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AI integration services

Machine learning and LLMs built into the systems you already run

Other expertises:
Generative AI AI chatbot AI & ML development

Artificial intelligence integration services

If you want to add AI to your systems, there’s no need to rewrite them. Blackthorn Vision’s enterprise AI integration services build machine learning and LLMs directly into the software you already run, and keep that software running the entire time we do our work.

As a Microsoft Solutions Partner, we work primarily on the Microsoft stack. That means models on Azure wired directly into your .NET services, your SQL Server databases, and the desktop applications your business depends on every day.

This service is for clients with a system in production, a model in mind, and no clear way to connect the two. We prepare an integration plan that names every connection point, every risk, and the associated cost. Once you approve it, we start the integration right away, without interrupting any of your processes.

Our AI integration services can help you avoid

  • Costly implementation failures

    A model that performs well in a demonstration can still fail the moment it meets your systems. The demonstration runs on clean data along a single smooth path, while production sends messy data, unexpected edge cases, and heavy load all at once. Many AI projects break at exactly this step, and fixing the resulting damage after launch tends to cost far more than preventing it.

    We map the integration carefully during the assessment phase, so that risks surface and get addressed while they remain inexpensive.

  • Data silos and fragmentation

    Your model must be able to reach all the necessary data. In most mid-sized companies, that data is scattered across a CRM, an ERP, several databases, shared drives, inboxes, folders, and spreadsheets.

    Our AI/ML data integration services build the pipelines that pull those scattered sources together into one consistent view and keep that view up to date as the underlying systems continue to change.

  • Workflow disruption and downtime

    An AI rollout can take the whole system down when the new path ships without a fallback in place, so we build against a staging copy and release everything behind feature flags. The old path keeps running until the new one has proven it can handle real traffic, and if it can’t, traffic falls back automatically to the path that was already working.

    Your team keeps working the entire time this transition takes place, and most of your users never notice that a change happened at all.

  • Security breaches and data leaks

    Pasting proprietary data into a public model endpoint that logs every prompt it receives is the fastest way to leak that data outside your organization. It only takes one person on your team pasting in a real customer record before that record lives permanently on a server you have never seen and cannot reach.

    We deploy private Azure OpenAI instances and keep your data inside your own environment, with role-based access configured during the first week of the engagement, so that nothing leaves a server your organization controls.

  • Inflexible tech stacks

    Wire your system into a single vendor’s API, and you inherit that vendor’s timeline along with it. Model generations turn over every few months, and prices tend to drop each time a new one appears. The model you build on this year can end up slower and more expensive than what ships next spring.

    We build the model layer as a swappable component, so that moving to a cheaper or better model takes a quick configuration change.

  • Poor user adoption and resistance

    To keep a new tool from getting abandoned after the first try, we interview the people who will actually use it and spend time learning the workflow they already rely on. Only then do we figure out how the AI should fit into that existing process.

    The tool ends up saving them time and effort on their daily work, which is why they keep reaching for it long after the initial rollout is finished.

Our AI integration services

Our AI integration services

API and custom AI endpoint integration

Our AI and GPT integration services connect your applications to AI through clean API layers, regardless of whether the model runs on Azure OpenAI, a third-party provider, or your own infrastructure. The endpoint handles authentication, rate limiting, retries, and a fallback for when a provider goes down.

When a model needs to run inside the .NET application itself instead of as a separate service, we use ML.NET, so there’s no network call involved.

Legacy software modernization with AI

Most of our clients run software that predates their AI plans by a decade or more. Replacing a system that already works doesn’t make much sense, it can be simply extended.

Our AI implementation services add ML and LLM functions to existing .NET, desktop and database applications through APIs and middleware, and when that isn’t enough, we go directly into the original codebase to make it possible.

Cloud AI infrastructure setup and migration

Our enterprise AI integration services set up the Azure infrastructure your AI systems need to run and scale reliably. That means compute for inference, storage for training data, and network rules that keep both private and secure.

For clients already on AWS, or moving between clouds, we handle the migration in stages, so the running system keeps serving traffic while its infrastructure moves underneath it.

Data pipeline and storage integration

Before a model can produce anything useful, the underlying data has to be located, cleaned, and pulled together into one place that both people and systems can rely on. Our AI/ML data integration services build the pipelines that collect that data from your CRM, ERP, databases, and spreadsheet exports, set up the corresponding storage on Azure, and define clearly who owns which dataset going forward.

The result is data that the next model can train on without weeks of manual preparation.

