AI Integration Services
AI integration is the engineering work of adding AI capabilities to software you already run. Our AI integration services connect modern AI models to your apps, portals and business data, then put the results where your team already works.
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.
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.
Practical adoption, not experimentation
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. If you want the engineering detail first, our guide on how to integrate AI into existing software covers the attachment points, the data work and the patterns that survive production.
Building for teams across healthcare, logistics, motorsport and manufacturing
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.
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
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.
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.
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.
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.
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.
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.
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 — CRM, ERP, databases, documents, portal, SaaS, APIs
- Scorchsoft integration / application layer — APIs, business logic, permissions, search, data services
- AI capabilities — search, summarisation, extraction, copilot, agents, automation
- Users & applications — staff, customers, existing apps, portals
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.
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 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 and 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. The specification is published openly at modelcontextprotocol.io.
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 use it on our own site: this website runs on Payload CMS with an MCP server in front of it, so approved AI clients can draft page content under the same permissions a human editor has, and every AI edit lands as a draft for a person to review. Our MCP integration services page covers the approach in detail.
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, a pattern known as human in the loop.
What our AI integration services include
An AI integration is a piece of production software, so the work looks like software delivery rather than a model experiment. A typical engagement covers the following.
- Use-case shaping — We pick one workflow where AI would remove real effort, and agree what working looks like before anything is built.
- Data and access design — How the model reaches your information: existing APIs, database queries, a search index, a vector store, a document pipeline, or a combination of them.
- The integration layer — The application code that holds permissions, business rules and data services. Most of the engineering sits here, and your portals and later AI work can reuse it.
- The AI capability itself — Prompting, retrieval, tool definitions, structured outputs, and sensible handling of the cases where the model gets it wrong.
- Wiring the output back — Results written into your CRM, portal, database or queue, so the answer lands in the system people already use.
- Controls and evidence — Authentication, validation, approval steps, logging and cost controls, plus a way to judge quality once it is live.
- Handover — Code and documentation, and the option of ongoing support.
Most of that list is ordinary API and systems integration work. That is the honest shape of an AI integration project, and it is why the engineering matters more than the model.
How we approach an AI integration
01
Find the valuable use case
Start from a real workflow where AI would save effort or reduce errors, not from the technology.
02
Map the systems and data
Identify what information and which systems the AI needs to reach, and how it should act on them.
03
Design the integration layer
Decide the access approach, permissions and business logic, reusing existing APIs where they are suitable.
04
Build and connect
Implement the capability, wire outputs back into your software, and choose the appropriate model for the task.
05
Govern and validate
Add authentication, permissions, validation, human approval where needed, logging and operational controls.
06
Prove, then extend
Ship the first use case into real use, learn from it, and reuse the foundations for the next.
Where AI earns its place, and where something simpler wins
AI is one tool among several, and part of our job is working out which one a problem actually needs. These are the cases where we would usually start somewhere other than a model:
- A rule, not a model. Where the logic is deterministic, such as a threshold, a lookup or a validation, ordinary code is cheaper, faster and easier to audit — and building that is our day job.
- A search or reporting fix. Plenty of "we can never find anything" problems are solved by a better query, a better index or a better screen.
- The data is not ready. If the information the model would need is scattered, stale or locked in documents nobody has processed, the first step is the data groundwork, which is a project in its own right.
- Nobody owns the workflow yet. An integration needs someone who can decide what a correct answer looks like, and pinning that down is part of the discovery we run with you.
None of that rules an idea out. Tell us what you are trying to fix and we will say which route we would take — AI, plain software, or a mix of the two. Where AI does earn its place, it usually earns it narrowly first: one workflow, measured, then extended.
Choosing an AI integration company
AI integration touches two sides at once: the AI and model layer, and the software, data and systems the AI has to interact with. A team strong on only one of those is the most common reason this work stalls, so it is worth asking any supplier how they handle both.
Four questions worth asking:
- Who writes the integration layer, meaning the permissions, business rules and data services the AI depends on?
- What happens when the model is wrong, and who decides what correct means for this workflow?
- Can the model be changed later, or is the build tied to one provider?
- What is left behind: code, documentation, and a route to support it?
Why Scorchsoft fits this work
We work on both sides. 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.
