Services
Applied AI Engineering
The pilots were promising. Production is another story: brittle integrations, unpredictable costs, no evaluation discipline, and a roadmap full of AI ambitions your systems can't support yet.
We're not an AI research firm — we're the engineers who make AI work in production.
Schedule a ConsultationWhat Changes For You
Business outcomes
What we deliver
- Use-case selection and feasibility assessment
- AI integration engineering — LLMs, computer vision, and ML models in real workflows
- Production AI pipelines and MLOps/LLMOps
- Evaluation, monitoring, and cost discipline
- Data foundations and governance to support AI workloads
- Team enablement and handoff
Typical use cases
- Moving a successful AI pilot into production
- Integrating LLMs into existing products and workflows
- Computer vision for real-time analysis
- Standing up AI pipelines and evaluation practices
- Rescuing a stalled or underperforming AI initiative
Approach & engagement model
A named senior leader owns your engagement end-to-end — onshore leadership and architecture, with nearshore engineering capacity where scale helps — with transparent reporting and a structured transition to your team.
No two organizations start from the same place, so we don't sell a template. The phases, pace, and team composition are scoped to your situation — from a short, focused assessment to a multi-quarter program.
Where are you today?
Wherever you are, we meet you there
Curious
AI-aware, not sure where to start.
- AI + LLM strategy
- AI readiness and training
- Pilot design and build
Experimenting
Pilots running, not yet scaling.
- Pilot evaluation
- Use-case prioritization
- AI governance and trust design
Scaling
Spreading what works.
- Enterprise rollout strategy
- Change and adoption programs
- AI operating model design
Operating
AI is running. Keeping it working.
- Managed agent operations
- Continuous tuning and governance
- AI strategy refresh
Frequently asked questions
How is this different from hiring an AI research team?
It isn't research — and that's the point. Our staff have built pipelines for AI workloads in production, our technical leadership includes Ph.D.-level AI depth, and we're advised by leading AI researchers. But the service is engineering: making models work reliably inside your systems, budgets, and compliance requirements.
Our data is a mess. Do we need to fix that first?
No — and 'clean everything first' is usually the wrong plan. We scope the data work to the use case: the foundations, pipelines, and governance the specific AI workload actually needs, built as part of the engagement rather than a year-long prerequisite.
Which models and platforms do you work with?
We are vendor-neutral across commercial LLM APIs, open-weight models, and custom-trained models including computer vision. The choice falls out of your use case, data sensitivity, latency, and cost constraints — not a partnership agreement.
How do you keep AI costs and risks under control?
Evaluation and cost discipline are built into the delivery: measurable quality gates before launch, monitoring after it, spend visibility per workload, and governance appropriate to your industry — including regulated environments like healthcare and financial services.
Let's take on your toughest challenge.
Tell us what you're up against in applied ai engineering — you'll hear back directly from our leadership team.