AI Integration Services

Integrate AI into your existing apps, portals, systems and workflows. Scorchsoft designs and builds production-ready AI integrations that connect modern AI models with the software and business data your organisation already relies on.

AI Integration Services | Scorchsoft
“The value of AI is often in the integration. A capable model matters far less than whether it can reach the right information and act inside the software your business already runs on.”
— The Scorchsoft approach to AI
Intelligent search, summarisation and document-extraction features layered into an existing application

What AI integration means

AI integration is the work of adding useful AI capabilities to the software your organisation already uses, rather than buying a separate AI product and hoping staff switch to it. The aim is for AI to appear where work already happens: inside your portal, your operational software, your CRM screens or your mobile app.

That is fundamentally a software engineering and systems integration problem. Connecting to an AI model is the straightforward part. The real work is giving that model controlled access to the right data, wiring its outputs back into your systems, handling permissions and edge cases, and making the whole thing reliable enough to run in production.

Practical adoption, not experimentation

This page is for organisations that already have software and business systems and want to add genuinely useful AI to them, safely and pragmatically, rather than run another proof of concept that never reaches daily use.

Where AI can fit into existing software

We add AI into the systems you already run, and choose the capability that fits the job rather than applying AI for its own sake.

Where it goes

Systems we integrate AI into

  • Bespoke web applications
  • Customer & employee portals
  • Mobile applications
  • SaaS platforms
  • CRM systems
  • ERP systems
  • Operational software
  • Document repositories
  • Databases & data lakes
  • Reporting systems
  • APIs & internal workflows
  • Third-party platforms
What it does

Capabilities we add

  • Intelligent search
  • Natural-language interfaces
  • Document analysis
  • Extraction & classification
  • Summarisation
  • Drafting
  • Recommendations
  • Conversational interfaces
  • Decision support
  • Automated data processing
  • Tool use & API calls
  • Document & image understanding

What AI integration can look like

Five concrete patterns, all built on software your organisation already uses.

Customer portal

An assistant inside your portal

A portal already holds account data, documents and operational information. We add an AI assistant that understands authorised portal data, answers questions, finds documents and performs approved actions.

Operational software

One answer across many systems

Staff currently search several systems by hand. An AI interface retrieves information from the relevant APIs or databases and presents a single useful answer, without navigating each system separately.

Document workflow

Read, classify, extract

A team inspects incoming documents or forms manually. AI classifies them, extracts structured information, flags exceptions and passes the resulting data straight into existing software.

CRM / ERP

Summaries and prompted actions

An AI capability summarises account activity, identifies records needing attention, prepares updates or triggers authorised processes, with a person confirming anything sensitive.

Existing SaaS

AI features in your own product

We add AI features directly into an existing SaaS product, so users get them in the tool they already pay for rather than moving to a separate AI application.

Something closer to your case?

These are patterns, not a menu. Most integrations combine a few of them around a specific business process. Tell us the workflow and we will map the fit.

An AI request passing through a controlled application service to authorised databases, a search index, a vector store and a data lake

Connecting AI to your business data

Most useful AI integrations need controlled access to business information. How that access is provided depends on the requirement, and part of our job is choosing the right approach rather than defaulting to a fashionable one.

Depending on the case we might use direct API access, structured database queries, search and indexing, a vector database, retrieval-augmented generation, a data lake, application services, Model Context Protocol, or a combination. The architecture follows the business need.

RAG in plain terms

Retrieval-augmented generation, or RAG, means the AI retrieves relevant information from your own sources at the moment it answers, so its response is grounded in your data rather than its training. It is a valuable technique, but not every integration needs it, and not every integration needs a vector database.

AI integration architecture

A useful way to picture an integration: AI is added into your existing business architecture, mediated by an integration and application layer that keeps permissions and business rules in deterministic code.