Scorchsoft is a UK-based team of web and mobile app developers and designers, working in-house from Birmingham's Jewellery Quarter, with over 16 years of experience across hundreds of small, medium and large enterprises. Our wider AI development capabilities cover consultancy, agents, automation and integration together.
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.
Learn moreData Lake Development
Durable historical and analytical data for AI and reporting.
Learn moreAPI & Systems Integration
The interfaces that let AI reach your systems safely.
Learn morePortal & Web App Development
The portals and applications AI features live inside.
Learn moreAI Agents & Automation
Multi-step AI that acts across connected systems.
Learn moreMCP Integration
Give ChatGPT and other AI clients controlled access via MCP.
Learn moreAlso explore
What Our Clients Say
Scorchsoft helped us take our idea for an app and make it a reality. Everything from the planning meeting to decide what we really needed to the project management and execution was great. It was delivered on time - early in fact - and on budget. Highly recommend.
Rebecca PalserDragonfly IntelligenceScorchsoft is a brilliant company with fantastic knowledge of the mobile app industry. From the project management to the development team, they have been the perfect candidate for our project, and we can't thank them enough!
Lance ChorltonGapped OnlineWe're really pleased with the work Scorchsoft has done in developing our web portal! They have been accurate with timelines and budget, delivering a solid product that allows us to monitor and manage patients remotely while they use our novel medical device at-home. The "plan - design - build" approach has worked well and saved us time in the long-run by catching requirements and issues early.
Daniel GreenSensTrainI'm really pleased with how my app came out, it was exactly what I was looking for. The team at Scorchsoft are great at what they do and made the whole process as simple and easy as possible. Being someone who is not very tech savvy the set up and back end operations were done in a great easy to use manner even for myself which makes using my app stress free. Thanks to all the team!
Ruben CarrollMosaic MasterpiecesThe new Flourish Education website has already removed a lot of manual processes, freeing up both schools, candidates and internal employees time. We are delighted with the look and feel which is clean, professional and more engaging. We are also pleased with the decision to have an HD video background on the homepage, and building immediate trust with our clients by giving them a taste of what it looks like in the Flourish Education offices.
James HancocksMarketing Manager, Flourish Education I can't believe how quickly we started to see results with this project. Scorchsoft provided us with graphic-designed mockups of how the app would look once built, and we were able to sell the product for use by our first customer before the product was finished. Since launching in March, we have secured a major television network as a client who now uses Image Approvals to manage the talent approval process for their productions.
Aimee SpinksMD, ImageApprovals.com
Ready to add AI to software you already run?
Share the process you have in mind and the systems behind it. We will come back with where AI could realistically help, what it would take to build, and whether it earns its place.
Frequently Asked AI Integration Questions
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. Our guide on how to integrate AI into existing software goes through it in depth.
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.
It depends on the workflow, the systems involved and how much groundwork already exists. A narrow first integration against a system with well-documented APIs is a much smaller piece of work than one that has to start by untangling fragmented data. We quote each integration after a short discovery conversation rather than from a rate card, so you see the scope and the price before any build starts. Get a Free Quote with a description of the workflow and we will come back with a realistic view.
Most of the variation comes from your systems rather than the AI. Reachable data and a documented API make it quick; scattered data, a legacy system with no API, or a long security review make it slower. We deliberately aim to get one narrow use case into real use first, because that is what tells you whether the rest is worth building.
A description of the workflow, someone who knows how it actually works today, and a route into the systems involved, such as API documentation, a test environment or a sandbox account. It also helps to raise security and data-protection requirements at that point, because they shape the architecture rather than being added at the end.
Not by default. The business API tiers of the major providers do not train on data sent through them, and we confirm the data-processing terms for whichever provider a project uses. We also design what the model can see: sensitive fields can be excluded from a request entirely, and data residency and retention are part of the architecture decision rather than an afterthought.
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, a human-in-the-loop pattern that is common wherever 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.
You choose. We hand over code and documentation, so the integration can be supported in-house, by your existing supplier, or by us. Where we support one we cover model and dependency changes, watching for quality drift, and extending it to the next workflow when you are ready.
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 services page for detail.
Need help building your ideas?
Tell us where you're headed and we'll come back with our thoughts, a realistic plan and a rough cost estimate. Scorchsoft is a UK-based team of app, portal and AI developers, working in-house from Birmingham's Jewellery Quarter.