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— AI Implementation · Pillar one —

AI that works on Tuesday morning.

Operational AI built for override, not against it. We frame the pain, pick the right place for the LLM, and ship in weeks not quarters.

Book an AI audit or — read our manifesto
— 01 / Selected AI work —

Four cases. Real numbers. Real architecture.

US WHOLESALER · 2025 · ANONYMIZED

Vendor onboarding, weeks to hours.

GPT-4o pipeline mapping inconsistent supplier files into a 120-column Acumatica master. Humans approve only the edge cases.
GPT-4o · Python 3.11 · Pandas · Pydantic · n8n · PostgreSQL · Acumatica
Read the vendor-AI case
60–75%effort reduction
per vendor onboarded
Weeks → hours cycle
120-column Acumatica master
80% automation target
284h scoped engagement
RETAIL SHELF · NATIONAL CHAIN · 2025 · ANONYMIZED

Per-store, per-shelf recommendations in seven days.

A national retail chain owner said: "Tell me what to put on each shelf, in each store, in the country." We translated that into a concrete optimisation surface — sell-through, freshness, category interaction — wrapped in an approvable dashboard so store managers stay in charge and override behaviour becomes data.
GPT-class LLM · Python · KPI ingestion · approvable dashboard
Read the retail-shelf case
7 daysvague brief →
working PoC
Per-shelf, per-store
Human-in-loop design
Override-as-data feedback
Generalises to any SKU
NAVON · MULTI-MODAL COMPLIANCE · 2025—NOW

60+ policy categories. Text, images, async video. One LLM call.

A regulated US user community needed real-time compliance scoring across text, images, and async video. We chose not to train a separate classifier. Instead: an LLM-as-policy-engine pattern — master prompt plus DB-stored per-user preferences. Single call returns answer plus structured compliance JSON across 60+ categories.
GPT-class LLM · Node.js · PostgreSQL · REST + Swagger · WebSocket
Read the Navon case
60+policy categories
scored per call
Two delivery teams
One interface contract
Parallel sprint cadence
Lead architect
SKARB.AI · KCG PRODUCT · 2025—NOW

Production GraphRAG family archive. Shipped solo in ten weeks.

Built for ourselves so we know what shipping production AI actually takes. GraphRAG-style hybrid retrieval, voyage-3-large 1024d multilingual embeddings, pgvector + HNSW, smart routing, encrypted memory, eval harness that killed BM25+RRF and parked Cohere reranker.
GPT-4.1 / 4.1-mini · voyage-3-large · pgvector · Supabase · React · Capacitor
Read the Skarb.ai case
10 weeksstart to shipped
web + iOS + Stripe
53 screens · 23 tables
15 edge functions
2,714 tests
EN / RU / PL trilingual
— 02 / How we approach it —

Four principles. Earned the hard way.

01

Frame the pain

Translating a vague brief into a concrete optimisation surface is the hardest part of the job. We do it before we touch a model.
Discovery is its own deliverable
02

Pick the right place

Choose where the LLM earns its place — and just as importantly, where to keep it out. Most AI failures start with the wrong location.
Hybrid > pure LLM, every time
03

Build for override

Humans stay in charge. That's not a limitation, it's the design. The override is data the system learns from.
The only design that survives reality
04

Ship in days

Working PoC in one week. Production engagement in weeks. Quarters are for theatre. We learn from contact with reality, fast.
Velocity is a quality signal
— 03 / How we think about it —

Essays from the operators' floor. Written by Max, not a content team.