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 B2B SaaS

Your competitors are shipping AI features. Your customers are asking for them. I integrate production-ready AI into your existing SaaS product in 2 weeks — no rip-and-replace, no 6-month roadmap.

AI integration for B2B SaaS means adding intelligent features to your existing product — RAG-powered search over your customers' data, AI assistants that answer questions from your knowledge base, automated workflows that replace manual processes, and smart features that make your product stickier. A 2-week sprint delivers one of these for €5,000, integrated into your codebase and deployed to production.

Most B2B SaaS companies do not need a custom model or an ML team. They need one well-scoped AI feature that solves a real user problem, integrated into their existing stack, and shipped before the market moves. The challenge is not the AI itself — it is scoping the right feature, integrating it cleanly with your data model, and shipping it with the reliability your customers expect. That is what a sprint delivers: not a prototype, not a demo, but production code in your repo that your team can maintain.

Problems I solve for SaaS teams

Your product search is keyword-based and users hate it.

Customers search for concepts but your search matches strings. They type 'how to cancel a subscription' and get zero results because the help article says 'manage billing.' Semantic search fixes this without changing your content.

Support tickets keep growing because the docs are buried.

Your knowledge base has the answers, but customers cannot find them. They open tickets instead. An AI assistant that searches your docs and responds in natural language deflects 40-60% of common support queries.

Manual workflows your team should have automated years ago.

Data entry, report generation, classification, routing — tasks that follow predictable patterns but still require human intervention because nobody has had the bandwidth to automate them properly.

What a 2-week sprint delivers

Each sprint focuses on one high-impact feature. Here are the most common B2B SaaS deliverables.

RAG-powered search — let your users search 10,000+ documents, help articles, or records using natural language, with results ranked by meaning instead of keywords

AI assistant or copilot — a conversational interface embedded in your product that answers user questions from your knowledge base, with citations and confidence indicators

Automated data processing — extract structured data from documents, emails, or forms your users upload, mapped to your data model with validation and error handling

Intelligent classification and routing — automatically tag, categorize, or route incoming data (support tickets, leads, documents) based on content, reducing manual triage to near zero

How it fits your stack

No rip-and-replace. AI features integrate into your existing architecture, not alongside it.

Deploys into your existing codebase — not a separate microservice you need to maintain, but code your team can read and extend

Uses your existing auth and permissions — AI features respect the same RBAC your product already enforces, no separate access layer

Stores vectors alongside your data — embeddings live in pgvector or your existing database, not a third-party vector service you do not control

Observable from day one — logging, error tracking, and usage metrics integrated with your existing monitoring (Datadog, Sentry, PostHog, whatever you use)

Tech I integrate with

Next.jsSupabasePostgreSQLVercelAWSpgvector

Also serving:

Fintech Healthcare Legal Tech HR Tech E-commerce

Ship your first AI feature in 2 weeks.

Book a 30-minute call to scope the highest-impact AI feature for your SaaS product. No pitch deck, no discovery workshop — just a conversation about what your users need.