A clear answer on where AI will pay for itself in your company, then a working system on Azure that proves it.
Blackthorn Vision is an AI consulting company operating worldwide. We have been building commercial software for healthcare, fintech, and B2B SaaS businesses for over 15 years, which means the person who assesses your case is the engineer who later trains the model and writes the integration code.
Most clients come to us with a budget for AI and no clear idea what to spend it on. So we start there. We look at your data, your systems, and your team, validate each use case, and tell you which ones are worth building and which will waste budget. That assessment is the first product of our AI consulting services.
We build on the Microsoft stack: ML and LLM systems on Azure, connected to .NET services, SQL Server, and desktop applications.
Our artificial intelligence consulting services cover AI strategy consulting and use-case scoring, security review, compliance documentation, deployment, monitoring, and retraining. All of it starts with a free 30-minute call.
Three weeks from now, you can have every AI idea in your company ranked: what to build, what to buy, and what to put aside.
A tool can look right in a demo and still miss most of the work it was bought for. Demos run on prepared data and a narrow path through the product, so the gaps appear once real cases hit it. We test candidate tools on your own data and give you a written comparison of what each one does and does not do in your setup.
Model generations replace each other every few months, and prices drop with each one. A system locked into one vendor’s API in 2026 can be expensive and out of date by 2027. We build the model layer as an exchangeable part. Switching to a better or cheaper model is a configuration change, with no development work behind it.
Someone in your finance department pastes a customer list into ChatGPT to reformat it faster. Nobody told them not to, and now that data sits on a server you do not control. We deploy private Azure OpenAI instances, set role-based access, and write usage policies short and clear enough that people read them.
Software that creates work for the people it was meant to help gets abandoned. The signs show up in the first month: logins drop, the old spreadsheets come back, and managers stop mentioning the project. We interview the future users during discovery and design the workflow with them and for them.
Healthcare and finance carry specific obligations: HIPAA in the US, GDPR in Europe, and the EU AI Act with its risk classes and documentation duties. Enforcement under these rules has been increasing. We map every proposed use case to the rules that apply to you before development starts, together with your legal team where there is one.
Most AI pilots never reach production. In the projects we have reviewed, they stopped for the same three reasons: no data pipeline, no integration path, no result owner. We have run an AI consulting business through enough of these cases to design the production version before the pilot starts.
We collect use cases from your leadership and department heads. Each one gets scored on expected return, data readiness, technical effort, and regulatory load. The leading candidates go into a roadmap with scope, budget, milestones, and named risks.
Our AI strategy consulting services close with two documents you can use immediately: the scored backlog and the roadmap.
If an existing product covers the need, we say so. If it does not, we build: forecasting, classification, anomaly detection, and computer vision models trained on your data.
The strictest environment we work in is regulated healthcare: our ML for a US laboratory diagnostics company supports antibiotic selection for sepsis patients, under accuracy requirements set by regulators and regular audits.
Most of our clients run software that predates their current AI plans by a decade. Why replace a system that works?
We add ML and LLM functions to existing .NET, desktop, and database applications through APIs and middleware. When that isn’t enough, we go into the original codebase. You get the integration estimate during the assessment, so the cost is known before you commit to anything.
Before any training, the data has to be located, cleaned, and centralized. In a typical mid-sized company, that means exports from a CRM, an ERP, several databases, and folders of spreadsheets different departments maintain by hand.
We build the pipelines, set up storage on Azure, and define who owns which dataset, so the next model you train starts from data that is already in place.
We build retrieval systems over your document base, fine-tune models on your domain language, and deliver assistants that cite a source for every answer.
Our artificial intelligence consulting services also cover prompt injection defenses, hallucination measured against a test set, and monthly token budgets with alerts before costs run away.
A PoC takes two to six weeks on a defined budget. It checks whether your data is sufficient, what accuracy is reachable, and what a production system would cost to build and to run. You get a working prototype, the numbers behind it, and a cost estimate for the production version.
As an AI consulting firm, we treat “this will not work” as a valid result, and we say so in the report rather than proposing the next phase.
