Find the work worth automating.
We embed in your business, map how you actually work today, and build the AI pipeline for the part that pays off. Safety and correctness are designed in from the first line, not bolted on before launch. You see it running on your own workflow before you commit to anything.
Starts with a free trial demo on one of your own workflows. Scope and price agreed case by case, after we both know it works.
How we scope it
Prove it first. Price it after.
Free trial demo
We take one real workflow of yours and build a working demo of the pipeline on your own data, so the decision is made on evidence rather than a pitch.
- One workflow, chosen together
- Built and run on your own data
- An honest read on what AI can and cannot do here
- A first estimate of the time and cost at stake
- Yours to keep either way
The full build
Every operation is different, so we do not quote a number before we understand yours. Scope and price are agreed together once the demo has shown what the system is worth.
- Production-grade agentic pipelines
- Wired into your existing tools and data
- Evaluated and verified before handoff
- Risk assessment and AI Act documentation
- On-prem or local models where needed
- Delivered with a KPI and savings report
No fixed price list. What it costs depends on the size of your operation and what the system has to touch, and we only quote once the demo has told us both.
How it works
Four steps from "we think AI could help" to a system running in your business
Most AI projects fail because they automate the wrong thing. We start by understanding the work, not the tools, and we prove it on your data before anyone commits.
Map how you work
We sit with your team and trace the real workflow: the handoffs, the copy-paste, the spreadsheet nobody talks about. The process as it actually runs, not the org chart.
Free trial demo
We pick one workflow with real leverage and build a working demo of it on your own data, at no cost. You see what the pipeline does before there is any money on the table.
Build the pipeline
If the demo holds up, we scope the full system and build it: agents, tools, data plumbing, human checkpoints, guardrails. Production-grade, evaluated to the same standard as our research work.
Hand over with the numbers
You get the system, the documentation, and a report with the KPIs it moves and what it saves you, measured against how the work ran before.
The deliverable
A working system, and the numbers behind it
You do not just get code. You get the pipeline running in your process, plus a report that says in plain numbers what it changed.
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Workflow mapHow your work actually flows today, with the bottlenecks and manual steps marked. This is the baseline we measure against.
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The built pipelineThe agentic system itself, wired into your tools and data, with human checkpoints where they belong.
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KPI and savings reportThe specific KPIs the system moves, measured before and after, alongside what you save in hours and cost, and where the gains actually come from.
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Safety and compliance evidenceThe risk assessment, the evaluation results on your own cases, and the documentation the AI Act asks for.
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Handover and monitoringDocumentation your team can run it from, plus the checks that tell you it is still working next quarter.
Safety first, correctness first
Built to be trusted on day one, not audited into safety on day ninety
Plenty of companies have shipped an AI feature fast and spent the following year explaining it: a chatbot that invented a refund policy, a screening tool that quietly discriminated, an assistant that leaked what it should not have. Those were not surprises. They were design decisions taken too late. We start from the risks, then build.
Risks named before code
Before any code, we write down what the system could get wrong and who it would hurt. That list drives the design: what the model decides alone, and what always goes to a person.
Correctness you can measure
Every pipeline ships with an evaluation suite built on your own cases, not a public benchmark. We know the failure rate before you do, and you get the number rather than a reassurance.
EU AI Act by construction
We classify your use case under the AI Act at the start and build to what it requires: risk management, data governance, logging, human oversight, documentation. Compliance falls out of the build instead of being retrofitted under deadline.
No silent degradation
Models drift and inputs change. The system ships with monitoring and guardrails, so a failure shows up in your logs rather than in a customer complaint or a screenshot on social media.
The same team that evaluates AI to research standards
We are researchers and engineers with peer-reviewed work at ACL, EMNLP, NeurIPS, and ICML. Everything we build, we can also measure, so you never ship something that only works in the demo, and so the savings we report are numbers you can check. See how we evaluate over at Coskew Assurance.
Let's find your first automation.
Tell us what your team spends too much time on. If AI can take it off their plate, we will show you on your own data first, for free.
hello@coskew.com