Most ML initiatives stall on the same two things: the data underneath is not trustworthy, and nobody owns the thing after launch. We assess readiness first, fix the foundation, then build something small enough to actually ship and govern.
In plain terms
Where teams get stuck
We ran an AI pilot, it worked in the notebook, and it never reached production.
We have no way to tell whether our model or agent is actually performing.
Our data is not clean or complete enough to train or ground anything.
The AI bill is growing and nobody can attribute it to a business outcome.
We shipped something and now nobody owns the evaluation or the monitoring.
What we deliver
A review of your data readiness, use cases, and existing tooling, with an honest read on which opportunities are real and which need foundation work first.
The feature pipelines, quality checks, and historical depth that any model needs before it can be trusted.
Build and evaluation of the applied models, LLM features, and agents that fit the use case, with evaluation harnesses rather than vibes.
Serving, monitoring, cost control, and the ownership model that keeps the system working after the project ends.
How deep it goes
The same delivery primitives (context, controls, and review) show up across every engagement.
Working out what is actually buildable before committing budget.
The unglamorous work that decides whether anything downstream works.
Building the narrow thing that ships.
Getting it into production and keeping it honest there.
Leaving your team able to run and govern it.
The stack we build with
Frameworks, platforms, and infrastructure we implement, integrate, and operate end to end.
What changes
Proof in production
We audited more than 400 instruments on a veterinary compliance platform before building anything on top, and produced a roadmap grounded in what the data could actually support.
Read the instrument audit case study →Applied AI was used to structure unstructured rate sheets on top of a consolidated, governed shipment data model, surfacing more than $300K in annual savings.
Read the applied AI case study →Common questions
Engagements start with a scoped assessment of data readiness and use-case feasibility. That assessment is deliberately cheap relative to a build, because the most common cause of wasted AI budget is starting the build before the data can support it.
Yes, and that is usually the point. Data readiness is the first phase rather than a precondition. We tell you what has to be true before a model can work, and we build the pipelines and checks that get you there.
We build, deploy, and hand over. Engagements that only produce a recommendation tend to leave the same gap they found, which is why deployment and evaluation are part of the scope rather than an optional extra.
Every build includes an evaluation harness before launch, so quality is measured against a defined set rather than judged by impressions. We also wire monitoring and drift alerting so the measurement continues after handover.
Yes, and we prefer it. We usually supply the data foundation, deployment, evaluation, and operations layers, and work alongside your team on the modelling where they hold the domain knowledge.
They overlap. AI consulting covers the strategy, vendor, and adoption questions. This engagement is the applied build path: data readiness, model or agent development, deployment, and operations.
How we work
We assess data readiness and use-case feasibility, and tell you which opportunities are real now and which need foundation work first.
We ship the smallest useful version with an evaluation harness, so performance is measured from day one instead of argued about.
We put monitoring, cost visibility, and ownership in place, then hand your engineers the process to retrain and extend it.
Our Trusted Partners
In one working session we'll name what's broken, what's possible, and the first system worth building.
