We integrate OpenAI, Anthropic Claude, Azure OpenAI, and — where policy or residency requires — EU-hosted or self-hosted models behind your infrastructure. The right choice depends on context length, language quality, tool-calling needs, latency, and whether data may leave UK or EU regions. Discovery workshops map workflows (classification, summarisation, generation, semantic search) and we prototype with realistic prompts before production wiring. If you already have enterprise agreements with Microsoft or AWS, we route traffic through those channels while keeping credentials server-side. Provider changes are managed with versioned prompts and regression tests so upgrades do not silently alter customer-facing behaviour. You retain direct vendor relationships for usage billing unless you ask us to manage keys under your account.
AI integrations for web & business systems
We wire large language models and automation APIs into your website, CRM and internal tools — with keys secured on the server, rate limits and human oversight.
Connect OpenAI, Claude, CRMs and your stack with secure server-side AI integrations.
What you get
- API design & documentation
- Server-side only credentials
- CRM / ERP / webhook sync
- Monitoring and cost controls
Who AI integrations are for
AI integrations from Faraday Web Services are for organisations that want large language models and automation APIs to do real work inside existing websites, portals, and back-office tools — not a novelty widget that answers generic questions and leaks API keys in the browser. Typical clients include B2B service firms, manufacturers, training providers, ecommerce brands, and internal teams that need faster triage, drafting, classification, or search without rebuilding their entire stack.
You are a strong fit when you have a clear process pain: support queues that repeat the same answers, product data that never stays consistent, lead forms that need summarising before sales calls, or knowledge bases that keyword search cannot search well. You are also a strong fit when compliance matters — UK GDPR, EU GDPR, customer contracts, or sector rules that require documented subprocessors, retention limits, and human review before AI output reaches a customer.
If you only need a conversational layer on marketing pages, our AI chatbot integration may be the faster path. If you need models wired into CRM, ERP, warehouses, or custom databases, this service is the umbrella: secure server-side calls, documented endpoints, and maintainable prompts. Many programmes start on a site we built through custom website design; others extend mature WordPress or bespoke PHP applications via API integrations planned alongside AI.
OpenAI, Claude, Azure and provider choice
We integrate the providers your use case actually needs: OpenAI (including function calling and structured outputs), Anthropic Claude for long-context analysis and careful drafting, Azure OpenAI when you already standardise on Microsoft tenancy, and — where residency or policy requires it — EU-hosted endpoints or self-hosted open-source models behind your firewall. Provider choice is never a logo exercise; it is a trade-off between latency, cost per thousand tokens, language quality, tool-use reliability, and where prompts and completions may be logged.
Discovery maps each workflow to a model tier. Lightweight classification on inbound forms might run on a smaller, cheaper model; contract summarisation for legal review might need a larger context window and stricter temperature settings. We version prompts in repository or configuration — not buried in a CMS field someone edits without audit — and we regression-test representative inputs before promoting changes. When you outgrow a vendor, migration is planned: parallel runs, diff review, and rollback paths rather than a silent model swap on a Friday night.
AI integrations sit alongside — not instead of — solid engineering. Rate limits, retries, circuit breakers, and queueing protect your site when an upstream API slows down. Usage dashboards and monthly cost alerts stop surprise invoices. For content-heavy teams, related work often continues in AI-assisted content workflows or AI-driven SEO with human editors still accountable for what publishes.
Function calling, RAG and structured outputs
Modern integrations go beyond “ask the model a question.” We use function calling to extract fields from unstructured text, route tickets, or trigger webhooks only when confidence thresholds pass. Retrieval-augmented generation (RAG) connects models to your approved documents — policies, product sheets, intranet pages — with chunking, embedding, and access control so answers cite sources instead of inventing policy. Structured JSON outputs feed downstream systems without fragile regex on free-form paragraphs. Each pattern is scoped, tested, and documented so your internal team knows what changed when marketing updates a PDF.
When to pair AI with site search
Keyword search fails when buyers describe problems in plain language or when your catalogue uses internal SKU language. Where semantic discovery matters, we implement AI-powered search with indexing strategy, relevance tuning, and fallbacks when the model is unavailable — visitors still get results, not a blank screen. Search and chat share infrastructure discipline: credentials on the server, cached embeddings where sensible, and logging that respects redaction rules.