For any system processing patient or financial data, we produce the documentation an audit will request: data flow diagrams, model cards, human oversight procedures, and retention policies.
As an artificial intelligence consulting company working under HIPAA and GDPR, we keep templates for most of it, so this stage usually takes days rather than weeks.
When the data a model sees stops matching the data it learned from, accuracy slides. What was reliable in March starts making odd calls by September.
Our AI consulting services cover that phase: monitoring with alert thresholds, retraining on schedule or when drift crosses a limit, and quarterly reviews of what the Azure setup costs you. Reports go to a named person on your side, on a schedule you agree at kickoff.
We have delivered software since 2008 and kept ML systems in production for years, including laboratory diagnostics models running under accuracy requirements set by regulators. Several client relationships have passed the ten-year mark.
On the first call, we can walk you through the AI projects still running and introduce the engineers who maintain them. The assessment and the PoC are fixed-price. Production runs on time and materials or a dedicated team, whichever fits your budget cycle. Exit terms are thirty days at any stage.
The artificial intelligence consultant who runs your assessment stays on the project if you proceed. Roadmap estimates are written by the team that will deliver against them.
As an AI consulting firm, we keep no separate advisory staff: every consultant here writes production code. Put your technical questions to them directly on the first call. Everything we produce belongs to you: the code, the model weights, the prompts and evaluation sets, and the documentation. Every stage ends with a decision point, and stopping there is a normal outcome.
We modernize old .NET and desktop systems as a core service, independent of AI. Connecting new functionality to a 2010 codebase follows procedures we have applied many times: an API layer, middleware for the data exchange, targeted changes inside the code.
Most clients expect trouble here. In practice, the integration is the predictable part, and it gets scoped during the assessment. As an artificial intelligence consulting company working under HIPAA and GDPR, we get audited by our healthcare and fintech clients regularly, and we pass. Every project runs on a private Azure deployment with role-based access and encryption at rest and in transit.
Before we plan anything, each use case gets a target: the hours it should save per week, the error rate it should bring down, the revenue it should protect or add. Each target comes with a date.
Our AI business consulting drops candidates that can’t be tied to a figure like that. The target goes into the kickoff document, and the closing report puts the measured result beside it. We keep the audit trail from the first week and produce the data flow diagrams, model cards, and oversight procedures that auditors ask for. Your compliance officer is welcome in project calls.
01
An artificial intelligence consultant examines your data, infrastructure, and team, and interviews the process owners before any planning starts. This step exists because most AI consulting services fail not on the technology but on the mismatch between what a company assumes it has and what actually sits in its systems. We check data volume, access rights, system interfaces, and whether a real decision-maker with allocated time is attached to the project. You get a written verdict: what is workable now, what needs preparation first, and what should not be attempted at all. Unlike many ai consulting companies, we put the negative verdict in writing too.
02
This is the core of our ai strategy consulting work. We collect use cases from leadership and department heads, then score each one on expected return, data readiness, technical effort, and regulatory exposure. The leading candidates go into a roadmap with scope, budget, milestones, and named risks attached to each item. Our ai strategy consulting services close with two documents your team can act on immediately: the scored backlog and the roadmap itself. Nothing enters production planning without a number behind it, whether that number is hours saved, error rate reduced, or revenue protected, tied to a delivery date.
03
Before any model sees a single training example, the underlying data has to be located, cleaned, merged, and given a clear owner. In a typical mid-sized company that means reconciling exports from a CRM, an ERP, several internal databases, and spreadsheets different departments maintain by hand, often with conflicting formats and no shared schema. We build the pipelines, set up storage and access rules on Azure, and document who owns which dataset going forward. Projects that skip this stage are the ones that stall later, regardless of how strong the model architecture behind them is.
04
Within two to six weeks you get a working system built on your own data, not a demo dataset, along with measured accuracy and a realistic cost projection for the production version. This stage exists to answer one question honestly: is this worth building at full scale. As an ai consulting company, we treat a negative result as valid output, not as a reason to invent a second phase. You leave with real numbers, whether the decision is to proceed, to pause, or to stop the initiative entirely before further budget is committed.