Existing systems & software

CRMERPDatabasesDocumentsPortalSaaSAPIs

Scorchsoft integration / application layer

APIsBusiness logicPermissionsSearchData services

AI capabilities

SearchSummarisationExtractionCopilotAgentsAutomation

Users & applications

StaffCustomersExisting appsPortals

The integration layer is where the engineering value sits. It holds the permissions, business logic and data services that every AI capability depends on, and the same layer can serve your portals, apps and automation too.

AI is only as good as the data it can reach

AI quality often depends on how accessible, structured and reliable the underlying information is. Where your data is fragmented, the highest-value first step may be to improve the foundations.

Data Ecosystem Development

Where useful information is scattered across many systems, connecting them into a coherent, AI-ready ecosystem is often the groundwork that makes AI genuinely useful.

Data Lake Development

Where durable historical or analytical data matters, a data lake gives AI a reliable source for trends, investigations and reporting. Not every integration needs one.

API & Systems Integration

Clean, well-designed APIs and integrations are what let AI reach your systems safely and be reused across future projects.

One application hub connected to several interchangeable model providers, weighed against cost, latency and security

AI APIs and model providers

We are pragmatic about models rather than tied to one provider. Depending on the project we work with OpenAI, Azure OpenAI, Anthropic, Google AI and other suitable providers, and we design the integration so the model can be changed as needs and options evolve.

Model choice depends on the job

The right model for a task depends on capability, cost, latency, security requirements, data residency, context needs, reliability and the environment it has to run in. A summarisation feature and a document-extraction pipeline may sensibly use different models. We choose on the merits for each case rather than picking a default and forcing every use onto it.

AI capabilities and multi-step automation built on the same integration layer

AI agents and workflow automation

AI integration is the broad capability. Many valuable integrations simply add intelligent search, summarisation or document processing to software you already use, with no autonomous behaviour at all.

Where AI needs to carry out multi-step work or take actions across several systems, that moves into agent territory. Our AI Agents & Automation capability covers processes that plan and act across connected systems, under proper governance. An integration and an agent often share the same foundations, which is why we build them to be reused.

MCP as an integration option

Model Context Protocol, or MCP, is one way to connect AI clients and agents to business systems. It provides a standard interface through which compatible AI applications can discover and use approved business tools and information.

MCP is useful when you want tools such as ChatGPT or an AI agent to reach a deliberately chosen set of your capabilities. It is one option among several, and it usually sits on top of the same APIs and services an integration already needs. We cover it in depth on our MCP Integration Services page.

Security and governance by design

Security is an architectural decision from the start, not something added at the end. An AI integration is a production application interface and is built like one.

Authentication & authorisation

Every request is authenticated, and users reach only the information and actions appropriate to them.

Role-based permissions

Where it fits, AI access follows the same roles your application already uses.

Read / write separation

Retrieving information is kept separate from actions that change data, and write access is granted deliberately.

Validation & audit

Inputs and AI-suggested actions are validated by deterministic code, and important operations are logged.

Data-processing controls

We consider where data goes, provider data-processing terms, data residency and the handling of sensitive information.

Rate limiting & controls

Endpoints carry the operational controls expected of any production service.

Automation does not have to mean autonomy. For important actions, and especially where AI can create, update or delete business records, the system can require explicit human approval before any change is made. You decide where a person stays in the loop.

How we approach an AI integration

How we tend to work, not a rigid process. Most projects start small and prove value before extending.

Find the valuable use case

Start from a real workflow where AI would save effort or reduce errors, not from the technology.

Map the systems and data

Identify what information and which systems the AI needs to reach, and how it should act on them.

Design the integration layer

Decide the access approach, permissions and business logic, reusing existing APIs where they are suitable.

Build and connect

Implement the capability, wire outputs back into your software, and choose the appropriate model for the task.

Govern and validate

Add authentication, permissions, validation, human approval where needed, logging and operational controls.

Prove, then extend

Ship the first use case into real use, learn from it, and reuse the foundations for the next.

Scorchsoft working on both the AI layer and the software and data behind it

Why Scorchsoft is suited to this

AI integration touches two sides at once: the AI and model layer, and the software, data and systems the AI has to interact with. We work on both.

