Six practice areas. One accountable partner.
Whether you need an AI product built, an engineering bench extended, or global payroll handled, every practice area below connects to the next — so nothing falls into a gap between vendors.
AI Engineering & LLM Solutions
We design and ship AI features that hold up in production — not just a promising demo. That means retrieval architecture, orchestration, evaluation, and monitoring from day one.
- RAG and knowledge-grounded applications
- Agentic workflows and copilots
- Model evaluation, guardrails, and prompt engineering
- MLOps: training pipelines, monitoring, and cost management
Where teams usually start: an internal copilot, a customer-facing assistant, or a search/RAG layer over existing data — scoped to a two to four week first milestone so value is visible early.
Product Engineering
Dedicated engineers, designers, and QA turn a roadmap into software your users can rely on — with a team that adapts to how you already work.
- Web and mobile application development
- MVP to scale-up architecture
- API design and third-party integrations
- QA automation and release engineering
Engagement fit: extend your existing squad with senior engineers, or hand us a scope and get a delivery pod that plans, builds, and ships on a visible sprint cadence.
Cloud, DevOps & Platform Engineering
We keep infrastructure boring — in a good way. Reproducible environments, automated pipelines, and observability that catches problems before your customers do.
- Infrastructure as code (Terraform, Pulumi)
- CI/CD pipeline design and automation
- Observability, alerting, and incident response
- Cloud cost optimization across AWS, GCP, and Azure
Where teams usually start: a platform audit that maps current spend, deployment risk, and monitoring gaps — with a prioritized plan you can act on immediately.
Data & Analytics
Clean pipelines and dashboards that people actually trust — built on the data infrastructure that will still make sense a year from now.
- Data pipeline and warehouse design
- BI dashboards and reporting layers
- Data quality, governance, and access control
- Analytics foundations for AI and ML workloads
Engagement fit: often paired with AI engineering, since reliable data pipelines are the foundation most AI features need before they can ship.
Technology Talent Solutions
Every consultant on our bench has shipped software, so technical screens hold up. Source, screen, and hire engineers who can deliver from day one.
- Targeted sourcing and technical screening
- Contract, contract-to-hire, and permanent placement
- Role scorecards and structured interview loops
- Offer strategy and negotiation support
Engagement fit: from a single contract engineer to a fully staffed pod — pipeline metrics like time-to-shortlist and offer-accept rate stay visible throughout.
Managed Payroll & Compliance
Keep every pay run, statutory filing, and benefit on track across countries — without standing up a separate PEO or vendor to manage.
- Global contractor and employee payroll
- Statutory compliance and tax filings
- Benefits administration
- Onboarding, contracts, and equipment provisioning
Engagement fit: runs underneath any talent or engineering engagement — or stands alone if you've already hired the team and just need it managed.
Services, answered
Can I combine multiple services into one engagement?+
Yes. Most clients combine at least two practice areas — for example, AI engineering with technology talent, or product engineering with managed payroll — under a single agreement.
What does a typical AI engineering engagement include?+
Use-case scoping, architecture for retrieval and orchestration, model or provider selection, evaluation harnesses, and production hardening with monitoring.
Can you staff a single contract engineer, not a full team?+
Yes. Our talent solutions practice supports everything from a single contract hire to a fully staffed delivery pod.