Senior engineers who map how your work runs today, find the AI use cases worth funding, prove them on your data in weeks, and stay until they run in production.
One consulting team across strategy, delivery and operations. Every recommendation comes from engineers who will have to make it work.
We map how the work actually runs today, step by step and system by system, then turn that into a ranked portfolio of AI use cases with owners, budgets and success metrics. The map is usually the part clients did not have.
We audit your data, systems, skills and processes against what your target use cases actually require, including where the data came from, who owns it and who may see it. You get a scorecard and a gap plan, not a maturity-model poster.
We scope each initiative down to architecture, team, timeline and cost before anyone writes code. Decisions fixed up front cut the expensive rework later.
We train your engineers, product managers and leadership on the parts of AI they will actually touch. Hands-on sessions on your stack and your data, not generic slideware.
Data ownership, retention, access and lineage, plus risk classification and review gates that satisfy the GDPR and the EU AI Act without stalling delivery. Governance engineers follow, because it fits how they already work.
We measure existing AI systems on accuracy, latency and cost, then fix the weakest link first. Typical work: better retrieval, tighter prompts, cheaper model routing.
Every engagement ends in artifacts your team can act on without us in the room.
Your workflows mapped against AI capability, with each opportunity scored for value, feasibility and risk.
Expected impact, cost to build, cost to run and payback period for every shortlisted use case.
A concrete system design for your top use cases: models, data flows, integration points and infrastructure.
Data ownership and retention rules, risk classification, review gates, documentation templates, and a GDPR and EU AI Act checklist mapped to your use cases.
Measured pilot results against the agreed baseline: quality, latency, cost per request and a clear go or no-go recommendation.
A sequenced plan from pilot to production to scale, with owners, milestones and the skills your team needs at each step.
We interview your stakeholders and map your data, systems and workflows as they actually run. Within two weeks you know which AI opportunities are real and which are noise.
We rank use cases by value, feasibility and risk, then write the business case for the top ones. You get a roadmap with numbers attached, not a vision statement.
We prove the top use case as a working pilot on your data, measured against an agreed baseline. The same engineers then take it to production.
We stay on as an advisory partner: architecture reviews, evaluations of new models, and a roadmap that stays honest. You scale our involvement up or down as needed.
Fixed scope where it can be fixed, flexible where your roadmap needs it. Every model starts with a free 30-minute call.
A fixed-scope audit of your workflows, data and systems that ends in a ranked shortlist of AI use cases. The right start when you know AI matters but not where.
Strategy, architecture and business case for your top use cases, built with your team. Ends with a roadmap you can fund and a pilot ready to start.
A senior AI engineer on call for your leadership and delivery teams. Architecture reviews, model evaluations and honest second opinions, month by month.
You need a defensible AI position for investors, customers and your own planning. We give you a strategy grounded in what your data and systems can actually support.
You have a backlog of AI ideas and pressure to ship something. We rank them by value and feasibility and prove the best one on real data before you commit a roadmap.
Vendors promise everything and the demos all look the same. We benchmark the options on your data and give you an evidence-based decision you can defend.
You do not need a data science department to use AI well. We act as your AI team until it makes sense to hire one, then help you build it.
Each area of the practice, explained in detail.
We audit your workflows and data, then rank opportunities by value and feasibility. You get a shortlist of PoCs with clear ROI, not a wish list. High-impact, low-risk bets first.
A prioritized roadmap in the first two weeks, a first working PoC in four to six. We optimize for shipped outcomes, so value lands early, not after a year of decks.
No. We start with what you have and fix data only where a use case needs it. Perfect data is a myth. We scope the minimum required to ship.
Engineers run the engagement, not analysts. We build and deploy, not just advise. Smaller team, senior people, production code you own. Less slideware, more shipped.
It rarely is. Most useful AI work starts on imperfect data: documents, tickets, spreadsheets, half-maintained databases. The audit shows which gaps actually block your top use case and which you can ignore. We fix the former, skip the latter, and put data quality on the roadmap only where it earns its cost.
The audit and the strategy sprint are fixed-fee with a defined scope and deliverables, so you know the number before we start. The advisory retainer is a flat monthly rate you can pause or stop at any time. If the first pilot needs engineering beyond the sprint, we quote it separately before any work begins.
Book a free 30-minute call with a senior engineer. You leave with two or three use cases worth testing, whether or not we work together.
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