Hiring cycles rarely keep pace with delivery commitments. Whether the trigger is a new client win, an attrition gap, or a deadline pulled forward by a quarter — we deploy full-stack, DevOps, and AI/ML engineers into your team in as little as seven working days, under an SLA, with the same engineering standards as our project-delivery work.
Full-time engineers assigned to your team, working under your delivery model, reporting structure, and standards. No layered account managers — you talk directly to the engineer, the engineer talks directly to your tech lead.
Pre-formed teams — typically full-stack plus QA plus DevOps — assembled around a specific deliverable. Shared accountability for collective output. Clear scope, defined exit point, single point of escalation.
Senior engineers, architects, and AI/ML practitioners for engagements where depth of expertise matters more than headcount. Useful when an internal team is stuck on a hard problem or needs to bring in a credibility-establishing perspective.
Onboarding designed to produce contribution in week one, not month two. Structured shadowing, tooling alignment, named backups, and replacement protocols that protect delivery through any transition.
Full management of payroll, statutory compliance, and HR operations for every deployed engineer. Your team retains delivery focus without inheriting the administrative overhead of a distributed workforce.
React · Node.js · Python · .NET · Java · modern web frameworks
AWS · Azure · GCP · Kubernetes · Terraform · CI/CD · observability
Applied AI · GenAI · MLOps · model fine-tuning · production-grade AI engineering
Manual & automation testing · performance · QA strategy for high-availability environments
Pipelines · warehouse modernisation · analytics infrastructure on cloud-native stacks
iOS · Android · React Native · Flutter for product and enterprise mobility
Monthly billing on agreed rate cards, structured for sustained capacity over 3–6 month rolling engagements with standard notice terms.
A defined team — typically 3–8 engineers — billed as a unit, with shared accountability for collective output and milestones.
For specific delivery commitments, structured against measurable outcomes with linked SLAs on deployment time and resource continuity.
Short-cycle pilots designed to validate fit before committing to longer-term capacity arrangements.
Structured discovery on the role, scope, technical environment, team structure, and timelines. Output: a calibrated profile defining what success looks like.
Shortlists drawn from the hybrid bench, aligned to both the technical brief and your working culture. Typically presented within five working days.
Your team interviews shortlisted candidates through your preferred process. We support technical evaluations, reference checks, and trial work where useful.
Structured onboarding — environment setup, context briefing, shadowing. Goal: meaningful contribution in the first working week.
Ongoing performance reviews, quarterly check-ins, and proactive resource development so the relationship continues to deliver value across the engagement lifecycle.
Standard deployment is seven working days from finalised brief to first day of contribution, supported by an SLA where required. Specialist roles — particularly senior AI/ML engineers — may take ten to fourteen days depending on the depth of the brief and the assessment cycle.
Marketplaces give breadth but variable quality and no continuity. Pure in-house staffing gives consistency but no flex. Our hybrid bench combines in-house engineers managed under our HR and operational systems with a curated remote network we have built and validated over time. You get the reliability of permanent capacity plus the flexibility of distributed delivery, without marketplace variance.
Time and material, dedicated capacity pods, outcome-linked engagements, and short-cycle trial deployments. Each is structured around your procurement preferences and delivery model, with standard 30-day notice terms across all formats.
Every engineer goes through a multi-stage technical assessment plus project-based evaluation before joining the bench. Once deployed, they operate under our engineering standards, code review practices, and quarterly performance reviews — the same governance framework that underpins our project-delivery work.