scoping

    Define scope, success metrics, and technical approach

    Stakeholder interviews and requirements gathering
    Technical environment assessment
    Data availability and quality audit
    Define success criteria and KPIs
    Risk identification and mitigation planning
    Create detailed project plan and timeline
    Deliverable: Project charter with scope, timeline, and success metrics
    Weeks 3-6

    Rapid Prototyping

    Build working proof-of-concept to validate the approach

    Set up development environment
    Build initial prototype with core functionality
    Integrate with sample data and systems
    User testing with key stakeholders
    Iterate based on feedback
    Validate technical feasibility and performance
    Deliverable: Working prototype demonstrating core capabilities
    Weeks 7-10

    Production Deployment

    Ship to production with monitoring and safeguards

    Production infrastructure setup
    Security hardening and compliance checks
    Integration with production systems
    Performance optimisation and load testing
    Monitoring, alerting, and logging setup
    Staged rollout with feedback loops
    Deliverable: Production-ready system with monitoring and documentation
    Weeks 11-12

    Handover & Training

    Transfer knowledge so your team owns the solution

    Complete technical documentation
    Team training sessions (developers, operators, users)
    Runbook and troubleshooting guides
    30-day support period
    Performance baseline and KPI tracking
    Roadmap for future enhancements
    Deliverable: Fully documented system with trained team

    Escaping Pilot Purgatory

    Most AI POCs never make it to production. They get stuck in an endless loop of "almost ready" - expanding scope, unclear metrics, and no path to deployment. We've seen it dozens of times. Here's how we avoid it.

    Scope creep

    POC keeps expanding without clear boundaries

    Fixed 2-4 week sprints with defined deliverables

    No production path

    Prototype built without considering deployment

    Production architecture from day one

    Wrong success metrics

    Measuring technical accuracy instead of business impact

    Business KPIs defined before development starts

    Dependency on consultant

    Only the consultant knows how it works

    Documentation and training built into every phase

    HOW WE WORK

    Three principles guide every implementation: speed, production-readiness, and knowledge transfer.

    Rapid Prototyping

    Working prototype in 2-4 weeks, not months. We validate approaches quickly so you can make informed decisions before scaling investment.

    • Focus on core functionality first
    • Use real data from day one
    • Iterate based on actual user feedback
    • Kill ideas that don't work early

    Production-First Mindset

    Every prototype is built with production in mind. No throwaway code - everything we build can scale.

    • Production architecture from the start
    • Security and compliance built in
    • Monitoring and observability included
    • MLOps pipeline for continuous deployment

    Knowledge Transfer

    Your team owns the solution. We document everything, train your staff, and ensure you're self-sufficient.

    • Pair programming with your developers
    • Comprehensive documentation
    • Training sessions for all user types
    • 30-day support after handover

    TECHNOLOGY STACK & TOOLS

    We're technology-agnostic and recommend the best tools for your specific needs. No vendor lock-in - everything we build is portable and open.

    Large Language Models

    OpenAI GPT-4Anthropic ClaudeOpen-source models (Llama, Mistral)Azure OpenAI Service

    Data & Infrastructure

    Vector databases (Pinecone, Weaviate, Qdrant)PostgreSQL with pgvectorRedis for cachingApache Kafka for streaming

    Cloud Platforms

    AWS (SageMaker, Bedrock)Azure (ML, Cognitive Services)Google Cloud (Vertex AI)On-premise deployment when required

    Development & MLOps

    LangChain / LlamaIndexWeights & BiasesMLflowDocker & Kubernetes

    We work with your existing tech stack and can integrate with any cloud or on-premise infrastructure.

    WHAT YOU OWN AT THE END

    No ongoing licensing fees. No proprietary frameworks. No dependency on us. You own everything outright.

    Complete Source Code

    All code is yours. No licensing fees, no ongoing royalties. You own it outright.

    Full Documentation

    Architecture diagrams, API documentation, runbooks, and troubleshooting guides.

    Trained Team

    Your developers and operators trained to maintain and evolve the solution.

    No Vendor Lock-In

    Built on open standards with clear migration paths. You're never trapped.

    At the end of every engagement, you receive complete source code, full documentation, a trained team, and 30 days of support. We build to hand over, not to lock in.

    FREQUENTLY ASKED QUESTIONS

    Everything you need to know about AI implementation.

    How long does AI implementation take?

    A typical first AI project takes 90 days from kickoff to production deployment. This includes 2 weeks of discovery, 4 weeks of prototyping, 4 weeks of production deployment, and 2 weeks of handover and training. More complex projects may take longer, but we always start with a focused scope to deliver value quickly.

    What do we own at the end?

    You own everything: all source code, documentation, trained models, and intellectual property. There are no ongoing licensing fees or royalties. The solution is yours to maintain, modify, and extend as you see fit. We build on open standards specifically to avoid vendor lock-in.

    Do you write production code?

    Yes. We don't just advise - we build. Our team writes production-quality code with proper testing, documentation, and MLOps pipelines. Everything we deliver is ready for production use, not just a proof-of-concept that needs to be rebuilt.

    What about ongoing support after the project?

    Every project includes 30 days of post-launch support. After that, you have several options: your trained team can maintain the system independently, we can provide ongoing retainer support, or we can engage for future enhancements on a project basis. We design solutions to minimise ongoing dependency on us.

    What if the prototype doesn't work?

    That's exactly why we prototype first. If the approach doesn't validate during the prototype phase, we pivot or stop before significant investment. You'll have clear evidence of what works and what doesn't within 4-6 weeks, allowing you to make informed decisions about whether to proceed to production.

    Do we need to have AI expertise in-house?

    No. We work with teams of all technical levels. Part of our engagement is building your team's AI capabilities through knowledge transfer. By the end of the project, your team will be equipped to maintain and evolve the solution. For ongoing development, we can also help you hire AI talent.

    How do you handle data privacy and security?

    Security and compliance are built into every phase. We conduct data audits during discovery, implement security controls during development, and ensure compliance with relevant regulations (GDPR, HIPAA, SOC 2, etc.) before production deployment. All solutions can run in your own infrastructure if required.

    What's the difference between this and your AI Strategy service?

    AI Strategy (Stage 2) focuses on identifying where AI fits in your business and building a roadmap. AI Implementation (Stage 3) is about executing on that strategy - actually building and deploying the AI systems. Many clients start with strategy, but if you already have a clear use case and technical requirements, you can jump straight to implementation.

    READY TO START YOUR FIRST AI PROJECT?

    Stop planning and start building. Book a call to discuss your use case and get a clear path to your first working AI system.