AI Integration Services for UK Organisations

Integrate AI Into the Systems and Workflows Your Business Already Uses

Embed generative AI, LLMs, intelligent automation and machine intelligence into your existing applications, data and operations—without creating another disconnected tool.

CRM, ERP, Microsoft 365 and application integration
AI-enabled workflows with human approval where needed
Secure data access, APIs and production controls
Evaluation, monitoring and continuous optimisation

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19+ Yearsof industry experience

750+satisfied customers

200+technology professionals

83%repeat and referral business

Trusted by leading organisations

Move Beyond Isolated AI Tools

AI integration turns useful intelligence into part of the way work actually happens

A model demonstration is not an operational capability. Real integration has to work with identity, live data, APIs, approvals, failure modes, user journeys and the systems that already run your organisation.

Disconnected AI tools

Teams copy information between AI interfaces and business applications because intelligence has not been embedded into the process.

Untrusted data context

Stale content, inconsistent records and weak access control can undermine the quality and safety of AI-assisted work.

Prototype-only integration

A single model call does not provide retries, validation, auditability, fallback behaviour or operational ownership.

Uncontrolled actions

AI should not update systems or trigger consequential activity without clear permissions, rules, approval boundaries and monitoring.

AI Integration Services

Connect AI capabilities with applications, data and business operations

IDS Logic combines AI engineering with APIs, enterprise integration, application development, testing and support so the new capability becomes part of a maintainable technology estate.

AI Integration Discovery

Map the business outcome, users, systems, data and operational constraints before selecting a model or provider.

Use-case and workflow assessment
System and data mapping
Integration roadmap

Generative AI & LLM Integration

Embed generation, summarisation, extraction, classification and intelligent retrieval into existing applications.

Model/API integration
Prompt and context management
Structured outputs and validation

AI-Ready Data Integration

Connect structured and unstructured information with appropriate freshness, permissions and source context.

Data pipelines and transformation
RAG and enterprise retrieval
Access-aware data flows

AI Workflow Integration

Introduce AI only where a workflow genuinely needs interpretation, extraction, generation or contextual decision support.

AI-assisted decisions
Human approval gates
Exception and fallback paths

CRM & ERP AI Integration

Bring AI into sales, service, finance and operational systems without creating another disconnected interface.

CRM and account workflows
ERP and finance processes
Controlled record actions

Microsoft 365 & SharePoint AI

Connect approved enterprise knowledge and collaboration processes with permission-aware AI experiences.

SharePoint knowledge retrieval
Microsoft 365 integration
Document and workflow intelligence

Legacy & API Enablement

Create integration layers where established applications lack modern APIs or reliable data contracts.

Middleware and API wrappers
Events, queues and webhooks
Phased modernisation

Evaluation & Production Operations

Measure quality, integration behaviour, cost and failure modes as models, prompts and systems change.

Representative evaluations
Observability and cost monitoring
Controlled optimisation

AI Integration Support

Maintain the complete service boundary around models, APIs, retrieval, permissions, workflows and users after launch.

Incident investigation
Dependency and provider changes
Improvement backlog

Connected Enterprise AI

Integrate intelligence where your teams already work

The value comes from the connections around the model: trusted context in, controlled actions out, and clear ownership throughout.

Business applicationsCRM, ERP, portals, ecommerce, CMS and bespoke software

Knowledge and dataDatabases, documents, SharePoint, APIs and data platforms

AI capabilityLLMs, retrieval, extraction, classification, predictions and agents

Operational controlPermissions, approvals, logs, evaluation, monitoring and support

Systems We Integrate AI With

Add AI without discarding the technology that already carries your business processes

We design around available APIs, authentication, data ownership, process risk and long-term maintainability rather than forcing every organisation into the same AI stack.

CRMSalesforce, Dynamics, HubSpot and bespoke customer platforms.

ERP & FinanceOperational, finance, procurement, product and fulfilment systems.

Microsoft 365 & SharePointDocuments, knowledge, collaboration and controlled business workflows.

Ecommerce & CMSSearch, service, content, catalogue and customer-experience integrations.

Customer & Employee PortalsEmbedded assistants, recommendations, knowledge and workflow support.

APIs & Data PlatformsDatabases, warehouses, APIs, middleware, document stores and event streams.

AI Workflow Automation

Use AI where the workflow needs judgement, interpretation or unstructured information

Deterministic routing, approvals and system actions remain conventional workflow automation. We add AI when it has a specific job that rules alone cannot handle cleanly.

AI-powered decision support

Analyse context, summarise evidence or create recommendations before a defined business decision.

Intelligent approvals

Extract and classify information before routing cases to the correct approver with relevant context.

Document & data extraction

Interpret invoices, forms, emails and documents, then pass validated structured data into downstream systems.

