Artificial intelligence consulting for companies
Ideasforge offers artificial intelligence consulting for companies that need to turn AI opportunities into specific, viable and measurable projects. We study processes, data and systems to find where AI can add value, what’s worth building and what architecture each case needs.
We don’t start from a technology. We start from the problem.
You don’t need to know which model or agent you need
Many companies know they want to use artificial intelligence but aren’t yet sure where to apply it. That’s normal. We start from the process and decide which technology makes sense: sometimes an agent, sometimes an automation, sometimes a conventional integration. And sometimes the conclusion is that nothing is worth building.
That’s exactly what consulting is for, making that decision before you invest in development.
- Which process we want to improve
- How much time it takes
- Who is involved
- Which tools it uses
- What data exists
- Which decisions are part of the flow
- What result we want to achieve
What do we analyze in an AI consulting engagement?
Consulting shouldn’t stop at a list of ideas. We analyze the company’s real context. The question isn’t only whether something can be done. It’s whether it’s worth doing.
Processes
How each flow works: repetitive tasks, bottlenecks, manual errors, waiting, duplicated work and processes that depend on too much administrative work.
Data
What information exists, where it lives, who can access it and whether its quality is good enough to support an AI solution.
Systems
ERP, CRM, databases, APIs, documentation, in-house software, storage and external applications the solution would have to work with.
Use cases
Where artificial intelligence can add value and where it’s probably not needed.
Feasibility
Whether the project can be built with the current data, integrations and constraints.
Impact
What improvement it could bring in time, cost, errors, speed, operational capacity, service quality or access to information.
The architecture is a consequence of the problem. Not the starting point.
When a case makes sense, we define the architecture it needs: models, agents, RAG, automations, APIs, integrations, conventional software, permissions, infrastructure, observability and human intervention.
We also define which parts have to stay outside the model. Critical rules, permissions and validations stay in code so the system behaves predictably.
From “we want to use AI” to specific use cases
We turn generic ideas into defined cases. Each case should answer at least these questions.
What problem do we want to solve?
What happens today and why it causes friction.
Who will use the system?
Employees, customers, the sales team, operations, administration or other users.
What information does it need?
Documentation, data, history, messages, internal systems or external sources.
What should it be able to do?
Answer, look things up, classify, validate, take actions or automate a flow.
Which systems does it need?
ERP, CRM, databases, APIs, in-house software or external services.
What benefit do we expect?
Time saved, fewer errors, more capacity, better access to information or a new capability.
What risks are there?
Privacy, errors, permissions, dependence on providers or the impact of a wrong decision.
The strategy can end in a real system
Ideasforge isn’t only a consultancy. We also build the solutions we design. The strategy doesn’t end in a slide deck: it can become software running inside the company.
From a minute per invoice to 98% of invoices untouched
Each utility invoice took a minute of typing. Today 98% go through without anyone touching them and the rest reach a person with the document beside it. Separating which part of a process needs a model and which part rules can handle is exactly what consulting studies.
See the Stanton case →
Correcting course in time: the model stopped writing SQL
The first version let the model write the query and run it. It made mistakes that went through without any error and returned a different figure from the one asked for, so we replaced it. Today the model only interprets the question and the code builds the query.
See the Savian case →An AI roadmap you can actually execute
The result of consulting should let you make decisions. A useful roadmap isn’t a list of ideas: it’s a sequence of decisions you can execute.
01 Prioritized use cases
By expected impact, technical complexity, data availability, integrations, risk, cost and ease of adoption.
02 Quick-win projects
A case with high impact and low complexity can be a good first implementation.
03 Dependencies and requirements
Which data has to be put in order, which API has to be built or which process has to change before building.
04 Proposed architecture
Which technology solves each part and what stays as conventional software.
05 Phases and success criteria
How to measure whether the development generates enough value, with data from the real process.
Security and compliance from the strategy stage
Some architecture decisions directly affect privacy, information exposure and regulatory risk. They shouldn’t be reviewed only once development is finished.
We also identify requirements related to the General Data Protection Regulation and the EU Artificial Intelligence Act.
During consulting we analyze
- 01 What data the system will process
- 02 Whether there is personal or sensitive information
- 03 Which providers are involved
- 04 Where the data is processed
- 05 Which permissions are needed
- 06 Which actions the AI can take
- 07 Which decisions need human oversight
- 08 What traceability has to exist
When does AI consulting make sense?
It adds value when the company knows there are opportunities but still needs to decide which ones to tackle. It can focus on a single process. And sometimes the next step is simply to build.
Before hiring a consultant, try our free tool for what to automate first →It can make sense if
- You don’t know where to start
- There are too many ideas and you need to prioritize them
- You want to identify processes that can be automated
- You need to validate whether a project is viable
- You want to work out whether the investment pays off
- You’re torn between off-the-shelf software and custom development
- You need to define an architecture
- There are security or privacy requirements
You don’t need it if you’ve already defined
- What problem you want to solve
- What the system has to do
- Which tools it has to work with
- What data it will use
- Which rules it has to follow
- What the project’s scope is
Frequently asked questions about AI consulting
What does an artificial intelligence consultancy do?
It analyzes processes, data and systems to find where AI can add value and how to implement each opportunity: use cases, feasibility, architecture, prioritization and impact.
What does AI consulting include?
Process analysis, identifying use cases, reviewing data and systems, prioritization, architecture, risk assessment and building a roadmap. The scope depends on whether we’re analyzing one process or a broader strategy.
How much does AI consulting cost?
It depends on the scope, the number of processes, the complexity of the systems and the level of detail. Assessing one specific case and a company-wide engagement have very different scopes.
How do you identify AI use cases?
By analyzing processes with manual work, information that’s hard to process, decisions that vary or a need to access knowledge. Then each one is assessed by impact, complexity, available data and risk.
How do you know whether an AI project will pay off?
By comparing the cost of building and maintaining the system with the value it can generate: time saved, fewer errors, extra capacity or better service.
Do I need my data ready before starting?
Not necessarily. Part of the consulting can be assessing what data exists and what would have to change before development.
Does consulting include development?
They can be hired as separate phases. Ideasforge can do only the analysis or go on to build the solution.
What’s the difference between AI consulting and AI development?
Consulting defines what’s worth building and how to approach it. Development turns that decision into working software.
Other solutions
Tell us what you want to improve, even if you don’t know how yet
You don’t need to arrive with a defined agent, a designed architecture or a chosen solution. Tell us which process you want to improve, what problems your team runs into or which opportunities you’re weighing. We’ll look at what makes sense to do, what doesn’t and what the next step should be.