AI Solutions

Practical AI, deployed into the systems your business already runs on.

We move executives from AI curiosity to measurable AI advantage — with custom AI agents, agentic systems, Microsoft Copilot rollouts and governed private-model deployments that plug into your existing stack.

Enterprise AI Division

We move executives from AI curiosity to measurable AI advantage — with custom AI agents, agentic systems, Microsoft Copilot rollouts and governed private-model deployments that plug into your existing stack.

40+

AI deployments shipped

12wk

Typical time to first live agent

3.2x

Avg productivity lift, first quarter

100%

Human-in-the-loop by design

The problem

Most organisations are stuck between AI theatre and AI paralysis.

Leadership sees the potential. Teams are exhausted by demos that never ship. Meanwhile competitors are quietly compounding advantage — one agent, one automated workflow, one governed copilot at a time. The gap is not talent. It is architecture, governance and delivery discipline.

  • Pilots that never make it out of a sandbox because there is no production path.
  • Copilot licences sitting idle because nobody owns rollout, training or governance.
  • AI initiatives blocked by security, privacy and compliance concerns nobody addressed up front.
  • A dozen point-tools that don't integrate with your CRM, ERP or ticketing systems.
  • No measurable ROI framework — so every AI conversation ends in scepticism.
  • Executives asked to sign off on AI they don't actually understand yet.

What we deliver

A full AI capability, engineered end to end.

Strategy, build, integration, governance and enablement. Every layer delivered by senior AI engineers, not junior operators reading playbooks.

AI Readiness & Strategy

A structured diagnosis of where AI creates real leverage in your business — and where it doesn't.

  • AI Opportunity Map across 12+ business functions
  • Data, security and compliance readiness assessment
  • 12-month AI roadmap tied to ROI

Custom AI Agents

Purpose-built AI employees for sales, service, marketing, HR, finance and operations.

  • Trained on your data, tone and policies
  • Integrated with CRM, ERP, ticketing and comms
  • Guardrails, escalation and audit built in

Agentic AI Systems

Multi-step agents that reason, plan and execute — with human approval at the decisions that matter.

  • Planner / executor / reviewer architecture
  • Tool use across APIs, databases and browsers
  • Approval workflows and rollback controls

Microsoft Copilot Rollout

Turn dormant Copilot licences into measurable productivity across your workforce.

  • Copilot Studio, Copilot for M365, Sales & Service
  • Prompt libraries and role-based enablement
  • Adoption tracking and value reporting

Private AI & Knowledge

Enterprise knowledge bases and private models that never leak IP into the public internet.

  • Retrieval-augmented generation over your data
  • Azure OpenAI, Bedrock, Vertex, on-prem GPU
  • Role-based access and full audit trails

AI Governance & Policy

Board-ready AI policy, risk register and acceptable-use frameworks aligned to ISO 42001.

  • Responsible AI charter and staff policy
  • Model risk register and evaluation plan
  • ISO 42001, NIST AI RMF and EU AI Act alignment

How we deliver

A delivery model built for enterprise reality.

No open-ended R&D. Every phase has a definition of done, a decision gate and a measurable outcome your board can defend.

  1. 01

    Phase 01 · Diagnose

    AI Opportunity Map & Readiness Audit

    We interview leaders, review data, security and systems, and map every viable AI use case in your business — sized by value and effort.

    • Executive workshop and stakeholder interviews
    • Prioritised opportunity map across departments
    • Readiness scorecard: data, security, change, skills
  2. 02

    Phase 02 · Design

    Architecture, Governance & Roadmap

    We design the technical architecture, integration model and governance framework — and lock in a 12-month roadmap with clear milestones.

    • Reference architecture and integration plan
    • AI policy, risk register and approval workflow
    • 12-month roadmap with budget and ROI model
  3. 03

    Phase 03 · Build

    Pilot to Production in ≤12 Weeks

    We build the first two use cases end-to-end — from prompt engineering and tool integration through testing, evaluation and go-live.

    • Two production-grade AI agents deployed
    • Evaluation harness and quality benchmarks
    • Documentation, runbooks and handover
  4. 04

    Phase 04 · Scale

    Enablement, Adoption & Continuous Value

    We train your teams, embed governance, monitor performance and roll out subsequent agents — turning AI from initiative into operating model.

    • Role-based training and prompt libraries
    • Adoption dashboards and monthly value reviews
    • Ongoing agent development and optimisation

The AI delivery lifecycle

Every agent moves through the same five-stage discipline.

Discover → Design → Build → Validate → Operate. No pilots without a production path. No production without governance. No governance without measured business value.

  1. 01

    Discover

    Map high-leverage AI opportunities across the business.

    • · Opportunity register
    • · ROI hypothesis
    • · Data readiness score
  2. 02

    Design

    Architect the agent, its tools, guardrails and integrations.

    • · Agent blueprint
    • · Tool & API map
    • · Governance controls
  3. 03

    Build

    Engineer the planner, executor and reviewer with your data.