Generative AI and LLM implementation

Our generative AI integration services deliver retrieval systems built over your existing document base, assistants that cite a verifiable source for every answer, and models fine-tuned on your organization’s domain language. Building these is the easier part; keeping them usable is harder.

We block prompt injection and continually measure how often the model makes things up against a fixed test set. We also set a monthly token budget with an alert that fires before it runs over.

CRM and ERP AI extensions

Our AI system integration services bring lead scoring, document classification, and forecasting directly into the CRM and ERP records they’re meant to support, so an operations manager sees the result on the record they’re already working.

Where the platform doesn’t offer a native way to plug that in, we build the connection ourselves.

Automated workflow and RPA integration

Our AI tool integration services connect AI to the repetitive processes that run your back office. A model reads the incoming document, pulls out important data, and sends it on to the right place. No need for a person to retype any of it by hand.

The AI handles the judgment calls that older rule-based automation couldn’t make, and we keep a human approval step in place for situations when a wrong call would be harmful.

Continuous integration and deployment for AI

An AI system needs a delivery pipeline capable of handling models and data alongside the application code. Our advanced AI integration services set up CI/CD that versions your models and runs evaluation tests automatically before any new model ships to production. If accuracy ever drops below the line you set, the system rolls back automatically.

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What makes us a great choice for AI integration services

  • 16+ years of tech expertise

    We have built commercial software since 2008 and kept machine learning systems running reliably for years. Our portfolio includes models that operate under strict accuracy requirements set by outside regulators, and several of our client relationships have already passed the ten-year mark.

    We are happy to walk you through AI systems that are still running in production today and put you directly in touch with the engineers who maintain them.

  • Official Microsoft Solutions Partner

    As a Microsoft Solutions Partner, we build AI integration work on Azure, .NET, and SQL Server, the same stack most of our enterprise clients already run internally. The engineer connecting a model to your .NET backend already knows that stack extremely well, because it is the same stack they work in every single day.

  • Deep legacy and cloud modernization skills

    We modernize old .NET and desktop systems as our core service, with or without any AI involved at all. Connecting new functionality to a codebase from 2010 runs on procedures we have used many times before. An API layer and middleware handle most of that work, and we only go into the code itself on the occasions when that isn’t enough.

  • Cross-functional engineering teams

    An AI integration touches the model, the underlying data, the backend systems, and the infrastructure that ties everything together. Our advanced AI integration services put the people who own each of those areas on the same team.

    The ML engineer who trains the model sits alongside the .NET developer wiring it into your system, with the cloud engineer running the Azure setup as part of that same team. Every handoff happens inside that one group, on a single shared timeline.

Our AI consulting process

Artificial intelligence integration services

  • 01

    System assessment and integration audit

    We go through your systems, data, and the model you want to add, and then talk directly to the people who run the processes it will touch. You get a written verdict at the end of that process. Some parts integrate cleanly on their own, others need real preparation before they will, and some aren’t worth doing at all.

    If the thing you have in mind genuinely won’t work, we say so at the beginning. We don’t take on integrations we already know will fail within a year of launch.

  • 02

    Architecture design and API strategy

    We design exactly how the model fits into your systems, mapping out where it connects and how data moves between it and your services. For each individual call, we decide deliberately whether it should cross an API boundary or run inside your existing process.

    You get a full diagram of the whole integration, so your engineers can go through it carefully and point out any spots that look problematic. We correct those spots right away, well before the actual integration work begins.

  • 03

    Data pipeline and storage alignment

    Our AI/ML data integration services build the pipelines that feed the model and set the storage and access rules on Azure accordingly. While this is the unglamorous part of the project, it ultimately decides whether the integration can survive real data volume once it goes live. Skip it, and the whole system tends to fall over during the first genuinely busy week.

  • 04

    AI model preparation

    We get the model ready for your specific environment and approach this step with a maximum precision. The input and output formats have to match exactly what your systems already expect. The latency has to fit comfortably within what your workflow can live with day to day. And when the model isn’t sure of itself, there needs to be a clearly defined answer for that moment as well.

    Any of these details, handled carelessly, can be the reason the whole integration goes wrong.

  • 05

    System integration and API connection

    We wire the model into your applications, backends, and databases directly. When we are done, it lives inside the screens your staff already work in every day, so nobody has to learn a second tool or copy answers manually between separate windows. The AI shows up where the work was already happening.

