AI workflows, shipped in four weeks.
Most AI projects stall in exploration. We take one high-friction workflow (documents, exceptions, approvals) and turn it into a working system your team runs in production. Fixed scope. Fixed timeline. Measured against a metric you choose.
Built by engineers · Gemini, Claude, GPT & open models · Anchored on Google Cloud
Automate the routine 90%, route the ambiguous 10% to a person, log everything for audit.
Systems that never talked to each other, reconciled on a schedule, with the exceptions surfaced instead of buried.
Grading at line speed, on the frames your cameras already capture, with every call traceable back to an image.
Deployed across logistics, manufacturing, food production, robotics, and education.
Start small, ship, then go deeper
Three fixed-scope engagements, in order. You know the deliverable, the timeline, and the success metric before we start. Every system we build keeps a person in the loop, because the decisions worth automating are the ones that matter when they’re wrong.
AI Workflow Workshop
Discovery, run like an engagement rather than a sales call. A structured working session with your operators and IT: we map your highest-friction workflows, score them for automation fit, and leave you with a ranked shortlist and a costed four-week plan, whether or not you build it with us.
- Workflow inventory & friction scoring
- Data and systems readiness check
- A scoped four-week plan with a success metric
The Four-Week AI Workflow
One workflow, running in production, in four weeks. Documents and intake, reconciliation across your ERP and the systems around it, or vision on the line: whichever workflow the workshop ranked first. Fixed scope, fixed timeline, measured against the baseline we set in week one.
- One workflow, scoped and baselined in week one
- Built on your real data, not synthetic samples
- Handed over running, with the metric to prove it
Roadmap Acceleration
For the problems four weeks won’t finish: multi-step agents, decisions with real consequences, the build your team has had parked for two quarters because nobody had the bandwidth. We pull it forward a quarter or two, with the controls that let you put it in front of an auditor, a regulator, or a customer.
- Approval gates before anything ships
- Full audit trail on every decision
- Confidence thresholds you control
Every engagement, stage by stage
Not a slide deck. Not a roadmap. Each engagement has defined stages, a fixed end date, and something working to show for it.
The Four-Week AI Workflow: four weeks, one working system. Running on your data, measured against the metric we agreed on in week one.
Scope & baseline
We pick one workflow, define the success metric, and measure how it performs today. Access to data and systems gets sorted here, not in week three.
Build the core
The pipeline takes shape on your real documents and data, not synthetic samples. Deterministic logic first, models where they earn their place.
Wire it in
Integration with your ERP, TMS, or internal tools. Exception handling and human review gates go live with the people who will actually use them.
Measure & hand off
We run the system against the baseline, document everything, and hand you the keys, with a clear read on what scaling it would take.
Why fixed scope? Open-ended AI consulting rewards billable hours. Fixed stages and a fixed end date reward shipping. If the workflow isn’t a fit, the workshop tells you that up front, before you’ve committed a build budget to it.
Systems in production
Real deployments, real constraints, measured results.
Face redaction that unblocked a robotics rollout under GDPR
A robotics manufacturer had working autonomous units and a European market it couldn’t enter. The machines operated in public spaces, and every frame that caught a bystander’s face counted as personal data. A redaction pipeline now blurs faces in the stream itself, at over two million images an hour, so identifiable footage never reaches storage. Compliance stopped being the thing standing between them and deployment.
Hybrid vision and reasoning that catches what detection models miss
A fleet operator was recording everything and still missing what mattered. Detection could confirm a vehicle was in frame, not that a driver had been following too closely through poor visibility. Pairing a YOLO detection layer with a generative model that reads what the objects are doing, not just what they are, surfaced the patterns the old tooling had no way to describe. The safety team stopped sampling footage and started being pointed at the clips worth watching.
Continuous monitoring that flags hazards the moment they appear
Hazards don’t wait for the inspection schedule, and scheduled walkthroughs meant a problem found on the afternoon round might have been there since morning. A generative model running on the cameras already installed flags anomalies against what normal looks like for that site. Detection moved from a fixed interval to the moment something changed.
Vision grading that keeps pace with a high-speed line
A processing line was running faster than the people grading it: fatigue, drift between shifts, and a ceiling set by how quickly a person can look at a fillet and make a call. A vision system now grades each one in under 90 milliseconds at full line speed, applying the same standard in hour eight as in hour one.
Three technical founders
No account managers between you and the people building your system. The founders scope it, build it, and answer for it.
Ahmad
CEO · Computer vision & ML systems
Beatrice
Technical Advisor · Systems & logistics operations
Arslan
COO · Quantitative engineering & delivery
Before you book a call
How is this different from hiring an AI consulting firm?
Every engagement is fixed-scope with a deliverable defined up front: a ranked automation plan, or a working system measured against a baseline. You are not buying hours.
Which AI models do you use?
Whichever fits the problem. Gemini, Claude, GPT, and open models all have places where they are the right call, and plenty of the pipeline is not a model at all. Where a deterministic rule beats a model, we use the rule. Reliability first.
How long does a project take?
The workshop is a short engagement measured in days. The build is four weeks from kickoff to a measured, working system. Production hardening, scale-out, and deeper roadmap work are scoped separately once the metric is proven.
What do you need from our side?
One person who knows the workflow end to end, access to a representative sample of real data, and someone who can approve system access. That’s usually it; we handle the rest.
What industries do you work in?
We’ve shipped in logistics and transportation, manufacturing, food production, robotics, and education, but the industry matters less than the shape of the problem. If people on your team are moving information between systems by hand, making the same judgement call a hundred times a week, or waiting on someone to check something before it can move forward, the work looks much the same whether you call it freight or finance. If you’re not sure yours fits, the workshop is the cheapest way to find out.
Got a workflow that’s costing you hours?
Bring it to a 30-minute scoping call. We’ll tell you whether it’s automatable, what the four-week build would look like, and what metric it should be judged on.
Book a scoping call