AI Integration Services
Add AI to the systems you already run.
AI integration services connect AI capabilities, such as language models, prediction or document understanding, to the software your business already uses. NetOfficials works with your existing applications and data, adds AI features through clean interfaces, and avoids a rebuild of what already works. Models that must keep running in production are covered by our MLOps services.
- Building software since
- 2011
- Approved before the build
- Written scope
- Tracked in Jira
- Sprint demos
- Confirmed in the contract
- Your IP
What Is Included in Our AI Integration Services
The model is only one part. Most of the effort goes into fitting it safely into software that is already in use.
System Review
We study your application, data flows and constraints to find the cleanest place to add AI.
Interface Design
An internal API or service that your application calls, so the AI part can change without touching the rest.
Data Preparation
Pipelines that gather the right records and strip out what the model should not see.
Access and Privacy Controls
Permissions and filtering so that users only get results from data they may view.
Testing Against Your Workflows
Checks that the new feature behaves with your real data and does not break existing screens.
Fallbacks and Limits
Timeouts, retries and plain fallback behaviour for when the AI service is slow or unavailable.
Who Our AI Integration Services Suit
If your software works and you want it to do more, integration is usually a better route than starting again.
-
Product teams
Add an AI feature to a live product
Ship search, summaries or suggestions inside a product your customers already use.
- Feature built behind its own service
- Rollout that you can switch off
- Usage and cost visible to you
-
Internal IT teams
Bring AI into core business systems
Give staff smarter tools inside the CRM, ERP or portal they open every day.
- Connection to your existing systems
- Respect for current permissions
- Documentation for your own engineers
-
Companies with older systems
Make legacy software more useful
Layer AI features on top of older applications without waiting for a full rewrite.
- Wrapper services around old code
- Step by step rollout
- Link to a later modernisation plan
Hosted AI Service or Self-Hosted Model?
You can call a model run by a provider over the internet, or run a model on infrastructure you control. The right choice depends on data sensitivity, volume and the skills you have in house. We lay out the options in discovery.
A hosted AI service is usually the better start when
- You want to prove value before investing in infrastructure
- Your data can be sent to a third party under agreed terms
- Request volume is modest or uneven
- You prefer the provider to maintain the model
| Consideration | Hosted AI service | Self-hosted model |
|---|---|---|
| Time to first result | Fast to connect | Needs servers and setup |
| Where data goes | Sent to the provider under its terms | Stays inside your environment |
| Cost pattern | Pay as you use | Fixed infrastructure, cheaper at steady high volume |
| Maintenance | Provider handles the model | Your team handles updates and scaling |
| Customisation | Limited to what the provider offers | Wide room to adapt |
| Best for | Early features and variable demand | Sensitive data or heavy steady use |
How We Deliver Your AI Integration
Every project moves from a written scope to agreed review points, so you always know what is happening and what comes next.
Assess Your Systems
We review the application, its data and its owners, and agree the AI feature and where it connects, in a written scope.
Signed scopeDesign the Connection
We define the interface, data flow, permissions and fallback behaviour, and review them with your engineers.
Approved designBuild in Sprints
The feature and its connectors are built step by step, with a demo at the end of every sprint.
Working featureTest in a Safe Environment
We try it against copies of your real data and workflows before it reaches users.
Verified behaviourRoll Out Gradually
The feature is released to a small group first, then widened as results look right.
Controlled release
What Affects the Cost of AI Integration
Integration effort is set by the systems involved more than by the AI itself. We spell out how each factor applies to you in the written scope before work begins.
Age and condition of your systems
Well-documented modern software connects faster than older systems with no interface.
Number of connection points
Each application, database or service the feature touches adds design and testing.
Data preparation
Records scattered across tools need gathering and cleaning before a model can use them.
Permissions and privacy rules
Role-based access and data filtering add design work. We confirm the rules in discovery.
Model hosting and usage
Provider fees or your own infrastructure grow with request volume.
Testing coverage
More workflows to protect means more regression checks around the new feature.
Why Choose NetOfficials for AI Integration
The working terms below are part of every engagement, not extras.
One Cross-Functional Team
Design, development, testing and DevOps run through a single team, so decisions stay connected.
Senior From Day One
The senior engineer who scopes your work stays involved while it is delivered.
You Own Everything
IP assignment is confirmed in the contract, and you receive the full source code and documentation at handover.
Mutual NDA
We sign a mutual NDA before you share the details of your project.
Your Working Hours
We agree a communication window that suits your time zone at the start.
Visible Progress
Work is tracked in Jira, with a demo and your feedback at the end of every sprint.
How We Choose the Right Stack for Your Integration
We recommend technology for your project and your team's future, not for our convenience. Three questions guide every choice.
Will it handle your load?
We size the design for your expected users, data and growth, so it does not need rebuilding when the business grows.
Will it fit your systems?
We check how the stack connects with the tools, databases and partners you already rely on before we commit to it.
Can you support it later?
We favour well-supported technology with a wide talent pool, so your own team or any future partner can maintain it.
Technologies we work with
- LLM APIs
Python
TensorFlow
Node.js
.NET
Java
GraphQL
PostgreSQL
MongoDB
AWS
Azure
Google Cloud
Docker
KubernetesFlexible Ways to Work With Us
Pick the model that fits where your project is today. You can move between them as it grows.
Project Delivery
We deliver an agreed scope, timeline and price.
- Written discovery scope
- Milestone reviews
- Best for a clear first release
- For ongoing work
Dedicated Team
A team that works only on your project, led by a named delivery lead.
- Cross-functional team
- Sprint demos and planning in Jira
- Scale the team up or down
Team Extension
Specialists who join your existing team, tools and processes.
- Works in your process and tools
- Agreed overlap hours
- Best for adding a missing skill
More From Our AI Development Practice
This service is part of our ai development practice. These pages cover the services most often needed alongside it.
AI Integration FAQs
What is AI integration?
It is connecting AI features to software you already use, rather than building a new product. The AI sits behind an interface, and your existing application calls it when needed.
Do we have to replace our current system?
Not usually. We design the AI part as a separate service so your current application keeps running and only gains new abilities.
Can the AI use our private data safely?
That is a design question we settle early. We agree which data the model may see, how access is limited by role and where processing happens, and record it in the scope.
What if the AI service goes down or gives a poor answer?
We build fallback behaviour so your application still works, and we add checks and review steps where wrong answers would matter.
Do we need to pick a model or provider first?
No. We compare options with you based on your data, volume and constraints. The interface is designed so a model can be swapped later.
Who owns the integration code?
You do. The integration services, connectors and documentation written for your systems are covered by the contract, and you receive the source at handover.
Ready to Add AI to Your Software?
Share a few details. A senior engineer reads every brief and replies with questions and a suggested next step.
- We review your briefAnd reply with questions
- We sign a mutual NDABefore you share the details
- You get a written scopeTeam, timeline and model, before any commitment