05
We train and tune the production model, either a custom architecture or a fine-tuned foundation model, depending entirely on what the PoC results showed rather than on a preference set before testing. This is where an ai consulting firm’s engineering depth actually shows: forecasting, classification, anomaly detection, and computer vision models built against your accuracy targets and your regulatory constraints, not generic benchmarks. The same team that ran the assessment and the PoC continues the work here, so nothing gets lost in a handoff between a sales-facing consultant and a separate delivery team.
06
The model connects to your existing APIs, .NET backends, and databases, then appears inside the workflows your staff already use daily, rather than as a separate tool nobody logs into. Most legacy systems predate the AI plans built around them by a decade, and integration is usually the step clients worry about most going in. In practice it is the most predictable part of the engagement: an API layer, middleware for data exchange, and targeted changes inside the original codebase when needed, scoped and estimated during the initial assessment so there are no surprises later.
07
We test accuracy under real load, check failure behavior when inputs fall outside expected ranges, and run a security review against the standard that applies to your industry: HIPAA, GDPR, or SOC 2. This is where ai business consulting has to be concrete rather than aspirational, since a model that performs well in testing but fails silently in production creates more risk than no model at all. Findings are documented the way an audit will expect them documented, with data flow diagrams and model cards prepared in advance rather than assembled after the fact.
08
We launch on Azure with dashboards, defined alert thresholds, and a retraining schedule tied to measured drift rather than a fixed calendar date. As an artificial intelligence consulting company working under HIPAA and GDPR, we keep the monitoring setup and the audit trail active from day one, not added later when a client asks. Support either continues with us on agreed terms or transfers fully to your internal team, with the documentation and access they need to take ownership. Reports go to a named person on your side, on a schedule agreed at kickoff, not left informal.
AI consulting services combine assessment, planning, and engineering. An outside team checks where AI can produce measurable value in your company, then builds and integrates the systems.
For most clients, the benefit is finding a shorter path to a working system and avoiding one or two expensive false starts. Firms offering artificial intelligence consulting services differ mainly in how much of the engineering they do themselves.
Yes, post-launch work is a standard part of our AI consulting services. It includes monitoring against thresholds, retraining when drift crosses a defined limit, and performance and cost tuning. Some clients keep this with us for years; others take over after a handover period.
Each project starts with a target metric fixed in writing. Those include hours saved per week, error percentage, cost per transaction, and revenue per account. We record the baseline before development and measure against it after launch.
No AI consulting company can guarantee the outcome in advance. What you get is a measured before-and-after that both sides can see and analyze.
Assessment takes from one to three weeks. Roadmap from two to four weeks. PoC, two to six weeks.
Production systems are scoped after the PoC and typically run three to nine months from first call to launch, depending mostly on the state of your data and the depth of integration. A responsible AI consulting firm can’t quote exact dates before seeing your data. It can give you ranges and then narrow them after the assessment.
Client data stays in the client’s environment. We work in private Azure deployments, send nothing to public model endpoints, and set role-based access in the first week.
As an artificial intelligence consulting company working under HIPAA and GDPR, we sign BAAs and NDAs and pass client security audits several times a year. Your compliance team can join project calls from day one.
Yes, integration into existing systems is a large part of our work. Our artificial intelligence consulting services grew out of application modernization, so connecting a model to a .NET backend or a ten-year-old desktop application follows procedures we have used many times.
We recommend full replacement only when the assessment shows that extending would cost more than rebuilding, which is not a common case.
No, we bring the data engineers and ML specialists. From your side, the project needs domain experts who know the process and one person with authority to make decisions. If you plan to build an internal team later, we structure the work so your hires can take it over, and write the documentation for that purpose.
The readiness check covers three areas: data (volume, quality, accessibility), systems (interfaces a model can read from and write to), and people (a decision-maker plus a process owner with allocated time).
An AI consulting company should complete this in one to three weeks and give you the verdict in writing, including the verdict that nothing should be built yet. Sometimes buying Copilot or ChatGPT licenses is enough, and we will say so in writing when it applies.
