From first architecture decision to production deployment and beyond.
We partner with ambitious companies to build AI-powered products from zero to production. No handoff — we stay from architecture through deployment and beyond. Every engagement starts with an honest assessment: do you actually need what you think you need?
Custom AI Product Development
From problem to production, end to end.
You have a problem. We build the solution — end to end. Model selection, training, API design, production deployment, and the monitoring that keeps it reliable. We’ve done this across global-scale systems. Now we do it for ambitious companies like yours.
- Model selection & training pipelines
- REST / gRPC API design & serving
- Production deployment (AWS, GCP, Azure)
- Real-time & batch inference systems
- Monitoring, alerting & drift detection
- Documentation & team handoff
AI Architecture & Strategy
Honest assessments. Architectures that scale.
Not sure if you need a fine-tuned LLM, a simple classifier, or just better heuristics? We’ll tell you honestly — even if the honest answer means less work for us. We design architectures that scale with your business, not architectures that impress in a pitch deck.
- Technical feasibility assessments
- Build vs. buy analysis
- Model architecture selection
- Data strategy & pipeline design
- Cost & latency optimization
- Scalability & growth planning
ML Infrastructure & Operations
The unsexy work that makes AI actually work.
Data pipelines, model serving, A/B testing, CI/CD for ML, cost optimization. If your model is great but your infrastructure is held together with duct tape, this is where we come in. We build the foundation that makes everything else reliable.
- Data pipeline engineering (Spark, Airflow)
- Model serving & orchestration
- A/B testing & experimentation frameworks
- CI/CD for ML (MLflow, SageMaker, Vertex)
- Cloud cost optimization
- Observability & logging infrastructure
How we work
Discovery & Assessment
We understand your problem, your data, and your business constraints before writing a single line of code. Half of good ML engineering is knowing what not to build. We’ll give you an honest assessment — even if it means recommending against ML.
Architecture & Design
We design the right approach for your scale, budget, and timeline. Not every problem needs deep learning. Not every startup needs real-time inference. We choose what works, not what’s fashionable.
Build & Validate
Rapid prototyping, validation with real data, then production hardening. Short cycles, constant feedback. You see working code every week — not quarterly demos.
Deploy, Monitor & Evolve
Production deployment with monitoring, alerting, and documentation your team can maintain. We don’t build systems that only we can understand. And we stay involved as your needs evolve.
Choose the model that fits.
Project-Based
A defined scope with clear deliverables, timeline, and budget. We take your project from specification to production deployment.
Best for: Companies with a specific AI product or feature to build.
Typically 2–6 months
Embedded Partnership
We embed with your engineering team as a long-term AI partner. We lead the ML engineering while your team handles the product around it.
Best for: Startups building AI-first products that need ongoing ML leadership.
3–12+ month engagements
Architecture Sprint
A focused 2-week engagement to audit your current AI approach, identify gaps, and deliver a detailed technical roadmap with implementation recommendations.
Best for: Teams that need expert guidance before committing to a full build.
2 weeks, fixed scope
Have a project in mind?
Every engagement starts with a conversation — no pressure, no commitment. We'll give you an honest assessment of your project, and you can decide from there. The best partnerships start with clarity.