Enterprise AI & cloud consulting
Fixed-price engagements, scoped after a 30-minute discovery call. Every engagement is delivered by working architects, and every AI agent we put into production carries its own identity, scoped credentials, and a full audit trail.
How an engagement moves
The grouping is the routing each service page already publishes. Strategy sequences the work; architecture and AI security design and review it; delivery builds it; LLMOps and governance keep it standing once it is in production. Governance is placed last because it is bought last, not because it should be designed last. Its own page says it is cheaper to design in than to add, and sends you to architecture if no agents are running yet. Most engagements start in the middle of this, not at the top.
Which engagement fits
Nine engagements. Each one says who it suits and when it is the wrong choice, so you can rule things out before a call rather than during one.
- Enterprise AI StrategyA board-ready roadmap from where you are today to governed AI in production, sequenced by value and risk.Best forExecutives who need a defensible AI plan before committing budgetOrganisations with pilots that demo well and do not reach productionRegulated environments where governance must be designed in, not retrofittedNot this one ifThis is not the right engagement if you already know what to build and need it built. Start with AI platform architecture or RAG platform development instead.
- Enterprise AI ArchitectureReference architectures for agents, RAG, and MLOps that hold up under real load, security review, and audit.Best forTeams whose prototype must now pass architecture and security reviewMulti-cloud estates that need one coherent AI platform patternRegulated workloads where the audit question arrives before launchNot this one ifIf the question is which use cases to pursue rather than how to build them, start with enterprise AI strategy.
- AI Security ConsultingThreat modeling for LLMs, data pipelines, and model endpoints, plus the controls to close the gaps.Best forTeams shipping LLM or agent features into productionSecurity functions asked to sign off on an AI system for the first timeOrganisations facing customer security reviews that now ask about AINot this one ifThis is an application and platform security engagement. It is not a penetration test of your wider estate, and it does not replace one.
- RAG Platform DevelopmentGrounded retrieval on your own corpora, evaluated for accuracy and built to keep your data isolated.Best forTeams whose RAG prototype answers well in demos and poorly in practiceMulti-tenant products where retrieval must respect existing permissionsOrganisations with large internal corpora and no measurement of retrieval qualityNot this one ifIf the corpus does not exist yet, or the answers require reasoning the documents do not contain, retrieval is the wrong tool and we will say so during discovery.
- LLMOpsCI/CD, evaluation, observability, and cost control so models ship and stay reliable in production.Best forTeams with LLM features live and no way to detect quality regressionsOrganisations whose model spend is growing faster than usagePlatform teams supporting several product teams building on shared modelsNot this one ifIf nothing is in production yet, this is premature. Build the system first; the operational practice is worth adding as it approaches real users.
- AI GovernanceIdentity per agent, autonomy tiers, and audit trails your risk and security teams will actually accept.Best forOrganisations with agents in production and no per-agent identityRisk and compliance functions asked to approve autonomous systemsTeams facing an audit, customer security review, or regulatory question about AINot this one ifIf you have not deployed agents yet, governance is cheaper to design in than to add. Enterprise AI architecture is the better starting point.
- Multi-Agent SystemsFleets of governed agents coordinated around real workflows. Not a demo that stalls after the meeting.Best forWorkflows genuinely requiring specialised agents rather than one well-prompted modelTeams whose multi-agent prototype is unpredictable at scaleOrganisations that need agent fleets auditable per actionNot this one ifMany problems presented as multi-agent are better served by a single agent with good tools. If that is the case, we will say so during discovery. It is cheaper for you and more reliable.
- Fractional Chief AI ArchitectSenior AI leadership on a retainer to steer strategy and delivery, without a full-time executive hire.Best forOrganisations with several teams building AI and no shared standardLeadership that needs independent technical judgement before large commitmentsTeams that need senior direction more than another pair of handsNot this one ifThis is not staff augmentation. If the need is implementation capacity, a scoped delivery engagement is the honest answer and usually the cheaper one.
- Cloud AI ModernizationBring production AI to a multi-cloud estate (AWS, Azure, GCP) without a rip-and-replace.Best forMulti-cloud estates where provisioning blocks AI deliveryPlatform teams asked to support AI workloads with existing capacityOrganisations with GovCloud or Azure Government requirementsNot this one ifIf there is no existing estate to modernise, this is the wrong shape, enterprise AI architecture builds the platform directly.
Not sure which one? Start with the assessment.
The Agent Assurance Assessment runs three weeks for $15,000, with the scope and the price published up front. It ends with a register of every AI agent in your environment, a governance evidence pack mapped to NIST AI RMF and ISO/IEC 42001, and one agent actually running under governance, not a slide deck recommending one.