Twelve services, three practices.

AI product engineering

Software with models inside it, built to production standard and owned by your team.

• A prototype that never reached production
• An internal tool that needs to reason, not just store data

• Production application with tests and monitoring
• Architecture decision records
• Runbook

Agentic workflow automation

Agents that carry routine work across systems and hand the hard cases to a person.

• Work that spans several systems and teams
• Skilled people spending their day on routine exceptions

• Agents with clear limits and audit trails
• Approval steps where they matter
• Escalation rules

Knowledge assistants

Assistants that answer from your own documents and data, with sources shown.

• Answers buried in procedures, contracts and tickets
• New staff who take months to get up to speed

• Assistant with cited answers
• Access rules matched to your permissions
• Quality evaluation set

Systems integration

Connect ERP, WMS, TMS, MES and planning tools so models see one picture.

• The same order looks different in every system
• Decisions made on yesterday’s extract

• Integration layer with tests
• Shared data definitions
• Monitoring for broken feeds

Forecasting and prediction

Demand, capacity and risk forecasts your planners can trust and question.

• Plans built on averages
• Forecast error nobody tracks

• Forecast models in production
• Accuracy tracked against a baseline
• Retraining schedule

Optimization and scheduling

Plans and schedules that respect real constraints and show their trade-offs.

• Schedules rebuilt by hand when inputs change
• Rules of thumb that leave capacity unused

• Optimization model with constraints written down
• Planner review screen
• Scenario runs

Analytics and decision dashboards

Views built around a decision, not a data source. What changed, why, and what to do.

• Dashboards nobody opens
• Reports that describe but do not recommend

• Decision-led dashboards
• Metric definitions
• Alerts on what moved

Data engineering and pipelines

Reliable pipelines from source systems to models, with quality checks built in.

• Data prepared by hand every week
• Models that break when a source changes

• Tested pipelines in your cloud
• Data quality checks
• Lineage and documentation

Evaluation and reliability

Independent tests that show whether an AI system is good enough, and stays that way.

• No agreed definition of good
• Quality that drifts after launch

• Evaluation suite and scorecard
• Regression tests on every change
• Release recommendation

AI strategy and operating model

Decide where AI changes outcomes, who owns it, and how work changes around it.

• Many pilots, no pattern
• Unclear ownership once a model is live

• Decision inventory ranked by value
• Operating model and roles
• Sequenced roadmap

Cloud, MLOps and handover

Infrastructure, deployment and monitoring in your own accounts, handed over in full.

• Models running on one person’s laptop
• No way to know when a model degrades

• Infrastructure as code
• Model registry and deployment pipeline
• Access returned at handover

Team enablement

Training and pairing so your people can run, question and extend what we built.

• A team that depends on outside help
• Leaders unsure what to ask of AI

• Working sessions on your own systems
• Playbooks
• Office hours during support