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AI builds

AI Implementation & Digital Infrastructure

AI-powered systems, CRM and martech infrastructure, integrations and the data plumbing that makes AI optimisation possible at all. The build work behind the platforms — for clients who want our AOS-style infrastructure inside their own business.

  • AI workflows designed for production reliability, not demos
  • Closed-loop data infrastructure across CRM, ads and analytics
  • Custom AI implementations under your brand and policy guardrails

AI implementation is not buying tools. It's the design and build work that turns AI capability into reliable commercial outcome inside a business — workflows, data infrastructure, integrations, governance, the production systems that survive contact with real operations. We've built our own Autonomous Operating System (AOS) to run marketing programmes; this service is for businesses that want similar infrastructure inside their own commercial operations — under their brand, their policy guardrails, their commercial logic.

What this service covers

The standard programme covers:

  • Custom AI workflow design: agent architectures, tool integration, prompt engineering, evaluation harnesses, guardrails for specific commercial use cases
  • Production AI implementation: monitoring, observability, fallback paths, human-in-the-loop escalation, audit trails
  • CRM build and integration: Salesforce / HubSpot / Pipedrive / custom CRM implementation, data model design, automation, integration with the wider stack
  • Martech infrastructure: marketing automation platform integration, customer data platform setup, attribution architecture, lifecycle messaging infrastructure
  • Data plumbing: ETL/reverse-ETL pipelines (Hightouch, Census, Fivetran), data warehouse setup (Snowflake, BigQuery, Redshift), event tracking infrastructure
  • API and integration work: connecting CRM, ads, analytics, ESP, support, ops systems into a coherent stack
  • Governance and security: AI policy guardrails, audit trails, data handling compliance, access control

Why AI implementation is harder than it looks

The gap between an AI demo and an AI production system is large and often underestimated. McKinsey's research on AI implementation consistently shows that the failure pattern isn't model capability — it's the surrounding production infrastructure. Specifically:

  • Reliability: AI systems fail differently from deterministic software. Outputs vary; edge cases produce strange results; the failure modes are subtle. Production systems need monitoring, fallback paths and human-in-the-loop escalation that demos don't.
  • Integration: most useful AI workflows touch multiple systems — CRM, ads, analytics, ESP, ops. The integration work dominates the engineering effort, not the prompt or model selection.
  • Governance: who's accountable when the AI makes a wrong decision? What's the audit trail? How is policy enforced? Production systems answer these explicitly; demos don't.
  • Cost: token costs scale with usage. A workflow that costs $0.05 per execution at demo volume costs $50,000/month at production volume. Cost optimisation is a meaningful engineering concern.
  • Maintenance: models update, APIs change, integrations break, business logic evolves. AI systems need ongoing maintenance like any production software. They're not 'set and forget'.

Implementations that succeed treat all of these as foundational. Implementations that fail treat them as Phase 2 concerns and never get there.

How an implementation runs

Implementation methodology

From use case to production

Typical 12-32 week engagement depending on use-case scope. Production-ready by phase end, not 'mostly working with known issues'.

  1. Scope

    Use case definition + success criteria + commercial framing

    What's the specific commercial outcome the AI workflow needs to drive? What does success look like quantitatively? What's the human-in-the-loop boundary? Without explicit scope, AI projects drift indefinitely.

  2. Architect

    System design + tool integration + governance

    Agent architecture, tool selection, integration map, guardrail definition, policy boundaries, escalation triggers, audit-trail design. The architectural decisions made here determine production reliability and maintainability.

  3. Build

    Workflow + integration + evaluation harness + monitoring

    Build the workflow itself, plus the integration layer that connects it to source systems, plus the evaluation harness that measures it against success criteria, plus the monitoring that catches production issues. All four are foundational; skipping any is technical debt.

  4. Validate

    Production test + evaluation + iteration

    Run the workflow against production-equivalent data. Measure against the success criteria defined at scope. Iterate on prompts, integration logic, guardrails based on what evaluation reveals. Bake in known failure modes as test cases.

