Generative AI Development Company

Step 1

Discovery & GenAI Readiness Assessment (Weeks 1–2)

Highest-value use cases identified, data landscape evaluated, infrastructure maturity assessed, and governance requirements mapped — before a single architecture decision is made.

Deliverable: GenAI Readiness Report + Prioritised Use Case Roadmap

Step 2

Solution Architecture & Data Design (Weeks 2–4)

Model selected, RAG pipeline structured, integration points defined, and compliance controls established. Data assessed, cleaned, and prepared for ingestion in parallel.

Deliverable: Solution Architecture Blueprint + Data Readiness Assessment

Step 3

Build & Iterate (Weeks 4–9)

Development in structured sprints — prompt engineering, model configuration, RAG pipeline build, fine-tuning, and integration completed iteratively with live demonstrations and real-time feedback at every milestone.

Deliverable: Working Solution Builds + Sprint Demonstration Records

Step 4

Evaluation & Quality Assurance (Weeks 8–10)

Output accuracy, safety, relevance, and compliance benchmarked. Adversarial testing, hallucination rate measurement, and user acceptance testing completed. Formal sign-off required before deployment proceeds.

Deliverable: Evaluation Report + Compliance Verification + Stakeholder Sign-Off

Step 5

Deployment & Enterprise Integration (Weeks 10–12)

Solution deployed into production, all enterprise integrations verified, security controls activated, and operational teams equipped to work alongside the system from day one.

Deliverable: Live Deployed Solution + Integration Confirmation + Operational Handover

Step 6

Monitoring, Optimisation & Continuous Improvement

Output quality, system performance, and model drift monitored continuously. Retraining cycles and proactive optimisation aligned to data changes and evolving business requirements.

Deliverable: Monthly Performance Reports + Model Drift Analysis + Optimisation Roadmap