Explore wider process automation →

Customer service workflows

Classify requests, retrieve approved information, draft responses and escalate cases according to defined rules.

Explore AI chatbot development →

Finance workflows

Support document interpretation, coding suggestions, exception triage and review while preserving human control over consequential actions.

CRM automation

Summarise activity, classify enquiries, enrich context or support next-action recommendations inside existing customer workflows.

Explore CRM integration →

AI Business Process Automation

Combine AI with deterministic process controls rather than asking AI to run the whole operation

Business process automation can span people, policies, systems and several workflows. AI becomes one controlled capability within that wider process when interpretation or context adds value.

Rules stay explicit

Known business rules, thresholds, approvals and compliance controls remain deterministic wherever possible.

AI handles ambiguity

Unstructured text, documents, classifications, summaries and recommendations can be delegated to an AI component.

People keep authority

Human approval is inserted where decisions carry material financial, customer, safety or compliance consequences.

Systems record the outcome

Approved actions flow back into CRM, ERP, workflow or operational platforms with logs and ownership.

If the requirement is primarily deterministic routing and approvals, see our workflow automation services. For broader end-to-end transformation across several workflows, systems and teams, explore business process automation.

Generative AI & LLM Integration

Embed generative capability behind controlled interfaces, context and business rules

A production generative AI integration needs more than a prompt. We engineer retrieval, identity, context, output contracts, validation and operational controls around the model.

RAG

Grounded knowledge experiences

Retrieve approved organisational content and pass relevant context to the model with source-aware access controls.

TXT

Summarisation & drafting

Create controlled drafts or summaries inside CRM, service, document and operational applications.

DOC

Document intelligence

Extract, classify and structure information from forms, invoices, contracts, emails and knowledge content.

SRC

Intelligent search

Improve discovery across documents, portals, products and internal knowledge using retrieval and semantic techniques.

Where the requirement is to build a new bespoke AI product rather than embed AI into an existing estate, see our AI software development services and broader AI development services.

AI Integration Architecture

Treat the model as one controlled layer in a complete business system

A dependable integration connects business context and data to AI, then constrains how recommendations or actions flow back into live systems.

Business SystemCRM, ERP, app or workflow

Data / IntegrationAPI, retrieval, events, identity

AI / LLMModel, prompt, classifier, prediction

Business RulesValidation, thresholds, permissions

Human ApprovalReview where risk requires it

ActionUpdate, route, notify or respond

MonitoringQuality, failures, latency, cost

AI-Ready Data Integration

Give AI the right information without giving it indiscriminate access

Quality and security depend on the data boundary. We define what information is authoritative, how it is retrieved, who can access it and what happens when the source is stale or unavailable.

Source of truth

Identify authoritative systems and avoid duplicating business-critical information into unmanaged AI stores.

Freshness & lineage

Track where information came from, when it changed and whether the AI context is still current.

Permission-aware retrieval

Preserve user, role and source-system access boundaries when retrieving enterprise knowledge.

Data contracts & validation

Validate structured data before it enters or leaves an AI step and define how invalid outputs are handled.

Where direct system connectivity is the main requirement, our API integration and development services, CRM integration and ERP integration provide the underlying integration capability.

Human-in-the-Loop AI Workflows

Keep human authority at the decision points that matter

Automation should reflect the consequence of the action. Some outputs can proceed automatically; others should pause for review, correction or explicit approval.

Recommend

AI proposes a classification, draft, decision or next action without executing it.

Review

A person sees the source context and AI output before accepting, correcting or rejecting it.

Approve

High-impact updates or communications require explicit approval before downstream systems are changed.

Learn operationally

Review outcomes become evidence for evaluation, prompt changes, workflow refinement and future release decisions.

AI Security, Governance & Access Control

Production AI needs explicit boundaries around data, identity, actions and change

Controls are designed around the connected systems and use case rather than added as a generic checklist after the AI feature is built.

Identity & least privilege

Users, service identities and AI tools receive only the permissions needed for their approved role.

Input & output controls

Validate data, constrain formats and route unsafe or invalid outputs into defined exception paths.

Auditability

Record relevant requests, model or workflow versions, decisions, approvals and resulting system actions.

Change control

Test model, prompt, retrieval and integration changes against representative scenarios before production release.

AI Agents & Workflow Orchestration

Use agentic behaviour only when the process genuinely needs controlled tool use and multi-step autonomy

Not every integration needs an agent. We distinguish embedded AI capabilities from agentic systems so the level of autonomy matches the business risk and operating need.

Embedded AI

A bounded capability performs one job—such as classification, extraction or summarisation—inside an existing workflow.

Tool-using AI

The AI can call approved APIs or functions to retrieve context or prepare a controlled action.

