AI workflow automation
Ideasforge builds automation systems with artificial intelligence for companies that want to cut manual work, get rid of repetitive tasks and connect processes that today depend on several tools and people. We combine AI, integrations and business rules so each part of the process is solved with the right technology.
We don’t automate for the sake of it. We study the process first and then decide what’s worth automating.
What AI workflow automation solves
The processes that classic tools couldn’t automate share one trait. At some point, a person has to read and decide. An invoice arrives and someone types it in. A request arrives and someone qualifies it. A question arrives and someone looks for the answer.
That step, reading something that doesn’t come in a fixed format and deciding what to do with it, was the limit of automation. Language models take over exactly that step, and with it the whole family of processes that contained it opens up.
- Artificial intelligence
- Conventional software
- OCR
- APIs
- Databases
- ERP and CRM
- Internal applications
- Business rules
- Validations
- Human intervention
Which processes can artificial intelligence automate?
Almost everything we’re asked for fits into four types of process. The fifth point isn’t a type of process, it’s what happens when a case needs a person to step in.
Check whether automating one specific task pays off →Document intake
Invoices, receipts and scanned forms that get read, validated and recorded in your systems without anyone typing them in.
Request handling
Incoming requests that get classified, qualified and sent to the right person, with the data needed to decide already extracted.
Queries on data
Business questions that become safe queries against the database and come back with the answer in seconds.
Writing from data
Emails, summaries and notices that the flow writes using data it has already retrieved and validated. The ones going to someone on your team can go out on their own. The ones going to a customer come from a template you approve, filled in with data that’s already validated.
Escalation with context
Cases that need a human decision reach your team with the data, documents and checks the system has already done.
AI interprets and code executes.
The code moves data between systems, takes the actions and controls the order in which each step happens. The model comes in only when a document has to be read, a request understood or a case classified. It returns a result with a fixed structure that the code can check.
If your process has clear rules on data that always arrives the same way, classic automation solves it cheaper and faster, with no model to monitor. We build that too.
We automate across the systems your company already uses
The automation connects to what you already use: ERP, CRM, databases, email, messaging and, yes, that spreadsheet that runs half the department.
ERP
Look up information, create transactions, update statuses or bring in data from other systems.
CRM
Record contacts, update opportunities, add information or start sales flows.
Databases
Look up and update structured information within the permissions defined for the process.
APIs
Connect applications and services so they exchange information automatically.
Receive messages, interpret their content and use it as the starting point of automated processes.
In-house software
Integrate automations with applications built specifically for the company.
Storage systems
Process files that arrive in folders, cloud storage or internal repositories.
Forms and external applications
Capture information and feed it automatically into the right flow.
Automations already working in production
The two cases have two things in common: they came in through a channel the team already used and they had a measurable figure before and after.
98% of invoices processed with no human intervention
At Stanton, a property management company, each tenant’s electricity, water and gas bills were keyed in by hand and each one took a minute of typing. Today the team forwards them on Telegram, a model reads the bill and the result lands as rows in the spreadsheet they already worked with. 98% go through with nobody touching them and the rest escalate with the document beside them.
See the Stanton case →
More than 3 hours a day saved screening rental inquiries
At Barceloneta Premium, a real estate agency in Barcelona, each rental inquiry arriving on WhatsApp took five to ten minutes of manual checking. Now the flow extracts the reason, the budget and the paperwork from each conversation. The team gets an email saying whether the applicant meets the requirements the agency set, with the reason beside it. The decision is still made by a person.
See the Barceloneta Premium case →AI business process automation, step by step
There’s no need to start with a big project. The first thing is to pick one specific process that’s worth automating.
01 We map the process
Step by step with whoever runs it every day: what comes in, who touches it, which systems it goes through and where it gets stuck.
02 We measure what it costs today
Measuring that starting point lets us compare the result later with data, not impressions.
03 We separate rules from interpretation
We ask for three real examples of the input, from three different sources. If they look alike, your problem is about rules. If each one comes in a different way, that’s where the model is needed.
04 We start with a pilot
It covers only part of the volume, with the figure that should move agreed before starting. The pilot is paid and its price counts toward the final project.
05 We scale up or stop
If the pilot delivers, it’s extended in phases. If it doesn’t, the project stops with a limited investment and specific information about what didn’t work.
