Solutions
AI-enabled engineering teams built to deliver
Add individual engineers, embedded teams or complete AI-native delivery pods to your organisation. Our engineers combine strong fundamentals with disciplined use of AI across the software lifecycle, so you gain capacity without lowering quality, security or accountability.
Delivery pressure is rising faster than you can hire
Today
- Specialist engineers take months to hire, and the best ones have options.
- Your roadmap keeps growing while budgets stay flat.
- Your team uses AI coding tools, but nobody can say whether delivery has actually improved.
- Adding contractors adds people, not necessarily output.
What you get
- More delivery capacity, from engineers who are productive in your tools and processes.
- AI used deliberately across requirements, coding, testing and documentation, with human review on everything that ships.
- Clear accountability: technical leadership and delivery quality are our responsibility, not just headcount.
- In pods, measured delivery: cycle time, quality and throughput tracked against an agreed baseline.
Choose the model that fits
| Model | What it is | Best when |
|---|---|---|
| Individual engineers | What it isAI-enabled specialists who join your team and work in your processes | Best whenYou have strong engineering leadership and need specific skills |
| Embedded teams | What it isA small SYGNISYS team, with a lead, working inside your organisation | Best whenYou need a capability, not just a person |
| AI-native delivery pods | What it isA cross-functional pod that owns delivery of a product area using SYGNISYS Flow | Best whenYou want outcomes and measured throughput, not hours |
Pricing is agreed during scoping.
A delivery pod typically includes
- Technical lead
- Software engineers
- Quality engineering
- Product or business analysis
- DevOps
- AI engineering
- Architecture oversight
- SYGNISYS delivery agents
Roles available
Engineering
- Software and full-stack engineers
- .NET, Java, Python, Node.js
- React and Angular
- iOS, Android and cross-platform mobile
Quality
- QA and quality engineers
- Test automation
- AI evaluation
Platform
- DevOps
- Cloud
- Site reliability
AI and data
- ML and AI engineers
- Data engineers
Product and design
- Product managers
- Business analysts
- UI/UX designers
Leadership
- Solution architects
- Engineering leads
SYGNISYS AI-Enabled Engineer Standard
AI capability builds on strong engineering fundamentals. It never replaces them.
Every engineer we place is trained and assessed against our standard for responsible, effective use of AI in software delivery. It covers:
- AI-assisted requirements and design
- Context engineering
- AI-assisted coding and testing
- Verifying generated code
- AI security
- IP protection
- Human review
Engineers work within your own AI usage policy, including where certain tools are not permitted.
From first call to productive team
Step 1: Understand
We agree the skills, outcomes, working model and tools.
Step 2: Match
We propose engineers or a team shape, with profiles.
Step 3: Meet
You interview and choose.
Step 4: Onboard
Engineers join your tools, rituals and security processes.
Step 5: Review
We review fit and delivery with you after the first month, then regularly.
Quality, security and accountability built in
- Senior review on AI-assisted work before it merges.
- Engineering leads accountable for delivery quality.
- Client data and code handled under documented security controls.
- Clear IP terms: what we build for you is yours.
Working your hours
Our engineering teams are based in Colombo, Sri Lanka. That gives you strong engineering depth and three to five hours of shared working time with both Australia and the UK each day. Where a client needs more overlap, we shift team schedules to match.
- Australia
- Shared working time across your afternoon.
- United Kingdom
- Shared working time across your morning.
- Sri Lanka
- Our home market and delivery base.
Frequently asked questions
What is an AI-native delivery pod?
A cross-functional team that owns delivery of a product area using SYGNISYS Flow. A pod typically combines a technical lead, software engineers, quality engineering, product or business analysis, DevOps, AI engineering and architecture oversight.
How is this different from staff augmentation?
You can add individual engineers, but you can also give us a capability or a whole product area. Technical leadership and delivery quality are our responsibility, not just headcount, and in pods we track cycle time, quality and throughput against an agreed baseline.
What makes an engineer "AI-enabled"?
Every engineer we place is trained and assessed against the SYGNISYS AI-Enabled Engineer Standard: AI-assisted requirements, design, coding and testing, verifying generated code, AI security, IP protection and human review.
Can your engineers follow our own AI policy?
Yes. Engineers work within your AI usage policy, including where certain tools are not permitted.
Who owns the code you write?
You do. What we build for you is yours under clear IP terms, and client data and code are handled under documented security controls.