10–40%
saving on total project time — from requirements to delivery
100%
of models linked to business goals — automatically
minutes
from requirements spec to SMART goals, models, estimates and plan
6
steps AI performs automatically — from requirements to finished plan
What AI takes over — and what you use the time for instead
Before a project can start in earnest, a long list of things must be in place. The requirements specification must be read and understood. Business goals must be clearly defined. The project must be divided into models. Every piece of work must be connected to a goal. The team must be matched to tasks based on what they can do. Time estimates must be set. And the plan must be laid — accounting for dependencies and who has time when.
That takes days. Often more. And it is work that requires experienced people — not because it is difficult, but because it is time-consuming and requires an overview of many things at once. Proglar AI does it in minutes. And frees up the experienced people for what actually requires human judgement.
⚠️
What normally takes daysRead requirements spec → define goals → divide into models → match skills → estimate time → create plan. All of this is done manually in most projects. And that is exactly what AI takes over.
Six steps AI performs automatically
Give AI your requirements specification or solution proposal. It can be a document, a description or a set of slides. AI performs six steps and delivers a complete foundation for the project.
Reads the requirements specification
AI reads your requirements specification or solution proposal — and understands what the project needs to deliver. You don't need to reformat anything. Give AI what you have.
Converts requirements into SMART goals
Vague requirements become concrete, measurable business goals. 'Improve customer experience' becomes 'reduce support requests by 30% by Q4.' You approve — and AI uses the goals as a guideline throughout the project.
Clear goals are the most important foundation for everything that followsProposes project models
AI divides the solution into models — the parts the project consists of. Every proposal is justified: what the model must deliver, what it depends on, and which business goal it supports. You decide what gets approved, adjusted, or removed.
The models are the foundation — mistakes here are cheap to fix. Mistakes discovered during implementation are expensiveAssigns skills per model
For each model, AI specifies which skills are needed — backend, frontend, UX, data analysis, compliance and more. This is the basis for assembling the right team and identifying what needs to be learned or brought in from outside.
Estimates time per model
AI gives a concrete suggested time estimate per model based on complexity and type. Not a rough guess — a structured estimate based on what the model actually needs to do and what that requires.
Estimates per model give a far more accurate overall picture than a single project estimateCreates a plan that fits your team
When models, skills and estimates are in place, AI creates a draft plan. It accounts for dependencies between models, how much time individual team members have available, and what skills they have — or can develop along the way. You approve and adjust. AI keeps track of the consequences.
The plan is a proposal — you decide. Change something and AI shows what it means for the restAll six steps take minutes. What normally requires days of preparation and coordination is done before the first meeting. And because everything is connected — goals, models, skills, estimates and plan — there is no need to gather information from six different places.
AI gives everyone an overview — not just the project manager
Because AI has built the entire foundation — goals, models, skills, estimates and plan — it is not only the project manager who has the overview. Everyone on the team can see what they are working on, why it connects to the goal, and what comes next.
These are three questions that normally only the project manager can answer. Now everyone can. And that changes how a team works.
✕ Days spent converting requirements spec into goals, models and plan
✕ Skills and estimates set manually — or not at all
✕ The plan does not account for who has time and what they can do
✕ Changes require manual replanning — takes hours
✕ Leadership receives status updates, not decision-making material
✕ Errors in models discovered late — when they are expensive to fix
✓ Requirements spec in — SMART goals, models and plan out. In minutes
✓ Skills and estimates proposed per model by AI
✓ Plan adapted to team availability and skills — automatically
✓ AI updates the plan immediately when something changes
✓ Leadership receives concrete figures and justification — ready to act on
✓ AI finds errors in models before anyone has written a line of code
AI keeps the team on track — even when something changes
Projects change. Requirements are adjusted. New requests come in. Team members have more or less time available. That is normal — but it means the plan that was right in week 1 is not necessarily right in week 6.
When something changes in Proglar, AI can update the plan. It knows which dependencies are affected, who on the team has the right skills and time, and what the consequence is for the rest of the project. You decide — AI shows what your decision means.
Approve the SMART goals AI has proposed
AI has read the requirements specification and proposed concrete SMART goals. Your task is to approve, adjust or supplement them. A good goal is specific and measurable: 'reduce support requests by 30% by Q4' — not 'improve customer experience.'
Spend 10 minutes quality-checking the goals — it saves days of misunderstandings along the wayReview and adjust AI's model proposals
AI has proposed which models the project should consist of. Go through them. Add what is missing. Remove what does not belong. Adjust dependencies. It is your project — AI has made the draft.
It is cheap to remove a model on the drawing board. It is expensive to remove it during implementationLet AI analyse each model continuously
While the project is running, AI can analyse each individual model: are inputs and outputs correctly defined? Is there output nobody uses? Are operations missing? Is the model connected to a goal? Errors found here take minutes to fix. The same errors found during implementation take hours — or days.
