01 · Strategy
When direction is unclear
Assess readiness, prioritize use cases, and define governance before delivery starts.
Services
One production path.
Choose the right level of support: define the AI roadmap, build production systems, or operate and improve deployed AI.
02 · Build
When you are ready to implement
Deliver pilots, integrations, data platforms, and production AI engineering.
03 · Operate
When AI is already live
Monitor models, retrain systems, maintain compliance, and provide support.
Service lines
Simple structure. Clear role for each service.
01 · AI Strategy & Roadmap
Plan before you build.
Engagement
2-6 weeks
Offerings
Four
Focus
Executive clarity
Advisory engagements that define what to build, in what order, and with what governance before a single line of code gets written.
01.01
2-4 weeksAI Readiness Assessment
Diagnostic across data, infrastructure, talent, and governance to identify high-value AI opportunities and blockers.
Clear view of readiness, gaps, and practical starting points.
01.02
4-6 weeksAI Investment Roadmap
Prioritized 18-36 month roadmap with sequenced initiatives, business cases, dependencies, and ROI logic.
Board-ready roadmap showing what to fund first and why.
01.03
3-5 weeksAI Governance & Ethics
Risk frameworks, model approval workflows, compliance readiness, and ethical guardrails calibrated to the sector.
Safer AI adoption with clear controls and accountability.
01.04
4-6 weeksAI Center of Excellence Design
Operating model, team structure, tooling, and capability ladder to scale internal AI adoption.
A practical model for building AI capability inside the organization.
02 · AI Professional Services
Build, integrate, deploy.
Engagement
8 weeks-10 months
Offerings
Four
Focus
Implementation
Delivery services for pilots, integrations, AI-ready data platforms, and MLOps foundations that move projects into production.
02.01
8-12 weeksPoC & Pilot Delivery
Structured program from problem definition to a validated working pilot running on real data.
Proof of value before committing to a wider implementation.
02.02
3-6 monthsAI Systems Integration
Full-scale AI deployment integrated with enterprise systems, workflows, and existing platforms.
AI that works inside real business operations, not separately from them.
02.03
3-6 monthsData Platform Engineering
Data lakes, warehouses, and AI-ready pipelines that create the foundation every AI system needs.
Reliable data infrastructure prepared for analytics and AI workloads.
02.04
2-4 monthsMLOps & AI Engineering
Model versioning, automated retraining pipelines, drift monitoring, and deployment workflows.
Engineering discipline around AI models running in production.
03 · AI Managed Services
Keep it running. Keep it improving.
Engagement
12-36 months
Offerings
Four
Focus
Operations
Post-deployment services that keep AI systems monitored, maintained, compliant, and continuously improved.
03.01
12-36 monthsAI Operations & Monitoring
Model health monitoring, drift detection, performance alerts, and incident response for live systems.
Visibility into model performance and issues after deployment.
03.02
12-24 monthsContinuous Retraining
Scheduled retraining cycles, evaluation, controlled rollouts, and rollback safety nets.
AI systems that adapt as data and business conditions change.
03.03
12-36 monthsAI Governance & Compliance
Ongoing audits, documentation, audit trails, and compliance reporting maintained over time.
Governance records that support accountability and oversight.
03.04
12-36 monthsDedicated AI Support Pod
Flexible support team of AI, data, and platform specialists working with the internal team.
Extra delivery and support capacity without building the full team internally.
Not sure which service fits?
Take the 60-second assessment to get a recommended starting point, or book a call to discuss the right sequence.