Security, GDPR and data residency
The most common failure mode in AI projects is treating API keys like front-end configuration. Faraday builds integrations server-side only: keys live in environment variables or secret stores, requests pass through your application layer, and the browser never sees provider credentials. Admin interfaces use role-based access; audit logs record who changed prompts, thresholds, or allow-lists. We align with your existing hosting and security hardening posture — headers, WAF, malware monitoring, and least-privilege accounts — so AI does not become the weakest entry point.
GDPR and UK data protection are discussed before code, not in a footnote after launch. We document what personal data enters prompts (names, emails, order IDs, support transcripts), which subprocessors process it, retention settings on provider accounts, and whether outputs are stored or discarded. For EU-facing programmes we favour EU regions and enterprise agreements with Data Processing Addenda where available; we flag when a use case would send special-category data to a public API without a lawful basis. Data Protection Impact Assessment (DPIA) support means clear data-flow diagrams your DPO can review, not hand-waving about “the cloud.”
Data residency questions are practical: where embeddings are stored, whether fine-tuning is off the table, if customer content may transit US regions, and how deletion requests propagate. We implement minimisation — send the model only what it needs, redact attachments, truncate logs — and configurable retention on your side. Human-in-the-loop gates matter for regulated or brand-sensitive output: drafts for approval, blocked publish until a reviewer clicks confirm, and explicit “AI-generated” labelling where transparency builds trust.
Server-side credentials and abuse prevention
Every public endpoint that triggers model calls is authenticated, rate-limited, and monitored. Anonymous visitors might get a capped number of search or chat turns; authenticated staff get higher quotas tied to their login. Prompt injection and jailbreak attempts are mitigated with input validation, system prompts that refuse credential exfiltration, and separation between user content and instruction layers. Secrets rotate on a schedule you control; staging uses separate keys and separate provider projects so tests never touch production billing or live customer data.
CRM, ecommerce and operations sync
AI becomes valuable when its output lands where teams already work. We sync structured results into HubSpot, Salesforce, Pipedrive, Zoho, Microsoft Dynamics, WooCommerce, Shopify, or custom SQL/NoSQL stores via REST, GraphQL, or webhooks — the same integration discipline we use for non-AI API integrations. Examples include lead scoring narratives appended to CRM timelines, product description drafts queued as unpublished posts, support ticket summaries with suggested categories, and order-exception notes generated from carrier messages — always with field mapping agreed in writing.
Bidirectional flows are scoped explicitly. Reading CRM context into a prompt (“show open opportunities for this email”) requires OAuth, token refresh, and scope minimisation. Writing back requires idempotency keys and conflict rules so a retry does not create duplicate deals. Error handling is user-visible where it matters: staff see “summary unavailable, try again” instead of a silent failure; engineers get structured logs without dumping personal data into Slack.
For ecommerce, AI assists merchandising and support but does not bypass payment or stock truth — inventory and price remain authoritative in the commerce engine. Operations teams often combine AI integrations with AI business automation for multi-step workflows (classify → route → notify → update CRM) while keeping approval steps for high-risk actions.
Webhooks, queues and observability
Heavy workloads run asynchronously: upload triggers a job, the user sees progress, completion posts to CRM when ready. Dead-letter queues capture failures for replay after a provider outage. Dashboards track latency percentiles, token usage by feature flag, and error rates per integration — essential when marketing enables a new “instant draft” button and finance needs to forecast API spend. Runbooks document how to disable a feature quickly without taking the whole site offline.
Use cases we deliver often
Lead and enquiry intelligence: summarise contact forms, extract budget and timeline signals, suggest next actions for sales, and block obvious spam before it hits inboxes. Support acceleration: classify tickets, propose replies from your knowledge base, and surface similar resolved threads — with agents editing before send. Content operations: draft blog outlines, meta descriptions, or product variants from structured attributes, routed into your CMS as drafts. Internal copilots: search across PDFs and wikis with citations for HR, compliance, or engineering — access-controlled by role.