Agentic workflows

Where justified, an agent can plan and coordinate several approved steps under permissions, guardrails, evaluation and human oversight.

Explore AI agent development →

AI Integration Use Cases

Practical integration opportunities across customer, knowledge and operational systems

The strongest use cases have a defined user, usable data, an existing system boundary and a measurable business decision or workflow to improve.

Customer service

Classify enquiries, retrieve approved knowledge, draft responses and escalate complex cases.

Sales & CRM

Summarise activity, enrich account context, classify leads and support next-step recommendations.

Finance & documents

Extract information, prepare coding suggestions and route exceptions into controlled review workflows.

Enterprise knowledge

Provide role-aware retrieval and summarisation across SharePoint, documents and internal repositories.

Operations

Interpret exceptions, consolidate context and support operational teams through existing ERP or service systems.

Ecommerce & digital service

Improve search, catalogue discovery, service assistance and content workflows within customer-facing platforms.

Employee services

Help teams find policies, summarise cases and guide approved internal processes without bypassing HR controls.

IT & service operations

Classify incidents, summarise technical context and support triage while leaving system actions behind explicit controls.

Our AI Integration Process

Move from a valuable use case to a controlled production capability

The sequence is iterative, but every stage creates evidence before the integration becomes more autonomous, connected or widely adopted.

Discover

Define the user, workflow, business outcome, baseline, data and system boundaries.

Architect

Select integration, data, identity, model, retrieval, approval and observability patterns.

Validate

Test the highest-risk assumptions with representative data and real integration behaviour.

Engineer & Assure

Build the service, APIs, controls, evaluations, user experience and deployment process.

Launch & Optimise

Release in controlled stages, monitor quality and improve prompts, data, workflows and integrations.

Why IDS Logic

AI integration backed by application, API, enterprise-platform and support expertise

The difficult part of enterprise AI is often everything around the model. IDS Logic brings the multidisciplinary capability needed to connect, assure and operate the complete service.

Integration-led discovery

We start with the workflow, systems, data and accountable outcome rather than a preferred AI product.

Software engineering depth

AI capabilities are delivered as maintainable application components with APIs, testing and operational ownership.

Enterprise-system experience

Our wider teams work across CRM, ERP, SharePoint, Microsoft 365, CMS, ecommerce and bespoke software.

Evidence before scale

Representative evaluations, failure handling, human review and release gates support controlled adoption.

Vendor-aware architecture

Interfaces and ownership are designed so the integration can evolve as models, providers and requirements change.

Long-term support

IDS Logic can remain involved as business rules, source systems, data and AI dependencies evolve after launch.

Relevant Integration & Automation Experience

Evidence across APIs, enterprise platforms and connected operational workflows

These published IDS Logic projects demonstrate integration, workflow and production-delivery foundations relevant to AI integration. They are not presented as bespoke AI integration case studies.

Multi-System Integration

RIAT

A secure middleware service connected ticketing and payment platforms while preserving the customer journey and coordinating several technology partners.

REST API integration
Secure callbacks and refunds
Multi-system transaction flow
Production testing and launch support

Learn more

CRM & Portal Integration

ICA

A website transformation that gave ICA flexible WordPress publishing while maintaining reliable Salesforce integration and alignment with its wider portal ecosystem.

Salesforce integration
Connected digital ecosystem
CMS modernisation
Controlled migration

Learn more

Workflow & Information Integration

Hessington Health

Connected SharePoint and Power Automate workflows improved process management and information control for a growing healthcare-services provider.

Process discovery
SharePoint integration
Power Automate workflows
Ongoing enhancement

Learn more

Words From Clients

Hessington Health is a national health screening and occupational health service provider. We have been scaling up our business activity aggressively over the past 12 months and our workflow/IT needs have changed significantly as we have grown. We instructed IDSLogic for their passion to support business in helping them to create efficient processes, which in the long term save time and money. Thery have start with getting a very granular understanding of the clients needs and then offer several solutions. They have helped us integrate Power Automate processes in our SharePoint system. They date flow works flawlessly within SharePoint which helps achieve grater data security, and process management. We have now instructed them to develop our Patient App. I can not recommend them enough.

Dr Harjeev Rai

CEO/Medical Director Hessington Health

IDS has become a true strategic development partner for all our digital work. We have found their technical expertise a perfect complement to our in-house creative and digital team and IDS has been highly dedicated to helping us meet our goals for growth. The Emma’s Diary channel continues to grow and our plans remain ambitious. We are pleased to have IDS Logic’s trusted support on our journey.

Gareth Huxtable

Director, Lifecycle Marketing

This was a very complex and multi-layered project with ambitious targets. IDS helped us define our requirements and made a real contribution to project delivery, demonstrating their development experience on major projects.

Chris Manko

Technical Solutions Architect, Lifecycle Marketing

The new site looks excellent. I’m very pleased with the results and with the quick responses during testing and UAT.

