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Het Resultaat 

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De Uitdaging

\n

Endeavour zocht naar een bestaand klantproject om de mogelijkheden van Proof of Concept met AI te bewijzen en bood dit 'Proof of Concept' daarom proactief aan bij Wijs & Van Oostveen. Het ABS Fund bleek de perfecte use-case om een slimme AI-chatbot te bouwen die complexe, niet-standaard vragen van klanten direct kan beantwoorden, om zo de druk op de binnendienst en klantenservice te verlagen.

\n

Hier zat wel een voorwaarde aan vast. De chatbot mocht alleen productinformatie leveren over het fonds, zoals de werking en de historische prestaties, en absoluut geen beleggingsadvies geven. Het inbouwen van waterdichte guard railing was dan ook essentieel om ervoor te zorgen dat de chatbot strikt binnen de wettelijke kaders blijft opereren.

\n

\n

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Onze Aanpak

\n

We zijn gestart met een strategisch gesprek met de klant om uit twee potentiële use-cases de meest geschikte te kiezen voor dit project. Nadat de focus definitief op het ABS Fund lag, verzamelde ons UX-team alle benodigde data bij de klant. Vervolgens gingen twee developers aan de slag met de technische realisatie.

\n

In plaats van 'vibe coding', waarbij je simpelweg opdrachten naar een AI slingert, kozen we bewust voor spec-driven design. Dit is een meer gestructureerde methodiek waarbij we de AI intensief bevragen en dwingen tot een onderbouwd plan bestaande uit concrete, heldere stappen. De grootste uitdaging in dit proces bleek de kwaliteit van de invoerdata; zo was één kritiek PDF-bestand onleesbaar, omdat het puur uit afbeeldingen bestond. Dit leerde ons al snel dat brondata honderd procent geschikt moet zijn (of gemaakt moet worden) voor AI-verwerking voordat je kunt gaan bouwen.

\n

Het testen van de bot vereiste tevens een compleet nieuwe werkwijze. Bij ‘klassiek coderen’ is het testen lekker voorspelbaar; geef je dezelfde opdracht, dan krijg je altijd exact hetzelfde resultaat. Maar omdat een AI-chatbot zijn antwoorden ter plekke berekent op basis van waarschijnlijkheid, is de uitkomst nooit honderd procent te testen. We losten dit op door een 'gold standard' te introduceren: een vaste set testvragen met de inhoudelijk correcte antwoorden. Een tweede AI controleerde vervolgens de antwoorden van de chatbot, waardoor we via statistieken de kwaliteit konden meten en bijsturen.

\n

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Hier zijn we trots op

\n

We zijn trots op het feit dat dit (voor ons) eerste Proof of Concept-project eigenlijk meteen goed functioneert. Met dit project laten we zien dat je met een korte doorlooptijd en een klein team een sterk digitaal idee kunt neerzetten. Dat maakt experimenteren met AI laagdrempelig en financieel aantrekkelijk.

Bovendien heeft het ons team veel waardevolle inzichten opgeleverd over prompt-kwaliteit en het belang van de gold standard. We hebben bewezen hoe onmisbaar guard railing is voor de veiligheid in de financiële sector.

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