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KREBS LABS Automation & applied AI · London & EU

Automation & AI,
built to be owned.

We remove the manual work that scales badly — and hand over systems you own, not rent.

50+
Engagements delivered
4–12wk
Typical build window
100%
Handover, always
Education · Client technologies we work with
MBA —Imperial College London Anthropic API n8n Supabase Amazon Vendor Central
Client names withheld by default; references available on request for shortlisted engagements.
§ 01 — How we think

A system —
not a tool.

Most automation work fails in the scoping, not the building. We take the operating reality seriously, and build systems that are still running in year three.

Upstream of the tool

Scope first. Architecture second. Implementation is the last 40% — and the easiest part.

Handed over, not hosted

Documented and handed over on your own infrastructure. No lock-in, no rental relationships disguised as software.

Model-aware

Claude, GPT, local models. Cost, latency, and failure modes designed in — not patched on afterwards.

§ 02 — What we do

Six disciplines. One practice.

Engagements typically combine two or three of the below. We scope the combination to the problem, not the other way around.

01

Workflow elimination

Manual work removed, not streamlined. Lead capture, invoicing, reconciliation, multi-location sync.

n8nMakePythonZapier
02

AI agents

Production agents for research, drafting, and structured judgement — built with cost and latency in the spec.

ClaudeGPTLangChainVector DBs
03

Data pipelines

Scrape, ingest, normalise, sync. Supabase-to-anything — queryable by your team without asking us.

SupabasePostgresAPIsWebflow
04

CRM & sales ops

Lead routing, deal hygiene, drafted outreach. Less of the work your sales team does that isn't selling.

PipedriveHubSpotGmail API
05

Commerce & marketplace

Vendor/Seller Central, flat files, pricing engines, multi-channel sync. Five years in the toolkit.

AmazonShopifyWebflowSAP
06

Conversational agents

WhatsApp, email, voice. Booking, triage, after-hours capture — deployed where your customers already are.

WhatsApp APITwilioRetell
§ 03 — The architecture

Build it twice, once on paper.

Every engagement follows the same shape: diagnose the real process, architect, build on your infrastructure, hand over. Systems built this way are still running when we're long gone.

1
Diagnose the real process
Two calls, a shared doc, a short written back-brief. The process as it actually runs — not the org-chart version.
2
Architect, then scope
Pick the intervention with the best return. Define success and edge cases before a line of code exists.
3
Build on your infrastructure
Your n8n, your Supabase, your Google account. No scratch credentials to migrate later.
4
Hand over, properly
Runbook, architecture notes, walk-through recording, thirty days of bug-fix support. Care retainer optional.
§ 04 — Case studies

A few recent builds.

Client names are withheld by default; descriptors tell you the sector and shape of the work. Two of the four are composites of recurring engagements — flagged where they appear.

Case study · European distribution group № 01 · Q1 2026

From a full day to forty minutes.

A specialist distribution group — tens of thousands of SKUs, tiered pricing, weekly pricelist runs — was building customer-specific pricelists by hand from SAP exports reconciled in spreadsheets. The internal view was that automation wouldn't work because of the edge cases.

We built a Python pipeline that resolves customer-level pricing logic against the account matrix and delivers a finished pricelist. Eighty-plus edge cases handled explicitly, not papered over.

40min
Runtime, vs ~8h fully manual
~7h
Weekly hours returned to one role
80+
Edge cases explicit in spec
krebs · pricing engine Pricelist run · KA-2024-Q1 Account: CUST-4821 · Tier 3 · Q1 refresh VALIDATED 39:48 RUNTIME · 4,218 SKUs · 12 edge cases hit INGEST · SAP APPLY · CUSTOMER AGREEMENTS RESOLVE · EDGE CASES (12/80) FORMAT · EXPORT EDGE CASES · RESOLVED EC-042 · volume-break override · SKU 7812..7850 OK EC-061 · legacy pricing · carry-over flag OK EC-074 · FX reset · EUR → CHF tier 3 MANUAL EC-088 · net-new SKU · default cascade OK
Case study · Wedding vendor directory № 02 · 2025 — ongoing

Zero listings to several thousand.

A consumer-facing directory in the wedding vertical needed to go from empty CMS to thousands of vetted vendor listings across multiple European cities — without hand-curation or a content team.

We built the full pipeline: scraping and enrichment from public sources, deduplication and quality-scoring in Supabase, AI-generated copy gated by rule-based validation, and a two-way sync into Webflow's CMS with publish state controlled from the database.

