How much does an AI integration sprint cost?

€5,000 flat for a fixed-scope, 2-week engagement. This includes discovery and scoping (days 1–2), implementation in your codebase (days 3–12), and handoff with full documentation, tests, and a walkthrough call (days 13–14). Two weeks of bug fixes post-handoff are also included. Most AI agencies charge €50,000–€300,000+ for comparable multi-month engagements.

What if I need more work than fits in 2 weeks?

Two sprints back-to-back, €10,000 total, 4 weeks of work. Each sprint still has its own fixed scope and deliverable — you see shipped results every two weeks rather than waiting months for one big reveal.

Do you offer hourly billing or retainers?

No. Fixed price per sprint, no hourly billing, no retainers. Fixed pricing forces clarity: we agree on the deliverable before I write code, and there is no incentive to stretch the work.

What if the feature does not meet spec?

I fix it for free until it matches the scoping document, or I refund in full. The delivery guarantee removes the risk of paying for something that does not work.

Why only 2 sprints per month?

Deep focus per client is the main value. Running more than two sprints in parallel means context switching, which hurts quality. If you need to book further out, schedule a call to reserve a slot.

What can an AI integration sprint deliver?

Production-ready features including RAG-powered search over internal data, intelligent document processing, AI-driven classification and summarization, LLM-powered workflow automation, multi-agent systems, and custom AI integrations for your existing stack.

How does the 2-week timeline actually work?

Days 1–2: discovery call and scoping document. Days 3–12: implementation in your codebase with daily async updates, feature in staging by day 8. Days 13–14: handoff with documentation, tests, and a walkthrough call.

How much of my engineering team's time does this require?

Minimal. A 60-minute scoping call on day 1, a 30-minute staging review around day 8, and a 60-minute handoff call on day 14. Roughly 2.5 hours of meetings total, plus async review of code and docs.

How long does it take to add AI features to a SaaS product?

With a structured sprint, production-ready AI features ship in 2 weeks. No discovery workshops, no 6-month roadmaps — just scoped, built, and deployed code.

What tech stack do you work in?

TypeScript and Next.js for the web layer, Python for data and ML pipelines, any modern vector database (pgvector, Pinecone, Weaviate). Compatible with most B2B SaaS stacks. If your stack is not a fit, I will tell you upfront on the scoping call.

Do I need to hire a full-time AI engineer?

No. A fixed-scope sprint lets you ship production AI features without the overhead of hiring. You get production-ready code, documentation, and 2 weeks of post-delivery support — all for €5,000 per sprint.

Who owns the code after delivery?

You do. Everything ships into your repository, branded as your team's code. No recurring fees, no lock-in, no dependencies on me to keep it running.

What happens after the handoff?

Two weeks of free bug fixes are included. After that, the feature is yours to own and extend. If new issues surface months later, you can re-engage me for a small scope or lean on your internal team — the code and docs are written to be maintainable.

AI Integration for E-commerce

E-commerce products live or die by search, recommendations, and content. I build AI that helps customers find what they want, discover what they didn't know they needed, and buy faster.

AI integration for e-commerce means building production AI pipelines that improve semantic search, personalize recommendations, generate product descriptions, and automate customer support. A 2-week sprint delivers one of these features for €5,000, integrated with Shopify, Algolia, Stripe, and your product database.

E-commerce conversion lives and dies on three things: can the customer find the product, does the product page convince them, and is the support experience frictionless? Traditional search and recommendation engines hit a ceiling because they match keywords, not intent. A customer searching for “gift for dad who likes cooking” will not find your premium knife set unless the AI understands the relationship between the query and the product attributes. That is the gap AI closes — and it compounds: better search means better click-through, better recommendations mean higher AOV, and AI-generated descriptions mean your entire catalog is discoverable instead of just the SKUs your content team had time to write.

Problems I solve for e-commerce teams

Product search returns irrelevant results.

Customers search for 'lightweight summer jacket' and get winter parkas because your search only matches keywords. Bad search means abandoned sessions and lost revenue.

Recommendations are generic and don't convert.

'Customers also bought' isn't cutting it. Your recommendation engine shows the same products to everyone because it can't understand purchase intent or product relationships.

Product descriptions are manually written for thousands of SKUs.

Every new product needs copy for the listing page, search snippets, and ad creatives. Your content team is months behind, and half your catalog has placeholder descriptions.

What a 2-week sprint delivers

Each sprint targets one high-impact workflow. Here are typical e-commerce deliverables.

Semantic product search — customers type 'lightweight jacket for hiking in rain' and get waterproof shells, not keyword-matched results for 'jacket.' Handles synonyms, misspellings, and multi-attribute queries out of the box

Personalized recommendation engine — combines purchase history, browsing behavior, and product similarity embeddings to surface relevant items per user, not the same 'popular products' list for everyone

Automated product description generation — takes structured product attributes (dimensions, materials, use cases) and produces unique, SEO-optimized copy per SKU, eliminating the content backlog that keeps half your catalog on placeholder text

Customer support AI — answers order status, return policy, sizing, and product comparison questions from your knowledge base, with handoff to human agents when confidence is low or the issue is complex

Built for catalog-scale reliability

E-commerce AI touches product data, customer behavior, and purchase flows. Accuracy at scale is non-negotiable.

PCI-DSS aware architecture — AI pipelines never touch raw payment data, only product and behavioral signals that are safe to process

Catalog-scale embedding pipelines — tested against 100K+ SKU catalogs to ensure search and recommendation quality does not degrade as your catalog grows

A/B test ready — every AI feature ships with feature flags and metrics hooks so you can measure conversion impact before full rollout

Fallback-safe recommendations — if the model is uncertain, the system falls back to rule-based defaults rather than showing irrelevant products

Tech I integrate with

ShopifyPostgreSQLAlgoliaStripe

Also serving:

Fintech Healthcare Legal Tech HR Tech B2B SaaS

Help your customers find what they want.

Book a 30-minute call to discuss how AI can improve search, personalize recommendations, or automate content in your store.