  5. Operate

    Production deployment + monitoring + ongoing iteration

    Phased rollout. Monitoring alerts on degradation. Human-in-the-loop escalation tested and documented. Iteration roadmap for ongoing improvement based on production data.

Common AI implementation use cases

Categorised by where the AI workflow sits in the commercial operation:

Marketing

  • AOS-style marketing optimisation platform (custom version of our internal AOS, configured for the client's specific channel mix, brand rules and commercial targets)
  • Lead-qualification and routing workflows
  • Content production workflows (drafting, briefing, reviewing — under named-author governance)
  • Audience and segmentation workflows
  • Ad creative generation under brand guardrails

Sales

  • Sales-call analysis and follow-up generation
  • CRM hygiene automation (cleanup, deduplication, enrichment)
  • Pipeline analysis and forecasting workflows
  • Outbound research and personalisation workflows

Customer support

  • Tier-1 support agent augmentation (drafting responses, routing escalations)
  • Knowledge-base maintenance workflows
  • Voice-of-customer analysis from support tickets, reviews and call transcripts

Operations

  • Document processing workflows (invoice processing, contract review, intake forms)
  • Reporting and dashboard automation
  • Internal knowledge-management workflows

We've worked across all of these. The common thread isn't the model — it's the surrounding production engineering, integration work and governance design.

AI implementation vs alternatives

Make vs buy decision

When custom implementation makes sense vs alternatives

Dimension
Custom AI implementation (this service)
Off-the-shelf AI tools
Time to first value
12-24 weeks
Days to weeks
Cost (initial)
£75-£500k+
£0-£15k/year per tool
Fit to commercial logic
Designed to your specifics
Generic — you adapt to the tool
Proprietary advantage
Your custom workflow + data — defensible
Same tool your competitors use
Integration depth
Native into your stack
Generic integrations + workarounds
Maintenance
Your team or ours; stable architecture
Vendor-driven; tool deprecation risk
Best for
Use cases where AI is core competitive advantage
Use cases where AI is supporting capability

The honest answer for many AI use cases is off-the-shelf tools — there's no point spending £200k building a custom email-writing assistant when ChatGPT or Claude does it well enough at £25/month per seat. The economics flip when AI is core to commercial advantage: marketing optimisation, customer-facing personalisation, proprietary workflows where the data and the workflow ARE the competitive moat.

What it costs

Implementations are scoped engagements priced on complexity and outcome. Indicative ranges:

  • Single-workflow implementation (well-defined use case, existing data infrastructure): £75-£200k
  • Multi-workflow programme (3-5 use cases on shared infrastructure): £200-£600k
  • Comprehensive AI infrastructure (custom platform, multiple workflows, full data layer build): £400k-£2M+
  • AOS-style marketing platform implementation (configured for client's specific operation): £300-£800k

Ongoing operation typically £8,000-£40,000/month depending on workflow complexity, monitoring requirements and iteration roadmap intensity.

Interactive · Cost Calculator

Compare against your current AI / infrastructure spend

Set in-house engineering, current AI tool subscriptions and integration cost. The calculator gives you a rough baseline for the comparison.

Your current setup

Current annual cost (excluding media)

£180,000

People + agency + tools. Media spend is held constant on both sides.

AI-powered agency · annual cost (excluding media)

£85,202

Management fee on £20,000/month spend at 23.0% + your existing tools.

Difference

£94,798/year

£7,900/month freed up. Reinvested into media, that’s an extra 4.7 months of working spend each year.

Build your growth plan→

Indicative only. Loaded cost per head includes salary, oncosts, software seats and overhead. Real proposals model your specific channel mix, attribution and margin targets via the discovery.

