Traversaal Labs

Production AI,
in the wild.

We're a forward-deployed engineering team. We take on two or three clients at a time, embed with their team, and ship to production.

Let's talk →
Engagement
6–12 wks typical
Deliverable
Working production system
Hand-off
Docs · training · code
Stack
Yours, not ours
Selected work

What shipping AI
actually looks like.

Real work, real clients, real results. Anonymised where we have to be - specific everywhere we can.

Operations · Delivery review
Decision layer live queue · today
At-risk · 12Auto · 47
#4821 · Whirlpool 27" Rangedamaged appliance · Atlanta GA
risk
#4819 · Vanity 48"contractor install risk
review
#4816 · LG Washercustomer confirmed window
auto
#4821Auto-reroute
Causehub damage
Actionreroute replacement
Ownerno human needed
~73% auto-resolved15× faster review8× daily scope
~73% automated
Retail
How we built a Fortune 500 retailer a multi-agent decision layer that fixes 73% of delivery problems
Fortune 500 Retailer
Multi-agentHuman-in-loopProduction
→
Learning · Expert archive
Cited answer workspace pilot · customer-ready
100% cited
Learner question
What does our research say about psychological safety on hybrid teams?
The guidance points to explicit norms, leader availability, and a visible trail back to source material.
source lockedfollow-up ready
Framework · team climate.94
Assessment guide.87
Facilitation guide.81
10M+ pages indexed100% cited3 sources matched
10M+ pages
Enterprise learning / L&D
How we built an agentic retrieval system for a global leadership development firm that turned 10 million pages of leadership research into a tool learners can actually trust
Global leadership development organisation
Agentic RAGCitationsEval harness
→
Demand · LTO #312 · 14-day
Forecast before first receipt new LTO launch
Under target
MAPE
18.23%
▼ vs <20% target
Supply
8–12w
ahead
Coverage
~50%
of category
Day 0Day 4Day 8Day 14
18.23% MAPE
Restaurants / QSR
How we built a forecasting agent for a quick-service chain that predicts launch demand for new limited-time menu items
National restaurant chain
ForecastingModel bake-offProduction API
→
destination · research · Lisbon
Destination research assistant traveller-facing
live brief
Ask4 days in Lisbon · food + viewpoints · no tourist trapsrunning
38 reviews14 sources
Source scanmaps · guides · traveller notes
3.2s
Local signal filterAlfama · Belém · Príncipe Real
live
Draft answer
Start in Alfama, save Belém for early day 2.Avoids coach-tour peaks and keeps the food stops close to viewpoints.
14 sources86 reviews weighed2024 live with travellers
30% sales lift
Travel / Consumer AI
How we built a travel company's AI destination research agent that increased its sales by 30%
AI travel planning startup
Agentic researchGuardrailsHosted API
→
How an engagement runs

Discovery → production,
in three phases.

Every engagement follows the same shape. Enough structure to be predictable, enough flexibility to fit the reality of what we find.

Phase 01
Discover
Audit goals, systems, data. Find where AI creates real value - and where it doesn't. No assumptions, no pre-sold solutions.
Phase 02
Design
Prioritise use cases. Architect the solution. Build the roadmap with the team who'll own it after we leave.
Phase 03
Deploy
Build, test, ship. Hand off with documentation, training, and code. The stack stays yours - we don't lock you in.
Your data is already there

If it should be driving
decisions, let's talk.

Two or three clients at a time. 30-minute call, no pitch deck, no slides about “the AI revolution”.

Get in touch →hello@traversaal.ai