Built Whole.Owned End to End.
A senior software engineering studio that designs, builds, ships, and operates the products our clients depend on.
AI and cloud engineering from one team
Custom AI agents, cloud migrations, and business software your team can operate after launch.
Built for your industry
Platforms built with domain knowledge and tied to measurable outcomes. The architecture is based on patterns we have run in production.
Explore all solutions
BFSIRisk and compliance automation
Risk platforms that flag anomalies and auto-generate audit reports in real time.
HealthcareUnified clinical data platforms
HIPAA-compliant platforms that unify EHR data and surface patient context at the point of care.
RetailPredictive inventory and personalization
Demand forecasting that reduces overstock and supports conversion with better product recommendations.
LogisticsAutonomous route optimization
Fleet management that uses live telemetry to re-optimize routes through the day.
ManufacturingIndustry 4.0 operations and predictive maintenance
Connected factory platforms that improve uptime and give plant managers a live view of production.
EducationLearning platforms and digital classrooms
EdTech systems that improve access, support engagement, and adapt to each learner.
From kickoff to release
A five-stage engineering process with clear handoffs and predictable timelines.
- Stakeholder workshops
- Technical assessment
- Requirements spec
- System design documents
- Tech stack and trade-offs
- Security and threat model
- Two-week sprints
- Continuous integration
- QA and code reviews
- API connections and tests
- Data migration
- UAT and sign-off
- Load testing
- Security hardening
- Monitoring and SLAs
What we built and what improved
Multi-Agent AI Candidate Screening with Panel Discussion & Continuous Learning
A four-layer screening platform built on Microsoft AutoGen. Specialised sub-agents collaborate, challenge conclusions through structured panel discussion, and adapt to each company's hiring standard.
AI-Driven Global Compliance Onboarding Engine
A working demonstration for a global workforce platform evaluating AI-driven compliance onboarding across countries, document sets, tax classification, e-signature, and audit trails.
Content-Led Advisory Platform for a Financial Services Firm
An AI-augmented publishing platform for a financial advisory firm whose clients are mid- and small-scale manufacturers.
In their words
Svegile vs. traditional agencies
| Feature | Svegile | Traditional agency | Offshore dev shop |
|---|---|---|---|
| AI engineering capability | ✓ Deep specialization | ✗ Generalist teams | ✗ Limited AI-agent capability |
| Custom AI agents for production workflows | ✓ Core offering | ✗ Usually outsourced | ✗ Rarely offered |
| Security-first architecture | ✓ Built in from day one | ⚠ Added after the build | ✗ Minimal coverage |
| Transparent sprint-by-sprint delivery | ✓ Always | ⚠ Inconsistent across teams | ✗ Hard to track |
| Domain expertise across finance, healthcare, and logistics | ✓ 5+ verticals deep | ⚠ Surface knowledge only | ✗ Limited domain depth |
| Prototype sprint option | ✓ 2–4 weeks | ✗ Rarely offered | ⚠ Quality varies by vendor |
| Post-launch SLA support | ✓ Tiered SLAs, 24/7 cover | ⚠ Extra cost | ✗ Best effort, no guarantees |
| Compliance coverage for HIPAA, SOC 2, and PCI | ✓ Built into the architecture | ⚠ Requires separate audit | ✗ Usually out of scope |
Why teams choose Svegile
AI expertise plus engineering discipline for systems that need to run securely in production.
AI and cloud-native engineering
We work across LLMs, MLOps, and cloud-native infrastructure.
Security built into the architecture
Security and compliance controls are planned early, including HIPAA and SOC 2 requirements where they apply.
Predictable delivery
Structured sprints, shared roadmaps, and regular status updates.
Designed to reduce rewrite risk as usage grows
We size systems around expected growth so scaling does not force an early rewrite.
Custom AI agents
We build custom AI agents around your workflows, tools, and approval paths.
How we engage
Fixed scope
4–16 weeks- Defined deliverables
- Milestone billing
- Fits well-scoped projects
Dedicated team
3+ months · 3–10 engineers- Embedded squad
- Open backlog and status
- Sprint-by-sprint delivery
Time and materials
Month-to-month- Scale the team up or down monthly
- Adjust scope as priorities shift
- Track effort against actual hours
Prototype sprint
2–4 weeks- Validate the core hypothesis
- Ship a working prototype
- Decide whether to continue
Latest writing
Agent Observability: Tracing Multi-Step Reasoning in Production
Agent observability is the difference between shipping AI agents and understanding why they fail, loop, or overspend in production.
Alert Fatigue: Designing Alerting Rules That Get Acknowledged, Not Ignored
Most alerting systems fail because they page on internal vibrations instead of real user pain. Better alerting starts with symptoms, SLO burn rates, and ruthless pruning.
Anatomy of a Production AI Agent: Memory, Tools, Guardrails, and Fallbacks
Demo agents chain a model to a tool. Production agents survive failing APIs, compliance constraints, and messy workflows because the right subsystems exist around the model.