Production AI that does the work.

What should AI fix first?

Pick the result you care about most. The offers and the catalog below reorder around it, and your choice travels with you into the booking.

• More qualified leads
• Less support workload
• Automated operations
• An AI product
• An AI roadmap

AI Operations

Automate repetitive knowledge work without replacing your existing stack.

Best for: Operations and support teams with repeatable, high-volume workflows.

• Support resolution
• Document processing
• Internal knowledge assistants
• CRM and back-office workflows

30–50% less manual handling in suitable workflows · First production workflow in 4–8 weeks

Find my best automation

Voice Revenue Agents

Answer, qualify and book every inbound lead — 24/7.

Best for: Sales and service teams losing opportunities to missed calls or slow response.

• Lead qualification
• Appointment booking
• Support triage
• Reactivation campaigns

<600ms response latency, with contextual human handoff · Production pilot in 2–4 weeks

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AI Strategy & Delivery

Turn scattered AI experiments into a prioritised, shipped roadmap.

Best for: Leadership teams that need senior technical ownership without building an AI department.

• Opportunity audit
• Build-versus-buy decisions
• Governance and security
• Weekly delivery leadership

7 days to a deployable plan and prioritised roadmap · Weekly engagement; roadmap in 7 days

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Six systems. One delivery team.

Each one ships with the same production controls, and each one can be the first step into the others.

• Autonomous marketing agents — Always-on agents that converse, qualify and personalise across your acquisition journey, then write clean context into your CRM. (+3.2× median ROAS lift; launch in 3–6 weeks)
• Conversational and support agents — Production agents grounded in your knowledge, integrated with support tooling and protected by escalation controls. (Up to 92% resolution rate; launch in 4–8 weeks)
• Voice AI agents — Realtime inbound and outbound voice agents for qualification, booking, service triage and reactivation. (Sub-600ms latency; launch in 2–4 weeks)
• Workflow automation — End-to-end orchestration across documents, approvals, CRM actions and operational systems using n8n, Python and custom integrations. (30–50% less manual handling; launch in 3–8 weeks)
• Full-stack AI architecture — Reference architecture for custom AI products: model routing, RAG, evaluations, observability and deployment infrastructure. (99.94% production uptime; launch in 6–12 weeks)
• AI training and enablement — Hands-on enablement that audits your stack, trains your team and turns experimentation into an operating playbook. (4 weeks to AI fluency; launch in 2–4 weeks)

Safe to ship is part of the build.

We design the controls, evaluation and recovery path before an AI system touches a critical workflow — not after the first incident.

• Evaluation suites — Every system ships with tests against your real tasks.
• Human escalation — Clear hand-off with full context when confidence drops.
• Observability — Prompt, version and outcome tracing from day one.
• PII controls — Redaction and retention agreed before data connects.
• Cost and latency — Budgets and alerts per workflow, not per surprise invoice.
• Role-based access — Scoped permissions for every agent and integration.
• Rollback — A tested path back to the previous version.
• EU / GDPR options — EU-hosted deployment when your data requires it.

Find the first use case worth shipping.

Twenty minutes with a senior engineer. You leave with the strongest opportunity, the systems it touches and a sensible next step — whether or not we work together.

Practical questions.

How fast can you deploy?

Focused pilots typically launch in two to eight weeks depending on integrations, data readiness and risk requirements. The first week establishes success metrics, access and the production plan.

Will this replace our existing stack?

Usually not. We integrate with your CRM, knowledge sources, communication systems and operational tools, replacing manual steps rather than forcing a platform migration.

How do you control model risk?

Every production system includes scoped access, evaluation criteria, observability, escalation paths and a rollback plan. PII and retention controls are agreed before data is connected.

Which models do you use?

The one that wins the evaluation for your task — Claude, GPT, Gemini or open-source — on Google Cloud, Vertex AI or your own infrastructure. We stay model-agnostic so you can swap when something better ships.

What does an engagement cost?

Scope depends on data, integrations and risk. A 20-minute call is enough to tell whether a focused pilot, a roadmap engagement or a larger delivery programme is the right fit.