AI Strategy & Roadmapping
Align opportunities to KPIs and create a prioritized roadmap with clear success criteria and risk assessment.
Services
End-to-end AI services that move ideas into trusted production
NOUT offers a complete service stack to help organizations adopt AI in a pragmatic, risk-aware way. We begin with discovery workshops to identify high-impact opportunities and establish measurable success criteria. Our data engineering practice prepares reliable pipelines and feature stores so models have consistent inputs. Product-focused modeling translates use cases into interpretable solutions while our MLOps capabilities ensure safe deployments and continuous monitoring. We pair engineering with governance: privacy-by-design, bias testing, and access controls are embedded in each phase. Our goal is to create production systems that deliver predictable business value, reduce manual work, and improve decision-making without creating long-term maintenance burdens. For teams that need an internal capability, we include training and documented playbooks so your organization retains ownership after handoff.
What we deliver
Our services are structured to deliver measurable outcomes and operational readiness. Strategy engagements clarify where AI can create value and set a realistic roadmap with prioritized use cases and success metrics. Data and engineering services build robust ingestion pipelines, data quality checks, and feature stores so models run on dependable inputs. Modeling and productization efforts focus on interpretable, maintainable solutions that align to user workflows and system constraints. MLOps brings the models to life with CI/CD, automated testing, monitoring, alerting, and rollback procedures that reduce downtime and operational risk. We add governance and compliance workstreams that include privacy assessments, bias audits, and documentation of data lineage. Every engagement can include knowledge transfer, runbooks, and hands-on training so your team can run the operation independently. We emphasize small pilots that validate impact quickly and include clear plans for scaling successful pilots into production-grade services.
Data Engineering
Scalable pipelines, testing, and feature management that enable reproducible, reliable model inputs.
Applied Modeling
Practical, interpretable models integrated into user workflows with performance targets and validation plans.
MLOps & Monitoring
Continuous integration, automated deployment, drift detection, and observability for production reliability.
Governance & Security
Privacy assessments, bias audits, access controls, and model documentation to satisfy regulators and stakeholders.
Training & Handoff
Workshops, runbooks, and in-context coaching so internal teams can maintain, iterate, and scale solutions.
Engagement model and timeline
We structure engagements to reduce upfront risk and demonstrate value early. Typical work begins with a two-week discovery that aligns stakeholders, inventories data, and proposes one or two pilot use cases with measurable baselines. A focused pilot runs for six to twelve weeks depending on complexity and includes model development, integration, and an evaluation against agreed KPIs. If the pilot meets success criteria, we transition to a production phase that implements MLOps, monitoring, and operational playbooks for a reliable rollout. Pricing is flexible: fixed-price discovery and pilot phases, followed by time-and-materials or outcome-based contracts for scaling. Every engagement includes a documented plan for governance, security, and operational ownership so your team can continue to run the solution independently after handoff.
Typical timeline
- Discovery - 2 weeks: alignment, data review, use case selection.
- Pilot - 6 to 12 weeks: prototype, evaluate, iterate.
- Production - ongoing: MLOps, monitoring, scale-up.