Real value. Not AI hype.
78% of companies already use AI. Only 25% deliver the expected ROI. The difference is not the model — it's the structure around it.
Before AI. After structure + AI.
We do not talk about AI in the abstract. We show concrete process changes — with numbers.
| Process | Before | After | Result |
|---|---|---|---|
| Handling e-mail enquiries | 2 h/day, manual reading & routing, ~30% missed | Auto-classification + draft reply in 15 min | Response time ↓ 80%, missed enquiries ↓ 90% |
| Generating offers & meeting summaries | 45–90 min per offer, inconsistent templates | Structured data → AI draft → 10 min review | Offer creation time ↓ 70%, quality ↑ |
| Reviewing documents & contracts | Lawyer or manager: 1–2 h per document | AI extracts key clauses + flags risk, human reviews | Review time ↓ 60%, fewer oversights |
| Searching internal knowledge | Slack/email noise, 20–40 min searching per query | Internal knowledge assistant answers in seconds | Time on knowledge retrieval ↓ 75% |
| Routine reporting | 3–4 h/week: manual data gathering, formatting, sending | Automated data pull + AI-formatted report | Report time ↓ 85%, fewer errors |
How much can you save?
Enter your numbers. See the estimated savings and payback period.
Estimates assume 60% automation of repetitive work. Actual results depend on process complexity.
The results of this ROI calculator are provided for illustrative and informational purposes only. They do not constitute an offer, a guarantee, or a representation that any specific results will be achieved.
Small implementations. Real impact.
Each case in 4 lines. We clearly mark what is real vs. a model example.
- → Problem: A service company was losing hours answering repetitive customer e-mails.
- → Solution: Message classification + AI-drafted reply workflow.
- → Implementation: 5 days.
- ✓ Result: Response time cut by ~70%, admin workload reduced significantly.
- → Problem: A law firm spent 2 h per contract on initial risk review.
- → Solution: AI extracts key clauses and flags deviations from template; lawyer reviews output.
- → Implementation: 8 days.
- ✓ Result: Review time ↓ ~60%, junior staff can handle more cases.
- → Problem: An accounting office manually reconciled data between ERP and spreadsheets — 4 h/week.
- → Solution: Integration + automated reconciliation with exception reporting.
- → Implementation: 2 weeks.
- ✓ Result: 4 h/week reclaimed, error rate dropped to near zero.
- → Problem: A sales team spent 45 min building each proposal from scratch.
- → Solution: Structured input form → AI draft → 10 min review & send.
- → Implementation: 4 days.
- ✓ Result: Proposal time ↓ 80%, more consistent quality.
We start with the process. Not the model.
Most AI projects fail not because of the model, but because the process is not ready. We fix that first.
Not all processes are worth automating. We look for high frequency, clear rules, measurable output.
Cycle time, error rate, cost per unit, employee hours. No baseline = no ROI proof.
Often it is a prompt + validation logic. Not a custom model. We pick the minimal viable solution.
A controlled test on real data. Human in the loop for all decisions. Measure against baseline.
Compare KPIs before and after. Only then decide whether to scale.
If the pilot delivers, we productionise. If not, we learn why and redesign. No sunk-cost bias.
"We do not deploy AI into broken processes. We fix the process first, then decide if AI adds value."
Where does it actually work?
Every industry has repetitive, rule-based work that is ready to automate — if the process is clean.
Invoice processing · Bank reconciliation · Report generation · Tax data extraction · Expense classification
Strategic tax advice, complex negotiations
Contract review · Clause extraction · Document classification · Client FAQ · Risk flagging
Court argumentation, complex legal strategy
Code review assistance · Test generation · Documentation drafts · Bug triage · Internal knowledge base
Architecture decisions, client relationship management
Lead qualification · Proposal drafting · CRM updates · Competitive research · Follow-up emails
Complex negotiations, key account strategy
Appointment scheduling · FAQ handling · Invoice creation · Client onboarding · Review responses
Bespoke creative work, face-to-face consultations
Order processing · Supplier communication · Anomaly detection · Report generation · SLA monitoring
Carrier negotiations, crisis response
What AI will NOT do without structure
Maturity means knowing the limits. Here is what we always tell clients upfront.
If the process is chaotic, data is inconsistent, or ownership is unclear — AI will automate the chaos. Structure first.
Someone must own the output. AI generates; a human approves. Always define who validates and who is responsible.
LLMs hallucinate. Classifiers misfire. Build quality gates, fallback paths, and human review into every workflow.
For low-volume, low-complexity tasks a simple script or template may deliver better ROI than a full AI pipeline.
Garbage in, garbage out. Messy, incomplete or unlabeled data will cap the ceiling of any AI implementation.
A 20-document pilot and 2 000-document production are different problems. Measure carefully before committing.
30 minutes, no sales pitch. We will identify the top 3 processes worth automating in your company — and what the realistic ROI looks like.
Download our 10-point checklist to evaluate any process before spending on automation.