The same team brings AI engineering together with bespoke software development, web portals, mobile apps, system and API integration, database design, data infrastructure, workflow automation and a genuine grasp of the business process involved. That combination is what turns a promising demo into an integration that runs reliably in production and keeps earning its place.

Related Scorchsoft capabilities

AI integration connects to the wider set of capabilities that make it work in practice.

Data Ecosystem Development

Connect fragmented systems into an AI-ready foundation.

Data Lake Development

Durable historical and analytical data for AI and reporting.

API & Systems Integration

The interfaces that let AI reach your systems safely.

Portal & Web App Development

The portals and applications AI features live inside.

AI Agents & Automation

Multi-step AI that acts across connected systems.

MCP Integration

Give ChatGPT and other AI clients controlled access via MCP.

Frequently Asked AI Consultancy Questions

What is AI integration?

AI integration is adding useful AI capabilities into the software and systems your organisation already uses, and connecting AI to the data it needs, so it works inside your existing tools rather than as a separate product. It is mostly a software engineering and systems integration task.

Can you add AI to software we already use?

Yes. That is the focus of this capability. We add AI features into bespoke applications, portals, mobile apps, SaaS platforms and operational software you already run, working through their APIs, databases or interfaces.

Can AI connect to our CRM or ERP?

Yes, through a controlled integration. AI can read information from your CRM or ERP and, where you allow it, prepare updates or trigger authorised processes, subject to permissions and validation.

Can you integrate ChatGPT into our customer portal?

Yes. We can add an assistant into your portal that understands authorised portal data, answers questions and performs approved actions. Depending on the case this may use a direct integration or an MCP interface.

Do we need to move our data into a new system?

Usually not. AI can often work with data where it already lives, through APIs and application services. We recommend moving or consolidating data only where it clearly helps.

Do we need a data lake before using AI?

No. A data lake is valuable for durable historical or analytical data, but many integrations work well against live systems. We suggest one only where it earns its place.

What is RAG?

Retrieval-augmented generation means the AI retrieves relevant information from your own sources at the time it answers, so responses are grounded in your data rather than the model's training. It is useful but not required for every project.

Can AI update our existing systems?

Yes, when you want it to. Write access is granted deliberately and treated more carefully than read access. Sensitive changes can require human approval, and inputs are validated by application code before anything is written.

Can actions require human approval?

Yes. Automation does not have to mean autonomy. Important actions can require a person to confirm them before any change is made, which is common where AI can create, update or delete records.

Which AI models can you integrate?

We work with providers including OpenAI, Azure OpenAI, Anthropic and Google AI, among others, and choose based on capability, cost, latency, security, data residency and reliability. We build integrations so the model can be changed as needs evolve.

Can you work with software another developer built?

Yes. We regularly integrate with systems built or maintained by other suppliers, working through their APIs or supported interfaces, and can collaborate with your existing partners where sensible.

What is the difference between AI integration and an AI agent?

AI integration is the broad capability of adding AI into your software. An AI agent is a narrower case where AI performs multi-step work or takes actions across systems. Many integrations need no agent at all.

Can you integrate AI using MCP?

Yes. Model Context Protocol is one option for exposing a chosen set of your capabilities to compatible AI clients such as ChatGPT. See our MCP Integration page for detail.

Need help building your ideas?

Scorchsoft are expert app and portal developers based in the UK. We have over 16 years experience making rich, functionally complex apps and web apps.

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Discover How Scorchsoft Can Help

We would love to hear about your project. Please contact us, and share your goals; we'll respond with our thoughts and a rough cost estimate.

Scorchsoft is a UK-based team of web and mobile app developers and designers. We operate in-house from Birmingham, and our offices are located in the heart of the Jewellery Quarter.

We can deliver your innovative, technically complex project, using the latest web and mobile application development technologies.

Scorchsoft develops online portals, applications, web apps, and mobile app projects. With over sixteen years experience working with hundreds of small, medium, and large enterprises, in a diverse range of sectors, we'd love to discover how we can apply our expertise to your project.

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