Four capabilities.
One integrated strategy.
Every engagement maps directly to business KPIs. We don't sell technology — we deliver outcomes that show up in your P&L.
Generative AI & Automation#
Make AI do the work
We design and deploy production-ready AI systems that eliminate manual effort and unlock capabilities that weren't possible before. From agentic knowledge assistants and document understanding to automated process orchestration — we build AI that integrates into your existing workflows and actually ships. This goes far beyond basic workflow automation. We use LLMs, computer vision, and decision logic to handle complex, judgment-intensive work at scale.
Use Cases
- Agentic knowledge base and internal search assistants
- Document classification, extraction, and contract processing
- Intelligent routing for customer service and support triage
- Internal copilots for sales, operations, and compliance teams
- A central AI Operating System that connects every tool and team to run the business seamlessly
Teams typically reclaim 30–50% of time spent on manual process work within 90 days. Knowledge workers report 2–4 hours per day reclaimed with well-designed AI copilots.
Fractional Chief AI Officer#
Executive AI leadership and org-wide enablement — without the full-time hire
Most companies are not ready for a full-time Chief AI Officer — but every company needs AI leadership to avoid wasting millions on initiatives that don't deliver. We embed a senior AI executive into your leadership team on a monthly retainer, covering two things most consultants ignore: top-down strategy (roadmap, governance, board communication, vendor accountability) and bottom-up enablement — training every employee, not just the C-suite, to work more effectively with AI in their daily role.
Companies with dedicated AI leadership are 2.5x more likely to achieve measurable ROI from their AI investments within 18 months. Org-wide AI training programs consistently improve team productivity by 20–35% within the first quarter.
Engagement Tiers
"We start with clarity before we touch any tools."
Best for: Organizations new to AI. The goal is a documented strategy and one working proof-of-concept — not a transformation.
- AI Readiness Audit (what's manual, costly, and ready for AI)
- 90-Day AI Roadmap with 3 prioritized opportunities
- One implemented proof-of-concept (low-risk, high-visibility)
- Executive briefing deck for board and leadership
- Monthly progress report and KPI baseline
- Staff AI literacy workshop (half-day)
"Your AI roadmap is done. Now we build the engine that runs it."
Best for: Companies with a clear pain point and appetite to move — ready to deploy real AI workflows.
- Everything in Tier 1, plus:
- 2–3 AI workflows designed, procured, and deployed
- Vendor selection and contract oversight
- AI governance policy (data handling, ethics, human-in-the-loop)
- Change management support for staff adoption
- Quarterly board and exec AI update
- ROI tracking dashboard (cost savings, time recovered, revenue impact)
- Integration with existing systems (CRM, ERP, etc.)
"AI is now a strategic asset and we govern it like one."
Best for: Organizations with executive buy-in and multiple ongoing initiatives that need governance and measurement across departments.
- Everything in Tier 2, plus:
- Full AI strategy aligned to 3-year business plan
- Multi-department AI deployment (3+ workflow areas)
- AI Operating System setup — a central AI brain connecting every tool and team across the business
- Data governance and AI ethics framework
- Board-level AI briefings and investor narratives
- Team hiring support: AI literacy rubric and AI roles defined
- SR&ED / IRAP / government grant identification (Canadian companies)
- Annual competitive AI benchmarking
Agentic Analytics & Predictive Intelligence#
Understand the past. Anticipate the future.
From reliable KPI dashboards to predictive ML models to agentic pipelines that answer your business questions automatically — we build analytics that your team actually uses and that show up in your operations. Ask a question, get a root cause and a recommendation. In minutes, not weeks.
Use Cases
- Executive KPI dashboards and self-serve analytics environments
- Agentic analytics pipelines — ask a business question, get a root cause and recommendation automatically
- User journey, funnel, and feature adoption analysis
- Churn prediction, LTV modeling, and retention cohort analysis
- Demand and inventory forecasting across SKUs and regions
- A/B testing infrastructure and experimentation frameworks
Clients improve activation and retention metrics by 20–40% and reduce ad-hoc analytics requests by 60–80%.
Agentic Data Engineering#
Data plumbing that builds and repairs itself — with your team in control
Think of your business data like the plumbing in a building: it carries information from your apps (sales, billing, support) to the reports you rely on. Those "data pipelines" are plumbing that someone has to build, watch and repair. We use teams of AI agents, like tireless engineers, to do that work around the clock, while your people approve every important change. An agent can turn a plain-English request into working pipeline code, find and connect to your data sources, and fix breakages on its own, such as when an app renames a field ("schema drift"). It also keeps checking that the numbers look right and writes the documentation as it goes.
Use Cases
- Describe a report or data feed in plain English and have an agent draft the pipeline (SQL, dbt or Python) for your team to review
- Automatic discovery and mapping of data sources across your apps, so new systems connect faster
- Self-healing pipelines that spot a changed field or failed job, propose a fix and apply it once approved
- Continuous data-quality checks that flag unusual numbers (like a sudden drop in orders) before they reach a report
- Documentation and "data lineage" (a map of where every number came from) generated automatically
- A coordinated agent team — planner, builder, tester and reviewer — with approvals, guardrails and an audit trail
- Open standards such as MCP (Model Context Protocol, a common way for AI agents to connect to tools and data) to avoid vendor lock-in
Fewer broken reports, less time spent on routine pipeline upkeep, and faster delivery of new data requests — with every change reviewed and traceable.