AI Solutions Development

Q3 AI engagement spans six defined phases: Discovery and Problem Framing (2–4 weeks), Data Strategy and Engineering (3–6 weeks), Model Development and Training (4–10 weeks), System Integration and API Engineering (3–6 weeks), Deployment and QA (2–4 weeks), and Continuous Monitoring and Optimisation (ongoing). Each phase has agreed deliverables and stakeholder sign-off checkpoints. You are not buying a model — you are acquiring a production-grade intelligent system with a defined support commitment.

Investment varies by scope, data complexity, integration requirements, and regulatory constraints. Focused AI modules — a churn prediction engine, a document intelligence API, or a fraud detection classifier — start from $25,000–$50,000. Comprehensive AI platforms involving custom LLM fine-tuning, multi-model architectures, and enterprise MLOps infrastructure typically range from $150,000–$500,000+. We provide a fixed-scope proposal with itemised costs after a free discovery session — no time-and-materials billing.

A focused AI feature with clean, accessible data can reach production in 8–12 weeks. A comprehensive platform involving custom data infrastructure, multiple model components, and full enterprise integration typically takes 4–9 months. Our phased delivery model ensures you see working software — and measurable early value — within the first 4–6 weeks of engagement, regardless of overall project scope.

AI excels at four categories of problems: (1) pattern recognition at scale — detecting fraud, defects, or anomalies in large data streams faster than any human team; (2) predictive intelligence — forecasting demand, churn, or equipment failure before it occurs; (3) natural language understanding — extracting insights from documents, emails, contracts, and customer conversations; and (4) autonomous decision-making — routing, triaging, and prioritising tasks without human intervention. If your problem involves data, repetition, or prediction, AI almost certainly has a role to play.

Our engineers work across the full modern AI stack. Core ML frameworks: PyTorch, TensorFlow, JAX, and Scikit-learn. LLM and agent frameworks: LangChain, LangGraph, AutoGen, CrewAI, and OpenAI Function Calling. Data infrastructure: Apache Spark, dbt, Feast, and Delta Lake. MLOps: MLflow, Weights and Biases, Kubeflow, and Evidently AI for drift detection. Deployment: Kubernetes on AWS (SageMaker), GCP (Vertex AI), and Azure (ML Studio) — and on-premise for regulated industries.

We define and agree success metrics with your team before Phase 01 begins — not after deployment. Metrics typically include: operational cost savings from automation (measured in FTE hours or $ reduction), revenue uplift from AI-driven personalisation or pricing (tracked via A/B testing), efficiency gains (process cycle time reduction), risk reduction (incident rates, fraud losses avoided), and customer experience improvements (NPS, CSAT, resolution rate). Our clients average 3–7x ROI within 18 months, with a clear attribution model presented in quarterly performance reviews.

Yes, we sign mutual NDAs before any discovery discussions. On intellectual property: the default position in our contracts is that all custom-built models, training pipelines, and associated code developed specifically for your engagement are fully owned by you on completion of the project. Q3 Technologies retains rights to our pre-built accelerator libraries and proprietary tooling that form part of the development environment. IP ownership terms are always stated explicitly in the proposal — we encourage you to review them with your legal team.

Our SLA-backed Continuous Learning and Optimisation agreement (Phase 06) defines minimum performance thresholds for every production model. If a model’s accuracy, latency, or reliability falls below the agreed threshold, our MLOps team is contractually obligated to identify the root cause — whether data drift, distribution shift, or infrastructure degradation — and implement a remediation plan within a defined SLA window. You receive a written incident report and a prevention plan for every breach.

Yes — this is one of our core capabilities. We build AI integration layers that connect modern AI services to legacy ERP platforms (SAP, Oracle, Dynamics 365), mainframe databases, and ageing CRM systems via API gateways, event-driven microservices, and ETL pipelines. Your operational data stays in your existing systems of record. Business users interact via AI-powered interfaces layered on top. We have successfully integrated AI into systems as old as COBOL-based mainframes without requiring migration.

Compliance is architected from Phase 01, not retrofitted at the end. For GDPR: we implement data minimisation, purpose limitation, and right-to-erasure mechanisms from the data pipeline design stage. For HIPAA: all PHI data is encrypted at rest and in transit, access is role-based, and full audit logs are maintained. For the EU AI Act: we assess each system’s risk classification (Limited, High, or Unacceptable) and implement the required transparency, human oversight, and documentation obligations before deployment. Our AI Governance practice can assist with regulatory filings on request.

AI consulting (our Phase 01) involves analysis, strategy, and recommendations — delivered as reports, roadmaps, and business cases. AI development is the engineering work: building, training, integrating, and deploying the actual intelligent system. Most of our clients need both: consulting to identify the right problem and validate the business case, followed by development to build the solution. We offer both as a continuum under a single engagement, which avoids the common problem of a consulting firm recommending a solution and then disappearing when it’s time to build it.

Evaluate AI vendors on five criteria: (1) Verifiable case studies — can they show you a production system with named outcomes, not just demos? (2) Named team expertise — do they publish the credentials of the engineers who will actually work on your project? (3) Compliance depth — can they demonstrate regulatory knowledge specific to your industry, not just general ISO certifications? (4) Post-launch accountability — do they have an SLA-backed MLOps commitment, or do they hand over and disappear? (5) Pricing transparency — are costs fixed-scope and itemised, or ambiguous time-and-materials? Q3 Technologies meets all five criteria — we encourage you to hold every vendor, including us, to this standard.