AI strategy development
We turn your business goals into a ranked portfolio of AI use cases with owners, budgets and success metrics. The output is a plan your board can fund and your engineers can build.
AI & Data
AI Consulting
Senior engineers who find the AI use cases worth funding, prove them on your data in weeks, and stay until they run in production.
2-6weeks from kickoff to a validated pilot
100%senior engineers, no juniors on your account
1engineering partner from strategy to production
3engagement models to match your stage
What we cover
One consulting team across strategy, delivery and operations. Every recommendation comes from engineers who will have to make it work.
AI readiness assessment
We audit your data, systems, skills and processes against what your target use cases actually require. You get a scorecard and a gap plan, not a maturity-model poster.
Implementation planning
We scope each initiative down to architecture, team, timeline and cost before anyone writes code. Decisions fixed up front cut the expensive rework later.
Team training
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.
AI governance
We set up risk classification, review gates and documentation that satisfy the EU AI Act without stalling delivery. Governance engineers follow because it fits how they work.
Performance optimization
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.
What you walk away with
Every engagement ends in artifacts your team can act on without us in the room.
AI opportunity map
Your workflows mapped against AI capability, with each opportunity scored for value, feasibility and risk.
Business case per use case
Expected impact, cost to build, cost to run and payback period for every shortlisted use case.
Reference architecture
A concrete system design for your top use cases: models, data flows, integration points and infrastructure.
Governance playbook
Risk classification, review gates, documentation templates and an EU AI Act compliance checklist mapped to your use cases.
Pilot evaluation report
Measured pilot results against the agreed baseline: quality, latency, cost per request and a clear go or no-go recommendation.
Adoption roadmap
A sequenced plan from pilot to production to scale, with owners, milestones and the skills your team needs at each step.
AI & Data
How an engagement runs
01
Discovery
We interview your stakeholders and review your data, systems and workflows. Within two weeks you know which AI opportunities are real and which are noise.
02
Strategy
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.
03
Execution
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.
04
Ongoing support
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.
Three ways to engage
Fixed scope where it can be fixed, flexible where your roadmap needs it. Every model starts with a free 30-minute call.
AI opportunity audit
2 weeks · fixed fee
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.
- Stakeholder interviews and workflow analysis
- Data and systems review against use-case requirements
- Scored shortlist of use cases with ROI estimates
- Recommendation for your first pilot
AI strategy sprint
4-6 weeks
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.
- Deep dive on your top two or three use cases
- Reference architecture and build-vs-buy decision
- Business case with cost, impact and payback
- Board-ready roadmap with owners and milestones
Advisory retainer
Monthly · ongoing
A senior AI engineer on call for your leadership and delivery teams. Architecture reviews, model evaluations and honest second opinions, month by month.
- Monthly architecture and roadmap reviews
- Evaluation of new models and vendors as they ship
- Hiring support and technical interviews
- Direct line to a senior engineer, no ticket queue
Who this is for
CEOs and boards
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.
Product leaders
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.
CTOs derisking build vs buy
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.
Mid-market companies without in-house ML
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.
Methods and stacks we work with
LLM evaluation harnesses
RAG architectures
Agent architectures
OpenAI & Anthropic APIs
Azure OpenAI
AWS Bedrock
LangGraph
Vector databases
Prompt versioning & testing
Data readiness audits
MLOps foundations
EU AI Act compliance
Cost & latency modeling
Build-vs-buy analysis
Go deeper
Each area of the practice, explained in detail.
Questions, answered
How do you find the right use cases?
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.
How long until we see value?
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.
Do we need clean data first?
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.
How is this different from a big consultancy?
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.
Our data is a mess. Is that a blocker?
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.
How do you price this?
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.
Not sure where AI fits your business?
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.
Schedule a consultation