Where this service wins

  • Businesses with clear commercial use cases for AI where off-the-shelf tools don't fit — proprietary workflows, brand-specific guardrails, integration depth that generic tools can't match
  • Operations where AI is core competitive advantage rather than supporting capability — the IP is in the workflow + data + governance, not in the model
  • Mid-to-large operations rebuilding their CRM and martech infrastructure for AI-readiness — combining the platform build with the AI workflow design saves rework later
  • Businesses that have tried generic AI tools and hit the limits — where the next step is custom implementation, not more vendor evaluation

Where it doesn't fit

  • Use cases where off-the-shelf tools (ChatGPT Enterprise, Claude for Work, Microsoft Copilot, Notion AI) cover 80%+ of the value at 5% of the cost — generic tools win
  • Operations without the data infrastructure to support AI workflows — usually the right first step is the data + integration work, not the AI workflow itself
  • Businesses looking for AI as a strategic narrative rather than a commercial capability — implementations need explicit success criteria; vague aspiration produces poor outcomes

Read deeper on this

FAQs

Common AI implementation questions

Are you building agent frameworks or wrappers around foundation models?

Both, depending on use case. For most commercial workflows, well-designed prompt engineering + tool calling on a strong foundation model (Claude, GPT-4 class) outperforms complex agent frameworks. Agent frameworks make sense for genuinely multi-step autonomous workflows. We make the architectural decision based on the use case, not on framework preference.

Which foundation model do you use?

Use-case dependent. Claude (Anthropic) is our default for most production workflows due to reliability, instruction-following and tool use quality. GPT-4 class models for use cases where their specific capabilities fit better. Open-source models (Llama, Mistral) for use cases where data sovereignty or cost optimisation matters at high volume. We recommend based on workflow needs, not vendor relationships.

How do you handle data privacy for AI workflows?

Foundationally. Workflows that process sensitive data run via privacy-preserving APIs (no training on your data, encrypted in transit, data retention controls). For high-sensitivity use cases (PII, regulated industries), we deploy via VPC or on-prem inference. Compliance documentation (SOC 2, ISO 27001 evidence, GDPR) provided for procurement.

What about ongoing model updates? Do workflows break when foundation models change?

Sometimes — model updates can change behaviour subtly. Production workflows include evaluation harnesses that re-run when model versions change, catching regressions before they hit users. Maintenance budget assumes 1-2 significant model upgrade cycles per year per workflow.

Can you build the AOS for our business?

Yes — this is one of our most-requested implementations. Configured to your channel mix, brand rules, commercial targets and operational context. Same architectural patterns as our internal AOS; specific business logic is yours. Typical scope: 6-12 month build, £300-£800k initial investment, ongoing operation thereafter.

How do you handle governance and AI policy?

Explicit guardrails configured into every workflow: brand rules, decision boundaries, escalation triggers, audit trails. Decisions outside agreed bounds escalate to named humans. Audit logs maintained for every workflow execution. Policy reviewed quarterly with the client.

What if our team wants to build this in-house instead?

Often the right answer if you have the engineering capability. We sometimes engage in advisory mode — helping in-house teams design the architecture, evaluate vendors, set up evaluation harnesses, build governance — without doing the implementation work ourselves. If your team has senior engineering leadership and AI fluency, in-house is usually faster long-term.

How do you measure AI implementation success?

Use case-specific. For marketing optimisation: improvements in blended ROAS or CAC efficiency. For sales: measurable changes in cycle time, conversion rate or rep productivity. For support: cost-to-serve, resolution rate, customer satisfaction. Success criteria defined at scope; reported continuously through monitoring.

Can you integrate with our existing systems (Salesforce, HubSpot, custom backends)?

Yes — integration work is most of the engineering effort in any AI implementation. We've integrated with all major CRMs, marketing automation platforms, data warehouses, ESPs and many custom backends. The integration architecture is part of the design, not an afterthought.

What's the maintenance burden after launch?

Lighter than equivalent custom software but not zero. Model updates, prompt tuning based on production data, integration maintenance, evaluation-suite expansion. Typical £8-40k/month depending on workflow complexity and iteration intensity. AI systems aren't 'set and forget' — but the maintenance is structurally smaller than equivalent rule-based systems.

Sources and further reading

Next step

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