~200/wk
Listings published, vs ~20 hand-curated
3k+
Vetted listings, European cities
<2%
Duplicate rate, post-dedup
supabase → webflow · sync engine PUBLISHED 3,184 + 42 today IN REVIEW 168 quality score < 0.7 DEDUPED 412 merged duplicates LISTINGS · 90 DAYS + 1,240 RECENT SYNCS florist · Lisbon vendor_id:8441 PUBLISHED venue · Barcelona vendor_id:8440 PUBLISHED catering · Milan vendor_id:8439 IN REVIEW
Case study · Multi-site service operator № 03 · typical shape

Twelve locations, one ledger.

A recurring pattern: a multi-location service business where each site tracks leads, bookings, and revenue slightly differently. Head office assembles a weekly report by chasing spreadsheets — often a week old and occasionally wrong.

The intervention shape is consistent: a central CRM strict enough that location managers can't go off-script, ingestion from whatever each site already uses, and a reporting layer head office can trust. What changes visibly is the Monday morning. Composite of recurring multi-site engagements.

12
Locations into one live ledger
Mon.
Report ready automatically
4–8wk
Typical delivery window
head office · live ops · all locations Network ops · live 12 locations · 847 bookings this week LDN · CENTRAL 94 +12% LDN · EAST 81 +8% MANCHESTER 67 −3% BIRMINGHAM 72 +5% BRISTOL 58 +4% LEEDS 63 +9% GLASGOW 54 +2% EDINBURGH 48 +6% + 4 more · expand LIVE · syncing from all 12 sites
Case study · Founder-led outreach № 04 · typical shape

A lead engine that does the reading.

A recurring engagement across real estate, professional services, and B2B: the pipeline is the bottleneck, but the founder is the only person who can judge which leads are worth their time. We build a private lead infrastructure — targeted signal sources, Claude-based rating tuned on past wins, and a private mobile feed that surfaces only leads worth acting on.

The founder stays in the loop for every message sent. Volume stays low; quality stays under their control. The bottleneck was never message volume — it was attention. Composite of recurring founder-led engagements.

~50/day
Candidate leads surfaced
5–8/day
Rated worth acting on
100%
Founder-sent first messages
automated lead desk · claude 9/10 r/realestateinvesting · 14m ago "Need to list 40 rentals across 3 portals every Monday — help" HOOK · multi-portal listing sync 📝 DRAFT 🔗 OPEN ⏰ 4D 5/10 LinkedIn · 42m ago "Anyone know good PropTech meetups in London?" SKIPPED 8/10 LinkedIn · 1h ago "Boutique agency, 3 agents, buried in viewings and follow-ups" HOOK · agent ops · CRM automation 📝 DRAFT TODAY SURFACED 47 RATED ≥ 7 6 DRAFTED 4 SENT · FOUNDER 3 QUALITY > VOLUME
§ 05 — How we engage

Three shapes of work.

Every engagement is scoped individually. The shape below describes the kind of work — the specifics come from a conversation.

01

Diagnose

For when you know something's broken but don't yet know where. We map the operating reality, surface the interventions with the best return, and hand back a written brief.

Scoped diagnosticWritten brief
Most common
02

Build

A specific workflow, agent, or pipeline — scoped, built, handed over, documented. The most common way clients work with us. Care retainer optional.

Fixed scopeFull handover
03

Partner

Ongoing arrangement. One or two days a week inside the business — spotting the right work before it's asked for, and executing. For clients we've already delivered for.

OngoingInvitation only
§ 06 — Founder

Hannes Krebs.

An MBA in data science. More than fifty engagements delivered across Europe, the UK, and North America — spanning automation builds, operational strategy, and marketplace growth for clients from five-person studios to mid-market operators. Full-time on automation since 2024.

The interesting part isn't which tool to pick. It's the scoping before anyone opens one, and the architecture that keeps the system running a year later.

Operating from London and Vienna. Native German, working English. Executive MBA, Imperial College London. We take a small number of clients at a time.

Data Science · MBA n8n certified Amazon Vendor · Seller Anthropic API builder Supabase Python
§ 07 — Next step

Thirty minutes. No pitch.

Describe the operating problem you're trying to solve. We'll tell you honestly whether we can help, roughly how the engagement would look, and whether we have capacity this quarter. If we're not the right fit, we'll say so in the call.