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 HR Tech

HR platforms sit on mountains of unstructured data — resumes, reviews, feedback surveys, employee records. I build AI that turns that data into actionable insight and automated workflows.

AI integration for HR tech means building production AI pipelines that screen resumes, unify employee data across systems, surface performance insights, and automate workflows. A 2-week sprint delivers one of these features for €5,000, integrated with Workday, Slack, or Google Workspace.

HR platforms collect enormous amounts of unstructured data — resumes, reviews, survey responses, Slack messages, interview notes — but surface almost none of it as actionable insight. The hiring manager who needs to know if a candidate is a fit reads a resume. The VP who needs to know if attrition is spiking opens a spreadsheet. AI bridges the gap between raw HR data and the decisions people actually need to make, without requiring anyone to learn a new tool or change their workflow. The constraint is privacy: employee data demands GDPR compliance, bias auditing, and granular access controls.

Problems I solve for HR tech teams

Resume screening takes hours per open role.

Recruiters manually read hundreds of resumes for every position. Top candidates get buried in the stack, and the screening criteria is inconsistent across reviewers.

Employee data is siloed across systems.

Performance data lives in one tool, compensation in another, engagement surveys in a third. Getting a complete picture of any employee means logging into five different dashboards.

Performance reviews lack data-driven insights.

Managers write reviews from gut feeling because the data is too scattered to synthesize. Trends in engagement, productivity, and retention go unnoticed until someone quits.

What a 2-week sprint delivers

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

AI-powered resume screening and ranking — matches candidates to job requirements using semantic similarity (not just keyword matching), with explainable scoring that shows why each candidate was ranked where they are

Employee data unification and search — connects HR, payroll, engagement surveys, and performance reviews into a single searchable knowledge base so any question about an employee is answered in seconds, not hours of cross-referencing

Performance analytics and trend detection — surfaces retention risks, engagement drops, and team health patterns from data your managers already collect but never have time to analyze

Automated job description generation — takes role requirements and hiring manager notes and produces consistent, inclusive listings with configurable tone, seniority signals, and compliance with local employment law formatting requirements

Built for employee data privacy

HR data includes PII, compensation, and performance records. Every sprint ships with privacy-first architecture.

GDPR-compliant by default — employee PII is processed per EU data protection requirements with consent tracking and right-to-deletion support

Bias-aware screening — resume scoring models include fairness constraints and explainability so you can audit for adverse impact before deploying

Role-based data access — managers see team analytics, HR sees compensation benchmarks, employees see their own data, nobody sees more than their role requires

Data retention controls — automated deletion of candidate data after configurable periods, with audit logs showing what was purged and when

Tech I integrate with

PostgreSQLWorkday APIsSlackGoogle Workspace

Also serving:

Fintech Healthcare Legal Tech E-commerce B2B SaaS

Turn your HR data into decisions.

Book a 30-minute call to discuss how AI can automate resume screening, unify employee data, or surface workforce insights in your platform.