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 Legal Tech

Legal work runs on documents — contracts, filings, case law, memos. I build AI that reads, compares, and extracts from those documents so lawyers can focus on judgment, not data entry.

AI integration for legal tech means building production AI pipelines that extract contract clauses, search legal documents, automate redlining, and assist with case law research. A 2-week sprint delivers one of these features for €5,000, integrated into your existing document stack.

Legal work is document work. A mid-size law firm processes thousands of contracts, filings, and memos annually. The bottleneck is not legal expertise — it is the time spent reading, comparing, and searching documents before the real analysis begins. AI handles the mechanical reading so lawyers can focus on judgment, strategy, and counsel. The key constraint is confidentiality: legal AI must run within your infrastructure, respect privilege boundaries, and produce auditable outputs that hold up under scrutiny.

Problems I solve for legal tech teams

Contract review takes days of manual reading.

Every new deal means someone reading 50-page contracts line by line, looking for risky clauses, non-standard terms, and missing provisions. It's the most expensive bottleneck in legal ops.

Legal research is scattered across dozens of sources.

Associates spend hours searching through case databases, internal memos, and regulatory updates. Relevant precedent gets missed because it's buried in the wrong system.

Document comparison is tedious and error-prone.

Comparing contract versions, identifying what changed between drafts, and flagging new risks is painstaking work. One missed clause can cost millions.

What a 2-week sprint delivers

Each sprint targets one high-impact workflow. Here are typical legal tech deliverables.

Contract clause extraction and analysis — identifies indemnification caps, termination triggers, non-compete scope, and liability limitations across your entire contract library, with risk scoring per clause

Legal document search — semantic search across briefs, memos, contracts, and case files that finds relevant precedent by meaning, not just keywords, even when different terminology is used

Automated document comparison and redlining — highlights substantive changes between contract versions (not just formatting), flags new risk language, and generates a summary of what changed and why it matters

Case law research assistant — searches case databases by legal concept, surfaces relevant holdings with citations, and generates concise summaries that associates can verify in minutes instead of hours

Built for confidentiality and privilege

Legal data carries attorney-client privilege and strict confidentiality obligations. Every sprint respects those boundaries.

Data isolation — client documents never leave your infrastructure or get sent to shared LLM endpoints without explicit configuration

Privilege-aware processing — the pipeline tags privileged vs. non-privileged content so automated workflows never inadvertently waive privilege

GDPR-compliant for EU firms — personal data in contracts and filings is handled per GDPR requirements, with right-to-deletion support built into the pipeline

Audit trail for every AI output — every extraction, summary, and comparison is logged with source documents, timestamps, and model versions for defensibility

Tech I integrate with

PostgreSQLElasticsearchAWSDocument APIs

Also serving:

Fintech Healthcare HR Tech E-commerce B2B SaaS

Give your lawyers their time back.

Book a 30-minute call to discuss how AI can accelerate contract review, power document search, or automate legal research in your product.