    • · Working agent
    • · Eval harness
    • · Rollback path
  4. 04

    Validate

    Red-team, benchmark and secure sign-off from risk and legal.

    • · Accuracy & safety scores
    • · Bias & privacy review
    • · Approval sign-off
  5. 05

    Operate

    Deploy under monitoring and continuously improve in production.

    • · Adoption dashboards
    • · Drift alerts
    • · Value reviews

Agentic AI

Agents that don't just answer questions — they get work done.

Agentic AI moves beyond chatbots. We architect multi-step systems that plan, use tools, call your APIs, escalate to humans on the decisions that matter, and hold state across long-running workflows. The result: real business operations executed by governed AI — with full audit, rollback and observability.

82%

of admin work automated in service ops pilots

24/7

queue coverage with zero seat-cost scaling

9d

average time from spec to first live agent

01

Planner

Decomposes a business goal into a sequence of steps, chooses tools, and reasons over context.

02

Executor

Calls APIs, queries data, writes to systems of record — with typed inputs and structured outputs.

03

Reviewer

Grades each action, enforces policy, requests human approval for anything high-stakes.

04

Orchestrator

Coordinates multiple agents, retries, escalations and long-running workflows across days or weeks.

Guardrails as first-class citizens

  • Typed tool schemas — no free-text side effects
  • Human-in-the-loop for spend, contracts, customer comms
  • Full trace: prompts, tool calls, outputs, decisions
  • Rollback path on every write to systems of record
  • Isolated tenants — no cross-customer data leakage
  • Evaluation harness re-run on every model or prompt change

Executive AI Coaching

A private advisor for the leader whose next decade will be defined by AI.

Four private engagements a month with a senior AI advisor. Confidential. Practical. Focused on your business, your board, your calendar. Built for founders, CEOs and executive teams who refuse to be the last in their sector to move.

M01

AI Literacy for the C-Suite

A private, jargon-free grounding in how modern AI actually works — and where it doesn't.

  • · Boardroom-ready vocabulary
  • · Vendor claim triage
  • · Personal prompt playbook
M02

AI Strategy Sessions

Working sessions with your leadership team to shape the AI thesis for the next 12 months.

  • · Prioritised opportunity map
  • · Investment thesis
  • · Success metrics
M03

Board & Investor Briefings

We prepare and co-present the AI narrative your board, investors and regulators want to hear.

  • · Board pack
  • · Risk & assurance view
  • · Roadmap defence
M04

Personal AI Operating System

A private stack of copilots, prompts and workflows built around how you actually run your week.

  • · Executive copilot
  • · Prompt library
  • · Weekly review ritual

The engagement

12 weeks. Four sessions a month. One senior advisor. No cohorts, no group calls.

Every engagement is bespoke and confidential. We work inside your calendar, your systems and your board rhythm. Applications are reviewed personally — we take on a small number of executive clients each quarter.

Measured outcomes

What our AI engagements typically produce in the first 12 months.

30–60%

Time recovered

on qualifying admin, service and content tasks.

2–5x

Sales throughput

on prospecting, qualification and follow-up workflows.

-45%

Response times

across customer service and internal helpdesk queues.

6–14x

Return on invest

measured on year-one deployments, net of licensing.

They didn't sell us AI. They rebuilt how our teams work, made it defensible to the board, and shipped agents that our people actually use every day.
Chief Operating Officer · National Professional Services Group

Tooling & partners

We build on enterprise-grade platforms, not novelty stacks.

Foundation models

  • Azure OpenAI
  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • Meta Llama

Agentic frameworks

  • Microsoft Copilot Studio
  • LangGraph
  • n8n
  • Semantic Kernel
  • Custom Python / TypeScript

Data & retrieval

  • Azure AI Search
  • Pinecone
  • Postgres pgvector
  • SharePoint
  • Snowflake

Governance

  • ISO 42001
  • NIST AI RMF
  • Microsoft Purview
  • Defender for Cloud
  • Custom evals

Frequently asked

Executive-level questions we hear most.

How do you keep our data safe when deploying AI?+

All private-model deployments run inside your Azure, AWS or GCP tenant — data never trains public models. We layer identity, DLP, Purview labelling and full audit logging on every integration.

Do we need to be on Microsoft to work with you?+

No. We are Microsoft-first because most of our clients are, but we routinely deliver on OpenAI, Anthropic, Google Vertex, AWS Bedrock and open-source models. We recommend based on fit, not vendor loyalty.

What does a first engagement typically cost?+

The AI Opportunity Map and Readiness Audit is a fixed-scope engagement. From there, most clients budget between $60k–$220k for the first two production agents. We share indicative pricing openly during the audit.

Can you work alongside our internal team?+

Yes. Roughly half our engagements are co-delivered with internal engineering, data or product teams. We upskill your people as we ship — the goal is capability transfer, not lock-in.

Start with a diagnosis, not a proposal

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