How we work

Most AI projects do not fail on the math. They stall between the person who scoped them and the person who built them, or between the demo and the systems the business actually runs on. Our engagement structure exists to close both gaps: the same senior engineer who characterizes your problem builds the solution and ships it into your systems, and every stage ends with a real decision point where you can stop.

Characterization is built into every engagement. It is not a separate product we sell you before the “real” work begins. It is how the real work begins.

Stage A: Characterization#

We map the business problem, the workflows around it, and the data that actually exists, not the data everyone assumes exists. Together we define the success metrics (KPIs) that will judge the project, in business terms: forecast error against your current method, hours saved, purchasing error reduced. The metric is agreed before anything is built, so nobody gets to move the goalposts later, including us.

Deliverable: a characterization document with the problem definition, the data assessment, the proposed approach, and the agreed success metrics.

Honest exit point. Sometimes characterization concludes that AI is not the right tool for this problem, or that your data is not ready yet. We will say so, in writing, with what to fix first. You keep the document and part as friends. Data readiness is the most common real blocker in the market; Israeli tech press reporting (Geektime) puts fragmented, unready data behind the large majority of failed AI initiatives, and pretending otherwise helps nobody.

Stage B: Feasibility proof and real-data validation#

We build a working version and validate it on your real data, against the success metrics defined in Stage A. Not a slide deck, not a demo on sample data: your history, your edge cases, your messy reality, measured against the numbers we agreed on.

Deliverable: results on real data, compared side by side with the agreed metric and with how you do it today.

Honest exit point. If the numbers do not clear the bar we set together, you stop here. You keep the findings, the code produced to date, and a clear written account of why, which is worth real money the next time someone pitches you AI.

Stage C: Implementation and handover#

We integrate the working solution into the systems you already run, whether that is Priority, SAP, monday.com, or your data warehouse, and we hand it over properly. You own what we build: the models, the code, the pipelines. There is no platform meter and no lock-in.

Deliverable: a solution running inside your environment, documentation, and a handover your team can actually operate.

Accountability after delivery: the graded ledger#

A forecast nobody grades is an opinion. Wherever a solution makes predictions, we track them openly against actuals on an ongoing ledger, month after month. This is not a policy we adopted for marketing: on our longest engagement, a three-year forecasting service for a US-market industrial manufacturer, every monthly forecast was graded against actuals with 3-month and 12-month error tracking, and that ledger sat on the table in leadership planning meetings. You should demand the same standard from anyone you hire, including us.

Engagement models#

• Hourly, on demand. Advisory, reviews, second opinions, specific technical questions. No minimum commitment.
• Project. The three-stage arc above, with a fixed scope and defined success metrics per stage.
• Monthly support. Ongoing operation of delivered systems: monitoring, retraining, the graded ledger, and incremental improvements.

What we are not#

We are not a staff-augmentation shop, not a transformation program, and not a chatbot vendor. If you need a hundred developers or an organization-wide program, an enterprise integrator is the right call, and we will tell you so in Stage A.