AI Consulting & Process Optimization

Discovery means sitting with the people who do the work. The documented process and the real one differ in nearly every organization, and the gap is where the opportunity usually hides — the spreadsheet that reconciles two systems, the inbox one person triages by hand, the report rebuilt from scratch every Monday.

We quantify what we find: how many times a month, how long each takes, what the error rate costs when it goes wrong. That turns a debate about AI strategy into arithmetic, and arithmetic is what survives contact with a CFO.

Each candidate use case gets scored on three axes: value if it works, feasibility with the technology as it exists today, and whether the data needed is actually available and clean enough. The list that comes out is usually not the one the organization expected — the flashy customer-facing idea often ranks below a dull internal process running ten thousand times a month.

We are equally direct about the cases we recommend against. If a rules engine solves it deterministically, if the volume is too low to repay the build, or if the data does not exist yet, that goes in the report with the reasoning. A consulting engagement that only ever says yes is not worth commissioning.

Three areas tend to pay back faster than the rest. Each one combines high volume with expensive manual reading.

Contract and compliance review. Long agreements and regulatory frameworks — GDPR, HIPAA, DORA — take up senior legal hours. A retrieval system finds the clauses that matter and cites the page and paragraph, so a lawyer verifies in minutes instead of reading for hours.

Internal support. Policy questions arrive constantly, and the answer is usually split between a policy document and live data in another system. Retrieval handles the policy half, a lookup in your HR or ERP system handles the rest, and the employee gets one answer instead of a ticket.

Invoice and document processing. Invoices and statements arrive unstructured and get retyped by hand. Extracting the fields against a fixed schema, checking them against your vendor records, and posting to the ERP removes the queue instead of assisting it. It is also the easiest of the three to measure, because you already know what that queue costs.

Most stalled AI programs stall on data, not models: records scattered across systems that disagree, documents that were scanned rather than structured, history that was overwritten instead of kept. We assess what you have against what each use case needs, and where remediation is required we scope it as a piece of work with its own cost rather than an assumption buried in someone's estimate.

For European operations we also position each use case against the EU AI Act — which risk tier it falls into, what documentation and human-oversight obligations follow, and what that means for the design. It is far cheaper to answer that before the build than to retrofit it after an internal audit asks.

The roadmap is written by senior engineers who deliver production systems, which is what keeps the estimates honest — we are the ones who would have to hit them. Our CEO runs discovery personally, and for a client we worked with over several projects the AI modules that came out of this process cut development effort by 40%.

You are not obliged to have us build it. The deliverable is yours and it is specific enough for your own team or another partner to execute. But when clients do continue, no handover is needed: the people who mapped the process are the people who write the code.