Classification and routing: language detection, sentiment, urgency, or topic tags that drive assignment rules. Document processing: summarise uploads, compare versions, or extract tables into JSON for downstream systems — with size limits and virus scanning on uploads. Personalisation within bounds: recommend content blocks based on behaviour without crossing into creepy or non-compliant profiling. Each use case gets success metrics defined upfront: time saved per ticket, reduction in manual tagging, or conversion on assisted flows — not vanity “we use AI” badges.
We discourage science projects without owners. The best programmes have a product sponsor, labelled training data or exemplar prompts, and a plan for human review. When SEO visibility matters, pair integrations with an SEO audit so AI-generated pages still meet search intent and technical quality; on-page SEO programmes help scale improvements after the integration ships.
Pricing, timeline and how we start
AI integration pricing depends on complexity, not hype. Factors include: number of distinct workflows, providers and models involved, CRM or ERP depth, RAG corpus size, review/approval UI, multilingual output, compliance documentation, and whether you need ongoing prompt tuning. A focused pilot — one workflow, one system, server-side proxy, logging — often lands in a predictable fixed fee after a short discovery. Platform programmes with multiple teams, environments, and governance gates are quoted in phases with written assumptions and explicit out-of-scope items (e.g. legal sign-off, licence costs billed by OpenAI/Anthropic directly to you).
Timelines follow the same pattern as other technical work: discovery and data-flow sign-off, technical specification, build on staging, test with real (redacted) samples, security review, then phased production rollout with feature flags. Rush requests only work when scope is frozen and your stakeholders answer questions within agreed windows. We are transparent about running costs: model usage is usually your direct vendor bill; our fee covers architecture, implementation, documentation, and optional retainers for monitoring and iteration.
To scope your project, use our free quote form or the contact page. We respond within one business day with clarifying questions or a ballpark range. Review our full services catalogue and delivery process if you are comparing AI work with redesign, automation, or search projects in parallel.
Why businesses choose Faraday for AI
Clients choose us when they need senior engineers and strategists who have shipped production websites and integrations since 2018 — not a reseller installing a plugin and disappearing. You work with the people who write the proxy layer, configure retention, and join the call when legal asks where data goes. We are bilingual (English and French), which matters for UK–EU groups standardising AI governance across Manchester, Paris, Brussels, and Geneva without duplicate codebases.
We are opinionated in the right direction: maintainable integrations, honest scopes, and no black-box dependencies you cannot audit. If a use case should not use a public LLM, we say so and propose alternatives. If performance suffers because every page loads a chat bundle, we fix architecture or move features behind intentional entry points — see performance optimisation when third-party scripts pile up.
Evidence, documentation and handover
Deliverables include architecture diagrams, endpoint documentation, environment variable lists, runbooks for disable/rollback, and prompt changelogs. Your team can own day-two operations or retain us for monitoring, cost reviews, and quarterly prompt audits. General agency questions live on our FAQ; company background is on about us. Policies and registration details are on legal information.
Built to extend, not replace, your stack
AI integrations should feel like part of your product roadmap — versioned, tested, measurable — rather than a side experiment on a subdomain. We plan extension points when you later add portals, partner APIs, or mobile apps, so you do not re-pay for the same wiring twice. When you are ready to discuss a pilot or a multi-workflow programme, start with a free quote: we will recommend the smallest proof that still answers your risk and ROI questions.
Frequently asked questions
No — not in our implementations. API keys and service accounts live only on the server (environment variables, secret managers, or your vault). The browser calls your application endpoints; your application calls the model provider with authentication, rate limits, and logging. Public chat or search widgets may use session tokens scoped to your domain, but they never embed provider secrets in JavaScript bundles or CMS fields. Admin screens that edit prompts or thresholds require authenticated staff roles. We review build pipelines so keys do not leak into front-end repositories or client-side source maps. Staging and production use separate credentials. If you inherited a site that already exposes a key, we treat remediation as a priority: rotate the compromised key, proxy traffic server-side, and audit logs for abuse before re-enabling features.
