Sectors
Our practitioners have delivered AI and data programmes inside organisations across retail, consumer goods, manufacturing, financial services, and the public sector. We bring domain knowledge that makes a material difference to the quality of our diagnosis and the relevance of our recommendations.
Most retail AI programmes fail not because of the AI but because of the data beneath it. Fragmented customer identity, disconnected commerce and CRM systems, and a personalisation layer that cannot see a complete picture of the customer it is trying to serve.
We work with DTC brands, omnichannel retailers, and marketplace operators across Shopify Plus, Salesforce, and Adyen. Our engagements typically start from one of two places: a platform that is live but not delivering, or a shortlist under evaluation before budget is committed.
Talk to our Retail teamAI personalisation not converting. Shopify and Salesforce hold different views of the same customer. The AI is making decisions on incomplete profiles.
Checkout conversion lower than expected. Payment friction, mobile performance issues, or a loyalty integration that breaks the flow at the critical moment.
Post-purchase experience not joined up. Returns, loyalty accrual, and re-engagement flows operating in isolation, creating a customer experience that contradicts the brand.
A fashion retailer with £15M annual revenue had completed a Shopify Plus and Klaviyo implementation six months earlier. AI personalisation was live but click-through on personalised recommendations was below their pre-AI baseline. Aravian identified a customer identity resolution failure between Shopify and Salesforce within the first week. Following remediation, personalised email CTR improved 34% and repeat purchase rate recovered to above pre-implementation levels within 60 days.
Consumer goods organisations face a data challenge that is structurally more complex than most: their customer data sits partly in retailer systems they do not own, their DTC operation is often separate from their trade channel, and the AI they want to deploy depends on connecting all of it.
We work with CPG brands building or fixing the data infrastructure that supports AI-driven demand sensing, trade spend optimisation, consumer personalisation, and supply chain exception management. We understand the data architecture challenges that are specific to the CPG distribution model.
Talk to our CPG teamNo direct customer data from retail partners. AI models trained only on DTC data make incorrect predictions for the majority of volume that moves through wholesale and retail.
Demand sensing not working at SKU level. Aggregated category data from retail partners is not granular enough for the AI to make useful replenishment recommendations.
Trade spend attribution missing. AI-generated promotion recommendations cannot be validated against actual trade ROI because the data is not connected.
A UK consumer goods brand was deploying AI-driven demand sensing across twelve retail partners and their own DTC channel. The AI was producing SKU-level replenishment recommendations that were consistently 25% above or below actual demand. The cause was a data integration gap between the retailer sell-through feeds and the internal demand model. Aravian rebuilt the integration architecture using MCP connectors and the accuracy of replenishment recommendations improved to within 8% of actual demand within the first full cycle.
Manufacturing organisations deploying AI face a set of requirements that most AI delivery teams do not fully understand: full audit trails, deterministic behaviour within defined parameters, AI Assurance for AI-generated code in safety-critical systems, and governance processes that can withstand regulatory scrutiny.
We work with manufacturers across discrete and process sectors deploying AI for supply chain exception management, quality assurance, predictive maintenance, and engineering document generation. We understand the governance and assurance requirements that apply to AI in operational environments.
Talk to our Manufacturing teamAI-generated code reaching production without appropriate assurance. Traditional QA tests code behaviour. It does not test AI output correctness or decision reliability under conditions not seen in training.
Supply chain AI not acting on exceptions. The system identifies the issue. It flags it. Then a human spends three hours doing the same analysis the AI could have completed and executed in minutes.
B2B quoting not connected to live capacity. AI-assisted quotes are based on standard lead times rather than actual production capacity, creating commitments that operations cannot always honour.
A UK precision engineering company had deployed an AI system to assist with customer quote generation for complex B2B orders. The system was producing quotes that engineering operations regularly could not fulfil within the stated lead time, because the AI did not have access to live production capacity data. Aravian implemented an MCP integration connecting the quoting AI to the production scheduling system and the ERP capacity data. Quote-to-commitment accuracy improved from 64% to 91% within the first three months of operation.
Financial services organisations deploying AI carry an additional layer of obligation: regulatory compliance, model explainability, audit trail requirements, and EU AI Act risk classification for any AI system that influences a financial decision. These are not optional extras. They are conditions of operating.
We work with fintech organisations, payment platforms, and regulated financial services businesses deploying AI in customer-facing and decision-critical contexts. We understand the compliance requirements and build them into the architecture from day one rather than retrofitting them later.
Talk to our Fintech teamAI decision trail missing for regulatory review. An AI system is influencing credit decisions, fraud scoring, or customer tiering without a documented audit trail that satisfies FCA or EU AI Act requirements.
Model drift not being monitored. The model producing risk scores was validated at deployment. It has not been formally reviewed since. The business does not have a process to detect when its behaviour has drifted outside the validated parameters.
Payment optimisation AI not connected to real-time data. The AI is making routing and optimisation decisions based on settlement data that is 24 hours old, when the decisions it needs to make require near-real-time payment telemetry.