  • 06

    Testing and security validation

    We test accuracy, load, and failure behaviour across the entire connected system, not merely the model running on its own in isolation. Then we run a full security check against whatever standard your particular industry answers to, whether that happens to be HIPAA, GDPR, or SOC 2.

    Prompt injection and data leakage get actively and repeatedly tested at this stage.

  • 07

    Deployment and performance tuning

    We launch on Azure with dashboards and alert thresholds already fully in place before any real traffic arrives. Then we tune the system against that real traffic, because production always tells a noticeably different story than the test set.

    Once real users start arriving in volume, we can see the actual response time and token cost clearly.

  • 08

    Maintenance and scaling

    Over time, the data a model sees in production gradually stops matching the data it was originally trained on, and its accuracy decreases. Because this happens slowly, the resulting slide is genuinely hard to notice.

    We watch closely for that kind of drift and retrain the model either on a set schedule or the moment it crosses a defined line. Every quarter, we also check what the Azure setup is costing you, and we send that report to a dedicated person on your team.

FAQ

  • What is the difference between AI consulting and AI integration services?

    Consulting answers the broader question of what to build in the first place. It looks closely at your business, finds the specific places where AI would genuinely help, and then hands you a ranked list of use cases along with a plan for tackling them in order.

    Integration is the part that actually builds it, and it usually comes directly after the consulting phase concludes. During this process, we take a model that’s already worth deploying and connect it to your .NET backend, database, and existing tools, then keep all of it working properly after launch.

    Blackthorn Vision does both of these. A lot of clients start with our AI integration consulting services first and then move into our AI implementation services once they know exactly which use cases are worth the investment.

  • Can you integrate AI into our custom legacy software, or do we need to upgrade first?

    In most cases, we can integrate without requiring any upgrade at all. Connecting a model to a .NET backend or an older desktop application usually just needs an API layer and some middleware for the data exchange between systems. We only go into the code itself on the rare occasions when that combination isn’t enough.

    We recommend rebuilding a system only in cases where the assessment clearly shows that extending it would end up costing more than replacing it.

  • How do you ensure the integrated AI systems remain accurate and don't slow down over time?

    We closely and continuously monitor both accuracy and speed after launch. For accuracy, we set clear thresholds and check the model against them on an ongoing basis, and we also retrain it either on a fixed schedule or the moment its outputs start sliding.

    For speed, those same dashboards track response time and token cost. At kickoff, you choose how often you would like to receive these reports going forward.

  • How long does a typical AI integration project take from start to finish?

    The initial assessment typically takes between one and three weeks to complete. From there, integrating into an existing system usually takes an additional one to three months, depending on how clean and well-connected your data is and on how many separate systems the model has to reach. You get a defined range immediately after the assessment, and we tighten that range further as the design itself settles.

  • What third-party AI models or platforms (like OpenAI, Microsoft Azure, or AWS) do you work with?

    Mostly Azure and Azure OpenAI, because that’s where most of our clients’ existing systems live. Through that same swappable model layer, we also integrate models from OpenAI, Microsoft, and the various open-source families, and we connect to AWS or other providers whenever a client’s setup calls for it.

    Building that layer as a separate, swappable component means you are never permanently tied to a single provider, and you can switch to a different one whenever you decide to.

  • How do you handle security, and will our proprietary company data be safe from exposure?

    Your data stays inside your own environment. We run private Azure instances and lock down role-based access during the first week of the engagement. Nothing ever gets sent to a public model endpoint at any point. The model only touches data that lives on servers your organization directly controls.

    We regularly build software under HIPAA and GDPR requirements and sign BAAs and NDAs whenever they’re needed.

  • Will our internal team need extensive technical or data science training to use it?

    For most of your team, no. We build the AI directly into the tools they already use, so what they get is a new feature on an already familiar interface.

    The people who administer the system will need to learn more than that. We write them a clear operating guide and walk them through it personally during a formal handover. And if you plan to run the whole thing entirely in-house, we set the work up from the start so your own engineers can pick it up and maintain it themselves.

  • How do you ensure that integrating AI won't disrupt our current daily business?

    We never test anything directly on your live system. First we build and run everything on a full copy, so that if something does go wrong, it never affects an actual customer or staff member in the process. When we do eventually switch the AI on, we do it for a small share of real traffic at a time, while your old setup keeps running underneath it.

    If the new path ever misbehaves, we route traffic back to the old one, and then fix the underlying bug before trying again.

Book a call to discuss your specific case with our AI integration experts.

    Daryna Chorna Customer success manager

    Daryna Chorna

    Customer success manager

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