Operations drowning in manual work. Every new project answered with another hire. A rolling backlog of "we'll automate this later" that never moved. Ops overload always won.
What we did
Audit of processes and technology. Hired 2 people into a dedicated automation & AI cell. Shipped the first credit-ops automation inside 8 weeks, with mentor retainer continuing through the quarter.
After
Month 9: a third analyst joins, not because ops grew, but because internal demand for automation outpaced the team's capacity. Bottom-up pipeline of new use cases from across the company.
$0 → $200k
in OPEX saved in year 1, from one in-house department.
“A 2-person department delivered $200k in savings in the first year. After 9 months we hired a third person. The company also saw a surge of bottom-up innovation ideas.”
1,500+ customer emails per month. The team buried in repetitive tickets. Every "let's buy an AI vendor" plan stalled on the same question: who inside actually maintains this after launch?
What we did
Stood up an internal unit under our mentoring. Built a classifier + draft-reply pipeline on the corpus of historical answers. Production rollout in 11 weeks, owned and extended by Esotiq's own people.
After
78% of inbound email is handled by AI. The team moved to cases that actually need a human. Repetitive load gone, morale noticeably up. Running cost flat and predictable.
1,200/mo → $60/mo
emails handled by AI, at $60/mo total running cost.
“Over 1,200 emails per month (78% of total) from customer service are now handled by AI for $60/mo. Finally, people focus on what matters, motivated and relieved by technology.”
Group One Media · Media buying · ~300 employees · TV analytics
Before
6 analysts in TV buying, each losing 3 hours per week to manual Excel wrangling. A two-year-old queue of "we'll build a tool for this" tasks that never got staffed.
What we did
Built a 4-person internal department. First deliverable: parallel Excel processing pipeline. Then a self-service library the analysts themselves extend. Every new use case compounds, no vendor in the loop.
After
Per-analyst weekly toil collapses from 3h to 15 minutes. Operational overload drops month after month as the team keeps shipping. Exactly because the capability lives inside the company now.
3 h → 15 min
per analyst per week, six analysts running Excel in parallel.
“Working with the Outcomes team gives me peace of mind. I was surprised by how many opportunities AI brings. It only took 4 right people for the operational overload to decrease steadily month after month.”
Customer success fielding hundreds of product and report questions every week. Knowledge scattered across docs and heads. "Let's put a chatbot on it" kept bouncing off the same wall. No internal time to set it up properly.
What we did
Grounded AI agent built on client data and internal reports. Controlled rollout with continuous evals and a tight feedback loop with the CS team. Ownership transferred in-house from day one.
After
Customers increasingly go to the AI first. The CS team is unblocked from repetitive answers; cost per answer is predictable, a fraction of a cent. The system pays for itself many times over each month.
human-only → $0.11
per AI answer, grounded in client data and reports.
“I had no idea how much work goes into setting up AI to answer customer questions properly. Thanks to the Outcomes team, it worked. Customers increasingly ask AI instead of the support team now.”
What our clients say
“
A 2-person department delivered $200k in savings in the first year. After 9 months we hired a third person. The company also saw a surge of bottom-up innovation ideas.
Michal
General Manager, Wealthon
“
Over 1,200 emails per month (78% of total) from customer service are now handled by AI for $60/mo. Finally, people focus on what matters, motivated and relieved by technology.
Julka
General Manager, Esotiq&Henderson
“
Working with the Outcomes team gives me peace of mind. I was surprised by how many opportunities AI brings. It only took 4 right people for the operational overload to decrease steadily month after month.
Bart
Head of TV Buying, Group One Media
“
I had no idea how much work goes into setting up AI to answer customer questions properly. Thanks to the Outcomes team, it worked. Customers increasingly ask AI instead of the support team now.
Artur
Head of Product, Exact Forestall
Why the vendor model has a structural flaw
Traditional approach
Traditional approach
→A vendor builds, charges and owns the knowledge
→When they leave, the capability leaves with them
What actually happens
What actually happens
·Fewer than 10% of AI use cases reach production
·No knowledge transfer to the company
·Vendor lock-in: you're back at zero when they leave
·Zero autonomy
source: McKinsey State of AI, November 2025
97% of automations we deployed in 2025 are still running in production today. All 22 specialists we placed for clients in 2025 are still on their teams. Not one has left.
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