A payment technology scale-up had deployed an AI fraud detection model that was flagging a significant number of legitimate transactions as high-risk. The false positive rate had increased from 1.2% at deployment to 4.8% over twelve months. The cause was model drift: the transaction patterns the model was trained on had shifted significantly as the client base grew. Aravian implemented continuous model monitoring, rebuilt the retraining pipeline with live payment data, and established an ongoing monitoring regime. False positive rate returned to 1.1% within six weeks.
Public sector organisations deploying AI carry a higher standard of accountability than most: GDS alignment, full audit trails, explainability requirements, and AI systems that can be defended in public and in parliament if required. We build to those standards, not to a commercial minimum.
We work with local authorities, NHS trusts, central government departments, and public sector arm's length bodies deploying AI in citizen-facing and operational contexts. Our practitioners understand GDS Service Standard, the government AI playbook, and the emerging NHS AI governance requirements.
Talk to our Public teamDiscovery not GDS-compliant. The team has moved to alpha without completing the user research and service design work that a GDS assessment panel would expect to see documented at discovery.
AI for citizen services not explainable. An automated decision or AI-assisted recommendation is being made about a citizen. The organisation cannot produce a plain-language explanation of how the decision was reached in a form the citizen can understand and, if necessary, challenge.
Azure Gov cloud migration stalled. The organisation has committed to moving sensitive workloads to Azure Government Cloud but the migration is behind schedule and the team does not have the Azure Gov-specific expertise the project needs.
A London borough council had developed an AI tool to assist housing officers in prioritising repair requests based on risk to tenant health and safety. The tool had been built by an internal team but the council's legal advisers had flagged that the decision logic was not documented in a form that would satisfy a challenge under the Equality Act or a Subject Access Request. Aravian conducted a governance review, documented the decision logic in plain language, implemented a human review gate for all decisions above a defined risk threshold, and produced the risk classification documentation required under the EU AI Act.
Customers in travel and hospitality are among the most forgiving in any sector , if the recovery is fast and the communication is honest. The problem is that most organisations in this sector do not know there is a problem until the review appears on TripAdvisor or the refund request arrives. By that point, the customer is already lost.
We work with hotels, travel operators, airlines, and hospitality groups deploying AI to reduce booking friction, automate service recovery, and build the loyalty architecture that keeps high-value customers returning. We understand the real-time data requirements and the integration complexity that makes AI genuinely useful in this sector.
Talk to our Travel teamBooking abandonment not understood. A guest starts a booking, encounters friction at the payment step or the room selection, and leaves. The data exists to identify exactly where and why. It is not being read in time to act on it.
Service recovery too slow to retain the customer. A flight is delayed. A room is not ready. A transfer fails to show. The customer contacts support. By the time the case is resolved, the customer has already decided not to return. Agentic AI can handle first-line recovery in minutes, not hours.
Loyalty programme disconnected from the actual experience. Points are accruing but the customer does not feel recognised. The CRM holds the history. The frontline system does not read it. The experience feels generic despite significant investment in loyalty infrastructure.
A UK hotel group operating 24 properties had deployed Salesforce Service Cloud for guest services but the integration with their property management system was incomplete. Guest history was not surfacing at check-in, loyalty tier recognition was failing for 38% of eligible guests, and service recovery cases were averaging 4.2 hours to resolve. Aravian built an MCP integration layer connecting Salesforce, the PMS, and the booking engine. Guest recognition accuracy rose to 96%. Service recovery average dropped to under 40 minutes. Repeat booking rate for loyalty members increased 18% in the first quarter.
Non-profit organisations and housing associations share a common constraint: technology investment must be justified against mission impact, not just operational efficiency. The case for AI has to be made in terms that a board of trustees or a housing committee can understand and vote on.
We work with charities, housing associations, and social enterprises deploying AI and digital transformation to improve service delivery, reduce operational cost, or close gaps in their data that prevent them from understanding who they serve and what those people need.
Talk to our Non-Profit teamSalesforce deployed, case management still manual. The CRM is live. The case management workflow that should run from it is still being managed in spreadsheets because the integration between Salesforce and the legacy case management system was never completed.
Data not available in a form that supports funding bids. The organisation collects the right data but it is held in systems that cannot talk to each other. The impact reporting required for major funders takes weeks to produce manually.
Staff AI adoption below target. Microsoft Copilot was rolled out three months ago. Adoption is at 23% of licences. The staff who are using it are using it for basic tasks. The productivity gains the implementation was supposed to deliver have not materialised.
A housing association managing 8,400 properties had a Salesforce implementation that had been live for two years but was not connected to their asset management system. Maintenance requests were being entered into Salesforce manually from a separate system by administrative staff. Aravian designed and built a read-write integration between Salesforce and the asset management system, eliminating the manual data entry step and reducing average time from maintenance request to contractor dispatch from 4.2 days to 1.1 days.
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