We are not a workshop firm. We come into the building, instrument the process, and put numbers on the table before anyone signs off on a build. Everything after that is engineering against a baseline we agreed on together.
Consulting on flow requires physical presence at least part of the time. The gap between the documented process and the real one only becomes visible when you stand next to the person doing the work.
Consultants embedded in your facility: production floor, warehouse, dispatch office, accounting department, clinic administration. This is where discovery happens and where adoption is won.
Used for engineering sprints, integration work and monitoring once we hold real visibility into the systems. Effective only when access is properly provisioned — so we treat provisioning as a kick-off deliverable with a named owner and a due date.
Most mandates combine both. On-site discovery and stakeholder validation, then remote build with on-site presence at every rollout gate and at go-live.
Timings below reflect a typical single-process engagement. Multi-process programmes run the same cycle in parallel tracks with a shared data foundation.
We spend the first days observing rather than proposing. Our consultants shadow the process across shifts, interview the operators and supervisors, and take read-only extracts of the event data: ERP audit tables, ticket histories, mail server logs, MES records, WMS transactions.
In parallel we reconstruct the real activity graph. Nearly every engagement surfaces process variants that management did not know existed — the manual spreadsheet that bridges two systems, the informal approval by text message, the queue that only one person knows how to clear.
We run the backlog through a single filter: how much throughput does this recover per dollar and per week of effort, and what is the risk if it behaves unexpectedly in production? Items that are technically interesting but economically marginal are removed in front of you, with the arithmetic shown.
Output is a scoped statement of work with acceptance criteria expressed as measurable thresholds — straight-through rate, precision on a holdout set, latency, cost per transaction — rather than as feature lists.
We build the single most constraining link first, in an isolated environment, fed by a historical sample drawn from your own archive. Synthetic demos prove nothing; a prototype that handles your ugliest month of records proves quite a lot.
Evaluation is quantitative and adversarial. We assemble a labelled holdout set with your subject-matter experts, including the edge cases they consider hardest, and we publish the confusion matrix — not a highlight reel.
Production is a different discipline from prototyping. We add durable orchestration, idempotent write-backs, dead-letter handling, structured logging, alerting on confidence drift and dashboards for the process owner.
Rollout is never a switch. The system first runs in shadow mode beside the human process, and we compare outputs until agreement is statistically established. Then volume moves in stages — 10%, 40%, full — with a documented rollback procedure and a named decision-maker at each gate.
We train your process owners and your IT team on the system they now own, hand over the repository and infrastructure definitions, and review the throughput baseline against the discovery numbers at 30, 60 and 90 days.
Some clients keep a light retainer for model evaluation refreshes, drift review and the next constraint in the chain — because once the first bottleneck is relieved, the constraint moves, and the next one becomes worth measuring.
These constrain what we will build. They exist because we have seen each one violated, and we have seen what it costs.
Optimizing a non-bottleneck activity produces zero throughput gain and real maintenance cost. We will decline work that does not sit on the constraint, and explain why.
Any inference that touches money, safety, compliance or a customer commitment has an explicit threshold and a defined human escalation path. A model is never the last line of defence.
Arithmetic, tax rules, tolerances and entitlement checks are code with tests. Models handle perception and language; they do not get to decide whether a total adds up.
Every deployment runs beside the existing process until agreement is demonstrated on live volume. No client learns about a regression from their customer.
Source code, infrastructure-as-code, evaluation datasets, runbooks. Vendor lock-in is a failure mode, not a business model.
If the constraint is resolved by an index, a corrected threshold or a removed approval step, that is the recommendation — even when a more elaborate engagement was on the table.
You end it with a quantified map of your flow and a ranked plan — whether or not you continue with us.