The LensPal

USA

IDS Logic has proved their expertise in timely project delivery and this helped us to a successful on-time launch. Our new website can now truly support our evolving business strategy to remain at the forefront of our sector.

Pickfords

We came to IDS Logic with a vision for our site. The team listened, understood our requirements and produced an attractive and functional website that led to positive results. It is really a great pleasure to work with them.

LFRA

I am really happy to have IDS Logic on board and have been very impressed with their speed of implementation and professional approach to their work. This has made our collaboration an enjoyable and extremely valuable partnership.

Pebble Grey

Connect AI to a Real Business Outcome

Turn an isolated AI idea into a connected, supportable capability

Share the system, data source or workflow you want to improve. We will help clarify the AI boundary, integration requirements, human controls and a sensible route to production.

Call our UK team
+44 (0)1135 316 314

Email IDS Logic
[email protected]

Frequently Asked Questions

AI integration questions, answered

For advice based on your systems, data, workflow and governance requirements, speak directly with our AI integration team.

Talk to an AI Integration Specialist

What are AI integration services?

AI integration services connect artificial intelligence capabilities with the applications, data and workflows an organisation already uses. This can include generative AI, large language models, machine learning, document intelligence or AI agents integrated with CRM, ERP, Microsoft 365, SharePoint, ecommerce, portals, APIs, databases and bespoke software.

How is AI integration different from AI software development?

AI integration focuses on adding AI capability to an existing technology estate or workflow. AI software development is a broader discipline for creating bespoke AI-enabled applications or products. An integration project may add summarisation to a CRM workflow, for example, while an AI software project could create an entirely new application with its own user experience, data model and operating model.

What is AI workflow automation?

AI workflow automation is appropriate when an automated workflow needs intelligence to interpret unstructured information, classify content, extract data, generate a draft, make a recommendation or support a context-dependent decision. Deterministic routing, approvals and system actions should remain conventional workflow automation where AI is not needed.

Can IDS Logic integrate generative AI and LLMs into existing applications?

Yes. We can integrate generative AI and large language models into existing applications through APIs, middleware, retrieval layers and secure application services. The implementation can include prompt and context management, structured outputs, validation, permissions, fallbacks, monitoring and human review based on the use case.

Which business systems can be connected with AI?

AI can be integrated with CRM, ERP, Microsoft 365, SharePoint, Salesforce, ecommerce platforms, CMS platforms, customer portals, data platforms, document repositories, service-management tools, databases, APIs and bespoke web or mobile applications. The integration approach depends on available interfaces, authentication, data quality and operational constraints.

Do we need an AI agent for an AI integration project?

Not necessarily. Many valuable AI integrations are simpler embedded capabilities such as document extraction, classification, knowledge retrieval, summarisation or recommendations. An AI agent is more appropriate when a use case requires controlled tool use, multi-step reasoning or a degree of autonomy across approved systems. IDS Logic treats AI agent development as a separate specialist discipline where that level of autonomy is justified.

What is human-in-the-loop AI?

Human-in-the-loop AI introduces a defined point where a person reviews, approves, corrects or escalates an AI-generated recommendation or action. It is useful where the decision carries material business, customer, financial, safety or compliance consequences, or where AI confidence and operating conditions require additional oversight.

How do you prepare business data for AI integration?

We assess source systems, ownership, data quality, freshness, access permissions, metadata and retrieval requirements before connecting AI. Depending on the use case, preparation may include API integration, data transformation, document indexing, retrieval-augmented generation, access filtering, data contracts and monitoring for stale or failed data flows.

How do you secure enterprise AI integration?

Security can include role-based access, least-privilege service identities, secure secret handling, data minimisation, source-level permissions, environment separation, audit logging, input and output validation, human approval gates and controlled deployment. The exact controls depend on the connected systems, information sensitivity and regulatory context.

How do you make AI integrations reliable in production?

Production reliability requires more than a model API call. We design timeouts, retries, fallbacks, queues where appropriate, structured validation, exception handling, logging, monitoring, quality evaluations and operational support routes. Model, prompt, retrieval and integration changes should be versioned and tested before controlled release.

Can IDS Logic integrate AI with legacy systems?

Yes. We first assess whether the legacy platform has usable APIs, database access, events or export interfaces. Where modern interfaces are limited, a custom integration layer, middleware service or phased modernisation pattern can isolate the AI capability from fragile legacy dependencies while keeping ownership and failure handling clear.

How long does an AI integration project take?

Timescales depend on the number and condition of connected systems, data readiness, security requirements, user experience, model or provider complexity, evaluation needs and release controls. A focused discovery or integration pilot can be relatively short, while multi-system enterprise AI integration is normally delivered in controlled phases.