We start with data-flow mapping: what personal data enters prompts, why it is necessary, lawful basis, retention on provider accounts, and subprocessors in your privacy notice. We favour minimisation — redacting attachments, truncating logs, excluding special-category data unless you have explicit grounds — and EU/UK regions where providers offer them. Enterprise DPAs and zero-retention settings are configured when available. Human review gates apply before AI text reaches customers or public pages. We supply diagrams and configuration notes suitable for DPIA review with your DPO or counsel; we do not provide legal advice, but we implement technical measures you specify. Deletion and subject-access requests are planned: which logs hold prompts, how long embeddings persist, and how to purge a user from RAG indexes. Cross-border transfers are flagged early so you can choose Azure EU, Anthropic policies, or on-prem alternatives rather than discovering issues at launch.
Yes. We map structured outputs into HubSpot, Salesforce, Pipedrive, Zoho, Dynamics, WooCommerce, Shopify, or custom databases using the same disciplined approach as our API integration work: field mapping documents, OAuth with minimal scopes, idempotent writes, and clear error messages for staff. Examples include lead summaries on contact records, suggested ticket categories, product description drafts as unpublished posts, and order notes from carrier emails. Read access into CRM for contextual prompts is scoped separately from write-back automations. We respect GDPR retention in CRM as well as in AI logs — deleting a contact should cascade where you require it. High-risk writes (creating deals, changing prices) stay behind approval rules or human confirmation. Bidirectional sync is quoted explicitly because conflict resolution and token refresh add complexity beyond a one-way webhook.
Cost splits into implementation and running usage. Implementation is quoted after discovery based on workflow count, systems touched, RAG size, compliance documentation, and UI for review. A single-workflow pilot with server-side proxy, logging, and one CRM or CMS target is often a fixed fee in the mid four figures GBP/EUR excluding VAT, depending on access and test data readiness; multi-workflow platforms are phased higher. Running usage is usually billed directly by OpenAI, Anthropic, or Azure to your account — we help forecast tokens and set limits. Optional retainers cover monitoring, prompt tuning, and cost reviews. Rush fees apply only when scope is fixed and capacity allows without skipping security testing. Proposals list assumptions, out-of-scope items, and who supplies legal sign-off. Request a tailored estimate via our quote form; we reply within one business day with questions or a ballpark.
Plugins optimise for quick install, not governance. They often embed keys insecurely, send full page context to third parties, offer limited CRM mapping, and break when themes update. Faraday integrations are custom to your processes: server-side proxies, versioned prompts, role-based admin, observability, and documented rollback. Chat may be one surface, but the same backend can power form summarisation, internal search, and automation without duplicating logic. You own the architecture — no opaque vendor dashboard holding your training data hostage. Plugins can be fine for experiments; production programmes with customer data, brand risk, or CRM dependency need engineering discipline. We can still deliver conversational UI through our chatbot service when that is the primary need, sharing infrastructure with broader integrations when you expand later.
Usually no. We integrate with existing WordPress, bespoke PHP, Laravel, Node, or static sites by adding server-side routes, webhooks, or microservices behind your domain. A redesign helps when performance, information architecture, or consent management are already blockers — otherwise we avoid rebuilding just to add a model call. Greenfield sites benefit from planning AI extension points during design so credentials, queues, and admin UI are native, not bolted on. We audit your current hosting, PHP/runtime versions, and plugin conflicts before estimating. If legacy code cannot safely hold secrets, we propose a thin API layer on modern infrastructure while the public site remains unchanged. Security hardening may be recommended in parallel when the site already shows compromise indicators or outdated access control.
Launch includes feature flags, monitoring dashboards, runbooks for disable/rollback, and a short hypercare window for defect fixes. We hand over documentation: architecture diagrams, environment variables, prompt changelog process, and CRM field mapping. You choose whether your team operates day-two or we retain for monthly reviews of cost, latency, and prompt drift. Model providers change behaviour over time; scheduled regression tests catch quality slips before customers do. New workflows should repeat discovery — not copy-paste production keys into another plugin. We can expand into business automation, semantic search, or content pipelines when pilots prove ROI. Maintenance plans may include dependency updates, secret rotation, and incident support aligned with your hosting retainer. AI is not fire-and-forget; measured iteration usually beats a one-off install followed by silent failure when APIs or policies change.
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