Platform Expertise
AI Platforms — OpenAI, Claude, Gemini & More
There is no single AI model that solves everything. OpenAI, Anthropic, Google, and Mistral each serve different purposes, carry different cost structures, and suit different data requirements. Selecting the right model matters. Integrating it into a production system so it compounds value over time matters more.
EXPRE connects leading AI platforms to client websites, marketing infrastructure, and operational systems. We work with GPT-4o, Claude, Gemini, and Mistral, choosing based on the task, data sensitivity, and commercial case rather than brand preference.
The Platforms We Work With
OpenAI GPT-4o
The strongest general-purpose model for content generation, analysis, summarisation, and customer-facing applications. Its multimodal capability across text, image, and voice makes it versatile where interfaces need to handle multiple input types. The default choice for most content and conversational applications.
Anthropic Claude
Delivers precise performance on tasks requiring careful reasoning, long document analysis, and constrained generation. Its approach to content safety and instruction-following makes it well suited to applications where output reliability is a commercial requirement. EXPRE uses Claude internally for our own Webgentic platform.
Google Gemini
Integrates naturally into Google Workspace environments and performs strongly where data infrastructure already runs on Google Cloud. For those businesses, the implementation path is shorter and the operational friction lower.
Mistral (Self-Hosted)
Open-weight models that can be self-hosted. This matters when sensitive data must not leave your own infrastructure. For organisations with strict data residency requirements, Mistral removes the third-party dependency entirely. EXPRE designs and deploys self-hosted Mistral deployments where the regulatory or commercial case requires it.
Common Integration Use Cases
Website Assistants
AI assistants that understand natural language, draw on your knowledge base, and handle customer queries consistently. Unlike rule-based chatbots, they manage novel questions and escalate appropriately when they reach their limits.
AI-Powered Search
Search that understands query intent rather than matching keywords. Visitors using natural language find relevant results even when their terminology differs from your product descriptions or content.
Content Generation Workflows
Automated content generation integrated into CMS workflows. AI drafts product descriptions, email subjects, meta descriptions, and social posts from structured data inputs, with human review before publication.
Customer Service Automation
AI drafts responses for agent review, categorises incoming messages, and routes them to the correct team. Response times drop. Agent cognitive load drops with them.
Data Analysis and Reporting
AI processes structured data, produces natural language summaries, identifies trends, and flags anomalies that rules-based reporting would miss. Insight without the manual data-pulling exercise.
Document Processing
Long document analysis, summarisation, classification, and data extraction at scale. Particularly relevant for professional services, insurance, and financial services organisations handling large volumes of documents.
Selecting the Right Model
The right model depends on four factors: task type, data sensitivity, request volume, and quality threshold. No single model leads across all four.
EXPRE runs a structured selection process for each integration. We define the use case precisely, benchmark relevant models against it, and select based on performance, cost, and data requirements. Many projects use multiple models: a larger model handles complex reasoning, a smaller cheaper model handles high-volume routine work.
Getting this architecture right at the outset prevents cost overruns as usage scales.
Data Security and Compliance
Sending personal data to third-party AI APIs requires a data processing agreement and an appropriate lawful basis under GDPR. EXPRE assesses these requirements for every project and implements the controls: data minimisation in prompts, enterprise API tiers with stronger data handling commitments, and clear audit trails.
For applications with strict data sensitivity requirements, self-hosted open-weight models provide AI capability without data leaving your infrastructure. EXPRE designs and deploys these where the commercial or regulatory case requires it.
Common Questions
Which AI model is best?
There is no universally best model. GPT-4o leads on general-purpose tasks. Claude excels at careful reasoning and long documents. Gemini suits Google-ecosystem integrations. Mistral suits self-hosted deployments. We select based on your specific use case.
How much does an AI integration cost?
A standard AI chatbot integration typically starts from 8,000 pounds. Complex multi-model integrations with custom data pipelines are scoped individually. Ongoing API costs depend on usage volume and the models selected.
Can we switch AI providers after going live?
With the right architecture, yes. A well-designed integration abstracts the AI provider, making it possible to switch models without rebuilding the entire system. We design for this flexibility from the outset.
Is it safe to use AI APIs with customer data?
With appropriate controls, yes. Enterprise API tiers, data processing agreements, data minimisation in prompts, and accurate privacy notices describing AI processing are the baseline requirements.
How do we control the quality and tone of AI outputs?
Through prompt engineering — the instructions the AI receives that define how it should respond. EXPRE designs prompt architectures that enforce tone, format, factual constraints, and escalation behaviour consistently.
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