Update the plan when something changes
New requirement in? Team member has less time than planned? AI recalculates the plan and shows the consequences. It knows who has the right skills and when they have time. You make the decision — AI ensures the rest holds together.
An updated plan takes seconds. Manual replanning takes hoursWhat Proglar AI actually does — and what it does not
🎯
AI checks whether your goals are concrete enough
Unclear goals become concrete requirements. AI finds the places where it is too vague what actually needs to be done — and suggests how it should be phrased so everyone understands it.
🔗
AI shows what connects to what
Every piece of work is connected to a goal. Models without a goal connection are flagged — their business purpose is not traceable. Tasks that cannot be explained from a goal are candidates for removal.
📊
AI gives the project an overall score
AI scores the project on five areas from 1 to 5 and explains each point. You get something concrete to show leadership — not just a feeling about whether things are going well or badly.
🔬
AI dives into each individual model
Want to know whether a specific part of the project is properly thought through? AI goes through it from end to end and tells you if something is missing, if there is overlap, and whether it can be built as described.
🔍
Example: what AI finds in a model analysisIn a sports data project, AI analysed the model Strength Of Schedule and found: output ProgramStrengthScore is not consumed by any other model — the model calculates something nothing uses. Input types are null — the operation cannot be implemented. No goal connection — the model's business purpose is not traceable. Missing operation: GetProgramStrength must be created. AI delivered a complete list with concrete recommendations. What would otherwise have required a thorough technical review took seconds.
✅
AI is not the project manager — you areAI does not take over your job. It takes over the tedious groundwork — reading models and requirements, finding gaps and inconsistencies, and writing an assessment. You use the freed-up time for what actually requires human judgement.
“We ran thirty projects a year. Getting an experienced person to review every project properly was unrealistic. AI does the first review now — and it catches things we used to miss completely."
— Chief Product Officer, enterprise software company
10–40% shorter project time — and fewer expensive surprises
The 10–40% saving on total project time comes from three places.
Saving 1: initiation
What normally takes days — reading the requirements spec, defining goals, proposing models, setting skills and estimates, creating a plan — now takes minutes. Not because the result is worse. But because AI is never tired, never in a hurry, and never forgets what was written in the requirements spec ten pages back.
Saving 2: avoided rework
Errors in model dependencies or output definitions found here take minutes to fix. The same errors found during implementation take hours. Found in testing takes days. The later an error is found, the more expensive it is. AI finds them before anyone has written a line of code — and that is where the biggest saving lies for many projects.
Saving 3: continuous replanning
When something changes — and it always does — AI can update the plan immediately. It knows who has the right skills, when they have time, and what the dependencies mean for the rest. Manual replanning takes hours. AI takes seconds.
💡
What you can use the saved time forTalk to the people the project is for. Spot problems before they grow large. Make sure the team understands what they are actually working towards. That is what determines whether the project succeeds — and only you can do it.
Give AI your requirements specification. Get goals, models and plan back.
Starter is free and requires no setup. Upload the requirements specification or describe the project — and see what AI delivers.
Frequently asked questions about AI in project management
What is the first thing AI does when it receives a requirements specification?
The first thing AI does is convert the requirements into concrete SMART goals — goals that are specific, measurable and time-bound. It then proposes which models — the parts the project consists of — are needed to achieve those goals. Every model proposal is justified and connected to a goal.
How does AI create a plan that fits my team?
When models, skills and estimates are in place, AI knows two things: what needs to be done and what skills it requires, and who on the team has which skills and when they have time available. AI uses both pieces of information to create a plan proposal that can realistically be carried out by exactly your team — accounting for dependencies between models.
Can AI estimate time even when the project is complex and uncertain?
Yes — because AI estimates per model, not per project. A total project estimate is always uncertain. But estimates for individual models — based on what they concretely need to do and what type they are — are far more accurate. The sum of accurate partial estimates is a better overall estimate than a single guess for the whole project.
What happens if we change the requirements along the way?
AI updates the plan. It knows which models are affected by the change, who on the team has the right skills and time to handle it, and what the consequence is for the rest of the project. You see the consequences before you decide — not after.
What does AI find when it analyses a single model?
AI checks a long list per model: whether inputs and outputs are correctly defined and of the right type, whether there are outputs no other model uses, whether operations are missing, whether the model is connected to a business goal, whether the description matches what the model actually does, and whether there is redundancy with other models. You get a concrete list of findings split into critical, warning and info — with recommendations for what needs to be fixed.
Does this suit us — we are not a large organisation?
Yes. Starter is free and suits individuals and small teams. The Analysis level makes sense as soon as there is more than one stakeholder and requirements that need to be prioritised. Pipeline is for projects where you must meet regulatory requirements — GDPR and similar. Enterprise is for organisations running many projects simultaneously and wanting an overview of them all.