We work where
the leverage is. Most start with adoption.
We don't sell packaged engagements. The shape depends on what your business actually needs — but most fit one of the three patterns below, and most start with AI adoption, because supercharging your teams is where the leverage compounds from. Each engagement begins with a short discovery pocket so we both know what we're committing to before either of us signs.
Most operating businesses are getting pressure from their board to adopt AI but have no idea where to start. A few people are experimenting in shadow IT. License purchases sit unused. The 'AI strategy' deck circulates but nothing ships. Meanwhile competitors are quoting AI in their press releases.
We supercharge teams with the best-practice patterns that actually work. A 2–3 week discovery pocket finds where AI changes the unit economics in your business — across engineering, operations, marketing, HR, finance. Then we roll out the patterns: team collaboration playbooks, local AI on engineering workstations (Claude Code, Copilot, Cursor), prompt libraries and custom agents tuned to each function (Claude, ChatGPT, M365 Copilot), and governance that satisfies legal without strangling productivity. The training isn't a one-day workshop — it's the program.
65% of organisations have abandoned at least one AI project because of the skills gap (Pluralsight, 2025). The skills gap is the binding constraint on AI adoption — not the technology, not the budget. We bridge it and unlock the velocity AI promised. By the end of the engagement, your team owns the playbook, the tools, and the metrics. Then we can talk about agents.
Deliverables
- Discovery pocket: per-team AI opportunity mapping with sized quick wins (engineering, ops, marketing, HR, finance, legal)
- Tool selection per function — Claude Code, Copilot, Cursor, Codex for devs; Claude, ChatGPT, M365 Copilot for operators; specialised tools per team
- Training program for engineers AND non-engineers — not one-day workshops, real skill building tied to the actual work
- Governance model: data visibility, license review, telemetry, audit trail, vendor approval
- Team-level configuration: prompt libraries, custom agents per function, MCP integrations, per-function safeguards
- Adoption metrics: actual usage, time-back, quality, employee NPS — not vanity 'licenses purchased'
- EU AI Act readiness (high-risk obligations live Aug 2026)
Best fit
Operating businesses between 100 and 5,000 employees adopting AI at organisational scale for the first time. Especially when leadership wants something to ship in 90 days, not 12 months.
Typical duration
2–3 week discovery pocket + 8–12 weeks rollout-and-train per team, then ongoing support
Team
Adoption lead + training partner + governance/security advisor + per-function specialists
You don't skip to Engagement 02. After 01 has built the skills and the tools are in real use, the repetitive workflows worth automating reveal themselves. Claims triage. Document review. Lead qualification. Prior-authorisation. Compliance review and reporting. Anywhere a human is currently routing things between systems, often after hours.
Most "AI agent" projects fail one of two ways: impressive demos that fall over the moment a real edge case shows up, or production deployments with no way to tell if they're working. MIT NANDA found 95% of enterprise GenAI pilots never deliver measurable P&L impact. Gartner expects 40% of agentic AI projects to be cancelled by 2027 due to cost overruns, unclear business value, and inadequate risk controls.
We map each workflow step by step and decide deliberately: full automation where the risk is low and the pattern holds, human-in-the-loop where judgment matters, no AI at all where it's the wrong tool. Then we build the agents the way the rest of your stack is built — with an eval harness, observability, rollback paths, identity-aware tool access, OWASP Agentic Top 10 mitigations, and ownership. Running in your cloud, in your security perimeter.
Deliverables
- Use-case selection: high-volume, repetitive, measurable workflows from your business
- Agent design: tools, memory, planning, escalation rules
- Eval suite: golden traces, regression tests, online evals in production
- Observability stack: tracing, session replay, drift detection (LangSmith / Langfuse / Arize / Datadog)
- Identity & access: RBAC, just-in-time approvals, immutable audit logging
- Governance: OWASP Agentic Top 10 mitigations, EU AI Act (Aug 2026) + Colorado AI Act (Jun 2026) compliance
- Hosted deployment: managed runtime (Bedrock AgentCore / Azure AI Agent Service / Vertex AI) with VPC isolation
- Quarterly tuning + cost discipline
Best fit
Businesses that have completed (or are completing) Engagement 01 and have specific high-volume, repetitive workflows worth automating. Anywhere a human is currently routing things between systems, after hours, or both.
Typical duration
10–16 weeks demo-to-production, then continuous operation
Team
Agent engineer + eval lead + integration engineer + governance advisor
Customer-facing AI is the largest new spend category in enterprise software for 2026 — and the hardest to get right. Gartner predicts that in 2026, one-third of companies will damage their customer experience by deploying AI prematurely. Stanford research found purpose-built legal-AI tools hallucinate on 17–33% of queries. The cost of shipping the wrong AI feature is higher than shipping no AI at all.
We're advisory, not implementation. Your team (or your vendor) ships it; we bring the patterns. The principle we work from: AI should make your existing experience smarter — not bolt on a new chatbot no one uses (B2B SaaS chatbot adoption sits at 5–15% of MAU after 90 days). That might be a smarter portal that summarises status in plain language. A recommendation engine that shows its reasoning. An intelligent scheduling system that explains the trade-off. The form changes; the principle doesn't. We help you decide what to build, what to skip, build-vs-buy, governance, and the guardrails that protect the brand.
Deliverables
- Use-case prioritisation with sized KPIs and explicit go/no-go gates
- Build-vs-buy-vs-embed framework (MIT NANDA: 67% success for buy/partner vs. 33% for internal builds)
- Customer-facing AI feature design patterns: semantic search, embedded action automation, anomaly surfacing, plain-language summaries
- Product governance: prompt-injection resistance, brand-voice guardrails, output evals, customer-data privacy
- EU AI Act high-risk categorisation review + workflow-redesign roadmap
- Change management for product, design, and customer-experience teams
Best fit
Established businesses considering AI in their customer-facing offerings — portals, websites, apps, lead pipelines, support flows. Especially when competitors are shipping AI features and you need to figure out which ones matter for your customers.
Typical duration
4–8 weeks for the strategic engagement; advisory retainer optional after
Team
CX-AI strategist + design lead + governance advisor
Not sure which fits?
Send us two paragraphs about what you're trying to do. If it's not in one of these three, we'll tell you — and we'll point you at someone who does do it.