AI integration services that work with the software you already run.
We are an AI integration company for businesses that want AI inside their existing tools rather than another app to log in to. We connect models to your CRM, your documents and your workflows, are designed to keep your data inside your boundary unless you approve otherwise, and put a person in the loop wherever a decision matters.
Integrate AI into your existing business
Most of the value of AI comes from where it sits. A model that drafts a reply inside your help desk, or answers from your own policy documents, saves more time than a separate chat window.
Background reading: what AI integration is, what an AI agent is and local AI vs cloud AI.
Where we connect it
- CRM integration: summarize accounts, draft follow-ups, flag stalled deals
- ERP integration: read orders, inventory and invoices to answer questions in plain language
- API integration with the line-of-business systems you already use
- Document and knowledge-base integration, so answers come from your own material
Automate repetitive tasks with AI
AI workflow automation works best on tasks that are frequent, rules-based and checked by a person: sorting email, extracting fields from documents, drafting routine replies, and building reports.
Background reading: how AI reads PDFs and business documents, how AI can automate email, and how to hire an AI consultant or automation firm.
Common workflows
- AI-powered customer service: draft replies for staff to approve
- AI sales and lead automation: qualify enquiries and draft first responses
- AI document processing and data entry automation
- AI reporting and analytics in plain language
Generative AI, LLMs and custom AI agents
Generative AI integration covers large language models (LLMs) such as open-weight models run on your own hardware, or hosted models where your policy allows it. Custom AI agents can carry out multi-step tasks, always inside limits you set and with a person approving anything that leaves the building.
What you get
- A model chosen for the task, with the trade-offs written down
- Retrieval over your documents so answers cite your sources
- Agents that can only use the tools you allow
- Training for your team to run and adjust it
Privacy, security and human review
We design so client data stays on hardware you control unless you decide otherwise. We design for least access, log what the system does, and keep a person reviewing any output that is sent to a customer or used for a decision.
Governance practices
- Designed so your data is not used to train third-party models unless you approve it
- Role-based access and audit logs
- Human review before anything is sent or filed
- A written record of what the system can and cannot do
How does AI integration work, step by step?
Every integration follows the same six steps. Each one ends with something you can review.
- Pick one workflow. Choose a frequent, measurable task and write down how it works today. You receive a one-page description of the task and what counts as a correct result.
- Map the systems and the data. List where the data lives, how each system can be reached (an API, a database or an export) and who may see what. You receive a connection map.
- Choose the model and where it runs. Hosted, open-weight on your own hardware, or both, with the trade-offs in writing, as set out in local AI vs cloud AI. You receive a recommendation and the reasons.
- Build the connection and the review step: the service that calls the model, retrieval over your documents, and the screen where a person approves the output. You receive a working integration on test data.
- Test on your own cases. Real past examples run through the system, with time and errors measured. You receive a test record you can read.
- Launch, monitor and hand over. Actions are logged, your team is trained, and the documentation says how to change the system or switch it off. You receive documentation and a written support arrangement.
Which systems can AI be connected to?
A model can be connected to most business systems that offer an interface. This list describes what can be connected, depending on the interfaces each system exposes. It is not a list of past client work.
- CRM: read accounts and notes, draft follow-ups, create tasks.
- ERP and accounting: read orders, stock and invoices so staff can ask questions in plain language.
- Email: sort, summarize and draft replies for a person to approve. Our private email assistant is an internal example.
- Microsoft 365: mail, calendar, Teams, SharePoint and OneDrive through Microsoft Graph, once your administrator grants access.
- Google Workspace: Gmail, Drive, Docs, Sheets and Calendar through the Google Workspace APIs.
- Databases: read-only access to SQL databases, often through a reporting view or a read replica.
- Other APIs and webhooks: help desks, ticketing, shared drives and any line-of-business system that exposes one.
- Systems with no interface: scheduled exports or files, with the limits written down.
AI integration, custom AI development or off-the-shelf AI: which fits?
| Factor | Off-the-shelf AI tool | AI integration | Custom AI development |
|---|---|---|---|
| What it is | A finished product with AI features, used as it comes | A model connected to your existing software and data | A model or product trained or built for one specific problem |
| Fit to your workflow | You adapt to the tool | Built around the tools your team already uses | Built around one problem, from the ground up |
| Cost pattern | Subscription, often per user | Build cost plus running cost for the model and hosting | The highest build cost, plus ongoing training and upkeep |
| Time to value | Fastest | Moderate, starting from one workflow | Longest |
| Best when | A tool already covers the task and its data terms suit you | The task lives inside systems you already run | No existing model can do the job, and the problem justifies the investment |
For many businesses the first two columns are enough. Where you have sensitive documents, see private AI for models that run on hardware you control. For a longer comparison of hosted and self-run models, read OpenAI API or an open-weight model.
When is AI integration not the right choice?
- A product you already pay for has added the AI feature you want. Switch it on first.
- The task is rare, or every case needs fresh human judgment, so there is little repeated work to save.
- The data the model would read is scattered, out of date or contradictory. Fix that first, because a model will repeat the confusion.
- You expect the system to make high-stakes decisions with no person reviewing them. We design for review, not for that.
- You want a new model trained from scratch. That is a research project and not an integration.
What drives the cost of AI integration?
- How many systems the model must connect to, and the quality of their interfaces
- How much work the person reviewing the output still has to do
- Whether the model is hosted (usage cost) or runs on your hardware (purchase and upkeep)
- The volume of documents or messages, and how often they change
- Sign-in, permissions and audit logging requirements
- Exception handling, monitoring and support after launch
We do not publish prices. You receive a fixed price for a defined scope, or a clear rate with a ceiling, in writing before any invoice. For the budgeting method, read how much AI automation costs.
What are the risks of integrating AI, and how are they reduced?
- Wrong or invented answers. Retrieval from your own documents with cited sources, and human review before anything is sent or filed.
- Data going where it should not. Least-access connections, logging, and a design intended to keep your data inside your boundary unless you approve otherwise.
- Hostile text inside emails or documents. Anything the system reads is treated as untrusted input, and the model gets only the tools it needs. The OWASP guidance on prompt injection describes the problem.
- Dependence on one vendor or model. The model is a replaceable part of the workflow, and the application keeps working if the AI service is down.
For a wider vocabulary of AI risk, the NIST AI Risk Management Framework is a useful reference.
How do you choose an AI integration company?
- Do they start from one workflow and a measurable result, or from a technology demo?
- Do they explain where your data goes, in writing, before they build?
- Is a person in the loop wherever a decision matters?
- Can the model be swapped later without rebuilding the workflow?
- Who maintains the system after launch, and can your team run it?
- Will you own the code, the configuration and the documentation?
Questions buyers ask
Tell us what you are trying to accomplish.
A conversation, then a written scope with a fixed price or a clear rate and ceiling. No discovery invoice before you know what you are buying.