Scorchsoft connects your CRM, ERP, apps, portals and data into one AI-ready ecosystem, reducing manual work, improving data quality and creating the foundations for automation and AI.
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.
“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

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.
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.
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.
Five concrete patterns, all built on software your organisation already uses.
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.
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.
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.
An AI capability summarises account activity, identifies records needing attention, prepares updates or triggers authorised processes, with a person confirming anything sensitive.
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.
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.

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

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.
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 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.
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 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.
Every request is authenticated, and users reach only the information and actions appropriate to them.
Where it fits, AI access follows the same roles your application already uses.
Retrieving information is kept separate from actions that change data, and write access is granted deliberately.
Inputs and AI-suggested actions are validated by deterministic code, and important operations are logged.
We consider where data goes, provider data-processing terms, data residency and the handling of sensitive information.
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 tend to work, not a rigid process. Most projects start small and prove value before extending.
Start from a real workflow where AI would save effort or reduce errors, not from the technology.
Identify what information and which systems the AI needs to reach, and how it should act on them.
Decide the access approach, permissions and business logic, reusing existing APIs where they are suitable.
Implement the capability, wire outputs back into your software, and choose the appropriate model for the task.
Add authentication, permissions, validation, human approval where needed, logging and operational controls.
Ship the first use case into real use, learn from it, and reuse the foundations for the next.

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.
AI integration connects to the wider set of capabilities that make it work in practice.
Connect fragmented systems into an AI-ready foundation.
Durable historical and analytical data for AI and reporting.
The interfaces that let AI reach your systems safely.
The portals and applications AI features live inside.
Multi-step AI that acts across connected systems.
Give ChatGPT and other AI clients controlled access via MCP.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.