Validating every result before it reaches your systems
A model can get things wrong, so its results have to be checked before the process continues. What passes validation flows on its own. What doesn’t isn’t thrown away or made up, it goes to a person with the case ready.
Some steps we deliberately leave with human confirmation: the ones that move real money, the irreversible ones and the ones that decide about people. The system prepares all the information and a person confirms the action before it runs.
When something falls outside the usual flow, the system knows how to
- 01 Stop the process
- 02 Ask for more information
- 03 Pass the case to a person
- 04 Log an incident
- 05 Retry an operation
- 06 Send an alert
- 07 Keep the state of the process
- 08 Continue once the problem is solved
When is a process worth automating?
We don’t automate processes without volume, because a flow that runs three times a month doesn’t pay for its maintenance, or processes that need redesigning before they’re sped up. If your case falls into one of those groups, we tell you before we start.
It usually makes sense when the process
- Repeats often
- Takes a significant amount of time
- Forces people to copy information between applications
- Produces manual errors
- Delays other tasks
- Follows fairly stable rules
- Can be measured before and after
- Has enough volume to justify the development
Before building we analyze
- How much time the process takes today
- How often it happens
- How many people are involved
- What errors it produces
- How much it costs to keep doing it by hand
- Which part can really be automated
Frequently asked questions about AI workflow automation
What is AI workflow automation?
Automation where a language model handles the steps that require reading or interpretation, inside a flow validated by code. The model interprets. The code checks and executes.
Is this RPA?
Not the same thing, although plenty of RPA suites now bundle AI-based reading inside them. RPA repeats clicks and rules over screens and breaks when something changes place or format. What we add is a model that interprets the content, so variation doesn’t break the flow. They work well together, RPA moving the stable parts and the model reading the variable ones.
What if the AI misreads a document?
A validation layer checks the result before it’s recorded: totals, formats, required fields. Doubtful cases go to a person, so review happens exactly where judgment is needed.
How much of the process really gets automated?
It depends how many edge cases it has, so it gets measured rather than estimated. The system records what it resolves on its own and what it escalates to a person, and you see that share from the first month.
Do we have to replace our ERP or our tools?
No. The flow connects to what you already use and the result lands where your team already works, be it an ERP or a spreadsheet. One of our clients manages invoices by forwarding them over Telegram, without having to learn a single new tool.
Can we start with just one process?
That’s exactly what we recommend. One process with measurable pain, a pilot with its metric, and growth in phases if it delivers. The pilot is paid and its price counts toward the final project. That’s how our automation clients started. The longest-running one keeps adding processes.
Does it work with bad scans or phone photos?
The legible ones pass and the doubtful ones escalate to a person with the image right there, instead of getting half-recorded. We don’t promise up front what share falls on each side. It gets measured in your pilot with your real documents.
Does the flow only read, or does it write into our systems too?
It reads and writes, with a safety net. Writes go through validation, and the sensitive ones require a person’s confirmation before they run. What each flow may touch is defined with you, per system and per action.
How long until the first flow is running?
It depends on three things: that the data exists, that access has been granted and that the task to automate is well defined. With all three, a pilot in two weeks is realistic. If one is missing, getting it is the first part of the project. That timeline isn’t ours to set.
What happens to the cases the flow can’t resolve?
They escalate to your team with all the context the system has already gathered. This also covers the cases the flow understands perfectly well but has no business deciding. The goal isn’t one hundred percent. It’s human review sitting where it adds judgment, with the resolved share measured rather than assumed.
How much does it cost to automate a process?
A single-process flow starts at around €2,500 to build, flows that touch several of your systems get closer to €10,000, and monthly operation runs between €150 and €500, which covers weekly monitoring and maintenance. The monthly fee has no minimum term.
The model and the infrastructure run in accounts under your company’s name, so those bills are yours and aren’t part of the fee. What moves those figures here is specific: how many systems have to be connected, how much validation the process needs and how much volume runs through it.
AI agent development cost, broken down → Who owns the flow you build?
You do. The repository is yours from day one, with its documentation and its operations manuals. If one day you want to run it with your own team or another provider, you take the whole flow with its documentation and its tests, not a subscription. What is a service while we work together is the operation, the weekly monitoring and the alerts.
Other solutions
Tell us which process still depends on manual work
You don’t need to decide which tools to use or which part should involve artificial intelligence. Tell us how the process works today, which applications your team uses and where manual work is still needed.