AI Strategy & Integration
I help leadership teams set the AI agenda, choose where to invest, and hold every initiative to a measurable return — with the hands-on capability to deliver it.
Data Ingestion Layer
HubSpot / Salesforce / Pipedrive
Webhooks, Stripe, Email, Forms
Spreadsheets, PDFs, Voice memos
Complex extraction, deep reasoning, nuanced synthesis
High-volume triage, fast classification, enrichment
Deterministic schema verification (Zod), deduplication, hallucination traps, and manual review triggers.
Automated retries with exponential backoff, rate-limit shielding, and complete immutable audit logging.
Production Execution Layer
Normalized CRM deals, contacts & notes
Slack alerts, Linear tickets, email queues
Postgres, Supabase, analytics pipelines
Scope
Production-grade operational plumbing connecting your data, your models, and your revenue engines.
Where AI should change the business, where it shouldn't, and a sequenced investment case leadership can sign off.
A target agreed before anything is built — cost, time, or revenue — and every initiative held to it. What doesn't earn its place is cut.
AI systems integrated into how the business runs, adopted by the people who use them, with cost and risk governed.
“We stop your CRM, email, and spreadsheets fighting each other so you can stop copy-pasting data like it's 2004.”
Production Systems
Verifiable operational middleware, autonomous pipeline architectures, and shipped AI products.
AI briefs arrived ambiguous — "build something with AI" — with no defined inputs, outputs, constraints, or success metrics, and no clarity on whether a given feature should be a copilot a person drives or an agent that acts on its own.
A fast-growing outbound sales agency — running campaigns for 7 enterprise clients, scaling toward 30–40 within a year — was operating entirely out of drifting spreadsheets. In the baseline client dataset alone: only 4 of 1,259 phone numbers were in a usable format, 359 rows had the country typed into the city field, 64 of 248 companies didn't match any approved target list, and there was no unified opt-out enforcement across clients — a real compliance risk, not just a tidiness problem.
Staff onboarding and day-to-day workflows had usability issues the teams using them had come to treat as permanent — the kind of friction people stop reporting because they've stopped expecting it to change. Nobody had gone back to actually fix the root causes.
The Mandate
Most organisations are already spending on AI: licences, pilots, internal experiments, vendor tools. Far fewer can say what that spend has returned. Pilots multiply, ownership is unclear, and the board asks the same question every quarter: what are we getting for this?
The issue is rarely the technology. It's the absence of a strategy that ties AI to business outcomes, and of the discipline to measure them. That's the gap I close.
I work with leadership teams to decide where AI should change the business and, just as important, where it shouldn't. We map the operations, revenue processes, and customer journeys where AI can move a number that matters, then rank them by value, feasibility, and risk.
The output is a sequenced AI roadmap with an investment case for each initiative that leadership can sign off with confidence. No AI for its own sake, and no strategy that can't be delivered.
Before anything is built, we agree the return it must deliver: hours saved, cost removed, revenue added, or risk reduced. That target is measured after launch, not assumed. Initiatives that hit it get scaled. Those that don't get fixed or cut.
This turns AI from a cost centre into a portfolio leadership can manage, with the same rigour you'd apply to any other capital allocation decision.
Strategy fails at integration. I don't stop at the roadmap. I design and integrate AI into your existing systems, data, and workflows, using the right model for each job, and work with your teams until the new way of working sticks.
That hands-on capability is the difference. Most AI strategy consultants advise and leave; most AI developers build without a business case. I do both, so what gets decided in the boardroom is what actually ships.
AI introduces new running costs, new data risks, and new ways for teams to work around a system they don't trust. I put governance in place from the start: model and vendor choices, cost controls, data guardrails, and adoption measures, so the return holds after the launch.
An AI strategy consultant helps leadership decide where AI should be used in the business, builds the investment case, and oversees implementation so that AI spend delivers a measurable return rather than a collection of disconnected pilots.
By agreeing a target before anything is built, such as hours saved, cost removed, revenue added, or risk reduced, then measuring against that baseline after launch. Initiatives are scaled, fixed, or stopped based on the result.
Yes, and it's a common starting point. We review what's running, measure what it's returning, and decide what to scale, fix, or cut, then set a strategy for what comes next.
Both. I set the strategy with leadership and integrate the AI into your systems and workflows hands-on, working with your teams through adoption.
I'm model-agnostic and work across the Anthropic and Google Cloud ecosystems, choosing the right model for each job on cost, quality, and risk rather than committing the business to a single vendor.
With a free call focused on one area of the business. It's an honest view of where AI would earn its place and where it wouldn't, with no deliverable and no obligation.
Execution Process
Diagnosis before prescription. A disciplined four-stage gateway model so you never pay for unnecessary builds.
20 minutes, one workflow, verbal. An honest diagnosis on where AI would earn its place — and where it wouldn't.
The diagnostic. Your workflows and data foundations mapped end to end, showing what to fix before automating.
The custom middleware, webhooks, and multi-model routing pipelines, built and tested against live data.
Ensuring the team actually uses it, edge cases are caught, and the system holds reliably after handover.